diff --git a/advanced_standardized_data.csv b/advanced_standardized_data.csv new file mode 100644 index 00000000..715649d0 --- /dev/null +++ b/advanced_standardized_data.csv @@ -0,0 +1,24 @@ +UT;DI;TI;SO;PY;DT;LA;TC;AU;C1;CR;DE;ID;AB +https://openalex.org/W2101234009;https://doi.org/10.48550/arxiv.1201.0490;Scikit-learn: Machine Learning in Python;arXiv (Cornell University);2012;preprint;en;63665;['Fabián Pedregosa', 'Gaël Varoquaux', 'Alexandre Gramfort', 'Vincent Michel', 'Bertrand Thirion', 'Olivier Grisel', 'Mathieu Blondel', 'Müller, Andreas', 'Nothman, Joel', 'Louppe, Gilles', 'Peter Prettenhofer', 'Ron J. Weiss', 'Vincent Dubourg', 'Jake Vanderplas', 'Alexandre Passos', 'David Cournapeau', 'Matthieu Brucher', 'Matthieu Perrot', 'Édouard Duchesnay'];"['Bauhaus-Universität Weimar', 'Amherst College', 'University of Massachusetts Amherst', 'Kobe University', ""Commissariat à l'Énergie Atomique et aux Énergies Alternatives"", 'Google (Canada)', 'Enthought (United States)', 'Total (France)', 'University of Washington', 'CEA Paris-Saclay', 'Nuxe (France)']";['https://openalex.org/W1496508106', 'https://openalex.org/W1571024744', 'https://openalex.org/W2024933578', 'https://openalex.org/W2035776949', 'https://openalex.org/W2040387238', 'https://openalex.org/W2047804403', 'https://openalex.org/W2063978378', 'https://openalex.org/W2097360283', 'https://openalex.org/W2097850441', 'https://openalex.org/W2118585731', 'https://openalex.org/W2146292423', 'https://openalex.org/W2152799677', 'https://openalex.org/W2153635508'];['Python (programming language)', 'Documentation', 'Computer science', 'MIT License', 'Artificial intelligence', 'Machine learning', 'Programming language', 'License', 'Software engineering', 'Operating system'];['Python (programming language)', 'Documentation', 'Computer science', 'MIT License', 'Artificial intelligence', 'Machine learning', 'Programming language', 'License', 'Software engineering', 'Operating system'];Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.org. +https://openalex.org/W3023540311;https://doi.org/10.5860/choice.27-0936;Genetic algorithms in search, optimization, and machine learning;Choice Reviews Online;1989;article;en;49332;[];[];[];['Computer science', 'Artificial intelligence', 'Machine learning', 'Quality control and genetic algorithms', 'Algorithm', 'Genetic algorithm', 'Meta-optimization'];['Computer science', 'Artificial intelligence', 'Machine learning', 'Quality control and genetic algorithms', 'Algorithm', 'Genetic algorithm', 'Meta-optimization'];"From the Publisher: +This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. + +Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required." +https://openalex.org/W2125055259;;C4.5: Programs for Machine Learning;;1992;book;en;23696;['J. R. Quinlan'];['University of Sydney'];[];['Computer science', 'Unix', 'Classifier (UML)', 'Machine learning', 'Artificial intelligence', 'Source code', 'Workstation', 'Software', 'Decision tree', 'Sample (material)', 'Software engineering', 'Data mining', 'Programming language', 'Operating system'];['Computer science', 'Unix', 'Classifier (UML)', 'Machine learning', 'Artificial intelligence', 'Source code', 'Workstation', 'Software', 'Decision tree', 'Sample (material)', 'Software engineering', 'Data mining', 'Programming language', 'Operating system', 'Chromatography', 'Chemistry'];Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use , the source code (about 8,800 lines), and implementation notes. The source code and sample datasets are also available for download (see below). C4.5 starts with large sets of cases belonging to known classes. The cases, +https://openalex.org/W1570448133;https://doi.org/10.1016/c2009-0-19715-5;Data Mining: Practical Machine Learning Tools and Techniques;Elsevier eBooks;2011;book;en;25711;['Ian H. Witten', 'Eibe Frank', 'Mark A. Hall'];[];['https://openalex.org/W23418094', 'https://openalex.org/W24402856', 'https://openalex.org/W28412257', 'https://openalex.org/W43179442', 'https://openalex.org/W64096637', 'https://openalex.org/W66926763', 'https://openalex.org/W69783631', 'https://openalex.org/W122789262', 'https://openalex.org/W123339444', 'https://openalex.org/W138217685', 'https://openalex.org/W149472151', 'https://openalex.org/W173229765', 'https://openalex.org/W187357405', 'https://openalex.org/W586094522', 'https://openalex.org/W589058777', 'https://openalex.org/W1483135265', 'https://openalex.org/W1483679765', 'https://openalex.org/W1492324553', 'https://openalex.org/W1492698999', 'https://openalex.org/W1495859460', 'https://openalex.org/W1500698297', 'https://openalex.org/W1506285740', 'https://openalex.org/W1513366687', 'https://openalex.org/W1516443426', 'https://openalex.org/W1520687314', 'https://openalex.org/W1523856491', 'https://openalex.org/W1524704912', 'https://openalex.org/W1528113134', 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'https://openalex.org/W2143426320', 'https://openalex.org/W2144907232', 'https://openalex.org/W2146257637', 'https://openalex.org/W2147492008', 'https://openalex.org/W2147810216', 'https://openalex.org/W2148949939', 'https://openalex.org/W2149706766', 'https://openalex.org/W2153028052', 'https://openalex.org/W2154318594', 'https://openalex.org/W2154642793', 'https://openalex.org/W2156909104', 'https://openalex.org/W2160642098', 'https://openalex.org/W2161077873', 'https://openalex.org/W2163915185', 'https://openalex.org/W2164818318', 'https://openalex.org/W2166559705', 'https://openalex.org/W2167793421', 'https://openalex.org/W2170112109', 'https://openalex.org/W2170654002', 'https://openalex.org/W2170726034', 'https://openalex.org/W2170913656', 'https://openalex.org/W2172162418', 'https://openalex.org/W2172780573', 'https://openalex.org/W2277957941', 'https://openalex.org/W2331052961', 'https://openalex.org/W2621280964', 'https://openalex.org/W2912934387', 'https://openalex.org/W2913066018', 'https://openalex.org/W2914369697', 'https://openalex.org/W2953014340', 'https://openalex.org/W2988864014', 'https://openalex.org/W3017143921', 'https://openalex.org/W3023540311', 'https://openalex.org/W3083113686', 'https://openalex.org/W3085162807', 'https://openalex.org/W3123294050', 'https://openalex.org/W3163638146', 'https://openalex.org/W3193477162', 'https://openalex.org/W3203633735', 'https://openalex.org/W3208887124'];['Computer science', 'Machine learning', 'Data science', 'Data mining', 'Artificial intelligence'];['Computer science', 'Machine learning', 'Data science', 'Data mining', 'Artificial intelligence'];"As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more." +https://openalex.org/W3120740533;;UCI Machine Learning Repository;Medical Entomology and Zoology;2007;article;en;24320;['Arthur Asuncion'];[];[];['Computer science', 'Artificial intelligence'];['Computer science', 'Artificial intelligence']; +https://openalex.org/W1663973292;https://doi.org/10.1117/1.2819119;Pattern Recognition and Machine Learning;Journal of Electronic Imaging;2007;article;en;22082;['Nasser M. Nasrabadi'];['West Virginia University', 'Microsoft Research (United Kingdom)'];['https://openalex.org/W1480376833', 'https://openalex.org/W1496317909', 'https://openalex.org/W2117812871', 'https://openalex.org/W3215037115', 'https://openalex.org/W4232383088', 'https://openalex.org/W4292691288'];['Computer science', 'Imaging science', 'Cover (algebra)', 'Data science', 'Artificial intelligence', 'Engineering'];['Computer science', 'Imaging science', 'Cover (algebra)', 'Data science', 'Artificial intelligence', 'Engineering', 'Mechanical engineering'];The Journal of Electronic Imaging (JEI), copublished bimonthly with the Society for Imaging Science and Technology, publishes peer-reviewed papers that cover research and applications in all areas of electronic imaging science and technology. +https://openalex.org/W1639032689;;Genetic Algorithms in Search, Optimization and Machine Learning;;1988;book;en;17771;['David E. Goldberg'];[];[];['Pascal (unit)', 'Computer science', 'Genetic programming', 'Genetic algorithm', 'Machine learning', 'Artificial intelligence', 'Quality control and genetic algorithms', 'Theoretical computer science', 'Algorithm', 'Programming language', 'Meta-optimization'];['Pascal (unit)', 'Computer science', 'Genetic programming', 'Genetic algorithm', 'Machine learning', 'Artificial intelligence', 'Quality control and genetic algorithms', 'Theoretical computer science', 'Algorithm', 'Programming language', 'Meta-optimization'];David Goldberg's Genetic Algorithms in Search, Optimization and Machine Learning is by far the bestselling introduction to genetic algorithms. Goldberg is one of the preeminent researchers in the field--he has published over 100 research articles on genetic algorithms and is a student of John Holland, the father of genetic algorithms--and his deep understanding of the material shines through. The book contains a complete listing of a simple genetic algorithm in Pascal, which C programmers can easily understand. The book covers all of the important topics in the field, including crossover, mutation, classifier systems, and fitness scaling, giving a novice with a computer science background enough information to implement a genetic algorithm and describe genetic algorithms to a friend. +https://openalex.org/W1583837637;https://doi.org/10.1145/1273496;Proceedings of the 24th international conference on Machine learning;;2007;preprint;en;11733;[];[];[];['Presentation (obstetrics)', 'Library science', 'Computer science', 'Medical education', 'Medicine'];['Presentation (obstetrics)', 'Library science', 'Computer science', 'Medical education', 'Medicine', 'Radiology'];This volume contains the papers accepted to the 24th International Conference on Machine Learning (ICML 2007), which was held at Oregon State University in Corvalis, Oregon, from June 20th to 24th, 2007. ICML is the annual conference of the International Machine Learning Society (IMLS), and provides a venue for the presentation and discussion of current research in the field of machine learning. These proceedings can also be found online at: http://www.machinelearning.org. This year there were 522 submissions to ICML. There was a very thorough review process, in which each paper was reviewed by three program committee (PC) members. Authors were able to respond to the initial reviews, and the PC members could then modify their reviews based on online discussions and the content of this author response. For the first time this year there were two discussion periods led by the senior program committee (SPC), one just before and one after the submission of author responses. At the end of the second discussion period, the SPC members gave their recommendations and provided a summary review for each of their papers. Also for the first time, authors were asked to submit a list of changes with their final accepted papers, which was checked by the SPCs to ensure that reviewer comments had been addressed. Apart from the length restrictions on papers and the compressed time frame, the review process for ICML resembles that of many journal publications. In total, 150 papers were accepted to ICML this year, including a very small number of papers which were initially conditionally accepted, yielding an overall acceptance rate of 29%. ICML attracts submissions from machine learning researchers around the globe. The 150 accepted papers this year were geographically distributed as follows: 66 papers had a first author from the US, 32 from Europe, 19 from China or Hong Kong, 11 from Canada, 6 from India, 5 each from Australia and Japan, 3 from Israel, and 1 each from Korea, Russia and Taiwan. In addition to the main program of accepted papers, which includes both a talk and poster presentation for each paper, the ICML program included 3 workshops and 8 tutorials on machine learning topics which are currently of broad interest. We were also extremely pleased to have David Heckerman (Microsoft Research), Joshua Tenenbaum (Massachussetts Institute of Technology), and Bernhard Schölkopf (Max Planck Institute for Biological Cybernetics) as the invited speakers this year. Thanks to sponsorship by the Machine Learning Journal, we were able to award a number of outstanding student paper prizes. We were fortunate this year that ICML was co-located with the International Conference on Inductive Logic Programming (ILP 2007). ICML and ILP held joint sessions on the first day of ICML 2007. +https://openalex.org/W1503398984;;Machine learning a probabilistic perspective;;2012;book;en;9327;['Kevin P. Murphy'];[];[];['Computer science', 'Probabilistic logic', 'Artificial intelligence', 'Field (mathematics)', 'Conditional random field', 'Heuristic', 'Machine learning', 'Graphical model', 'Regularization (linguistics)', 'Software', 'Programming language'];['Computer science', 'Probabilistic logic', 'Artificial intelligence', 'Field (mathematics)', 'Conditional random field', 'Heuristic', 'Machine learning', 'Graphical model', 'Regularization (linguistics)', 'Software', 'Programming language', 'Pure mathematics', 'Mathematics'];Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students. +https://openalex.org/W1901616594;https://doi.org/10.1126/science.aaa8415;Machine learning: Trends, perspectives, and prospects;Science;2015;review;en;9497;['Michael I. Jordan', 'Tom M. Mitchell'];['University of California, Berkeley', 'Carnegie Mellon University'];['https://openalex.org/W99485931', 'https://openalex.org/W1503398984', 'https://openalex.org/W1512585804', 'https://openalex.org/W1554944419', 'https://openalex.org/W1569098853', 'https://openalex.org/W1873763122', 'https://openalex.org/W1970472910', 'https://openalex.org/W1992570774', 'https://openalex.org/W1999352252', 'https://openalex.org/W2006452405', 'https://openalex.org/W2019363670', 'https://openalex.org/W2042469398', 'https://openalex.org/W2047281923', 'https://openalex.org/W2076063813', 'https://openalex.org/W2097381042', 'https://openalex.org/W2100495367', 'https://openalex.org/W2117726420', 'https://openalex.org/W2127180992', 'https://openalex.org/W2132001804', 'https://openalex.org/W2145339207', 'https://openalex.org/W2146774335', 'https://openalex.org/W2151320232', 'https://openalex.org/W2160815625', 'https://openalex.org/W2163605009', 'https://openalex.org/W2174706414', 'https://openalex.org/W2618530766', 'https://openalex.org/W3102480238', 'https://openalex.org/W4214717370', 'https://openalex.org/W4231109964', 'https://openalex.org/W4238893454', 'https://openalex.org/W4244670803', 'https://openalex.org/W4292363360'];['Intersection (aeronautics)', 'Computer science', 'Artificial intelligence', 'Core (optical fiber)', 'Data science', 'Machine learning', 'Big data', 'Computation', 'Lying', 'Engineering', 'Data mining'];['Intersection (aeronautics)', 'Computer science', 'Artificial intelligence', 'Core (optical fiber)', 'Data science', 'Machine learning', 'Big data', 'Computation', 'Lying', 'Engineering', 'Data mining', 'Aerospace engineering', 'Radiology', 'Telecommunications', 'Medicine', 'Algorithm'];Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. +https://openalex.org/W1746819321;https://doi.org/10.7551/mitpress/3206.001.0001;Gaussian Processes for Machine Learning;The MIT Press eBooks;2005;book;en;10487;['Carl Edward Rasmussen', 'Christopher K. I. 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'Artificial intelligence', 'Computer science', 'Online machine learning', 'Gaussian process', 'Probabilistic logic', 'Relevance vector machine', 'Support vector machine', 'Kernel method', 'Artificial neural network', 'Gaussian', 'Quantum mechanics', 'Physics'];A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. +https://openalex.org/W2997591727;https://doi.org/10.5555/1953048.2078195;Scikit-learn: Machine Learning in Python;Journal of Machine Learning Research;2011;article;en;8193;['PedregosaFabian', 'VaroquauxGaël', 'GramfortAlexandre', 'MichelVincent', 'ThirionBertrand', 'GriselOlivier', 'BlondelMathieu', 'PrettenhoferPeter', 'WeissRon', 'DubourgVincent', 'VanderplasJake', 'PassosAlexandre', 'CournapeauDavid', 'BrucherMatthieu', 'PerrotMatthieu', 'DuchesnayÉdouard'];[];[];['Python (programming language)', 'Computer science', 'Artificial intelligence', 'Machine learning', 'Programming language'];['Python (programming language)', 'Computer science', 'Artificial intelligence', 'Machine learning', 'Programming language'];Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. 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Tucker', 'Vijay Vasudevan', 'Pete Warden', 'Martin Wicke', 'Yuan Yu', 'Xiaoqiang Zheng'];['Google (United States)'];['https://openalex.org/W104184427', 'https://openalex.org/W145476170', 'https://openalex.org/W941230081', 'https://openalex.org/W1442374986', 'https://openalex.org/W1484210532', 'https://openalex.org/W1485981043', 'https://openalex.org/W1493893823', 'https://openalex.org/W1498436455', 'https://openalex.org/W1548328233', 'https://openalex.org/W1598866093', 'https://openalex.org/W1658008008', 'https://openalex.org/W1667652561', 'https://openalex.org/W1869752048', 'https://openalex.org/W1978660892', 'https://openalex.org/W1992479210', 'https://openalex.org/W2041517243', 'https://openalex.org/W2055312318', 'https://openalex.org/W2061570747', 'https://openalex.org/W2066443755', 'https://openalex.org/W2072566913', 'https://openalex.org/W2083842231', 'https://openalex.org/W2099471712', 'https://openalex.org/W2100664567', 'https://openalex.org/W2109722477', 'https://openalex.org/W2117539524', 'https://openalex.org/W2120480077', 'https://openalex.org/W2131400476', 'https://openalex.org/W2131975293', 'https://openalex.org/W2132339004', 'https://openalex.org/W2140833774', 'https://openalex.org/W2141992894', 'https://openalex.org/W2145339207', 'https://openalex.org/W2146757372', 'https://openalex.org/W2160815625', 'https://openalex.org/W2163961697', 'https://openalex.org/W2168231600', 'https://openalex.org/W2173213060', 'https://openalex.org/W2186615578', 'https://openalex.org/W2198403777', 'https://openalex.org/W2259472270', 'https://openalex.org/W2308045930', 'https://openalex.org/W2336650964', 'https://openalex.org/W2339765813', 'https://openalex.org/W2384495648', 'https://openalex.org/W2591588155', 'https://openalex.org/W2949117887', 'https://openalex.org/W2949245006', 'https://openalex.org/W2949501655', 'https://openalex.org/W2949605076', 'https://openalex.org/W2949650786', 'https://openalex.org/W2949888546', 'https://openalex.org/W2950094539', 'https://openalex.org/W2950179405', 'https://openalex.org/W2950577311', 'https://openalex.org/W2951714314', 'https://openalex.org/W2951781666'];['Scale (ratio)', 'Computer science', 'Artificial intelligence', 'Machine learning', 'Cartography', 'Geography'];['Scale (ratio)', 'Computer science', 'Artificial intelligence', 'Machine learning', 'Cartography', 'Geography'];"TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous ""parameter server"" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with particularly strong support for training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model in contrast to existing systems, and demonstrate the compelling performance that TensorFlow achieves for several real-world applications." +https://openalex.org/W2084812512;;UCI Repository of machine learning databases;Medical Entomology and Zoology;1998;article;en;10547;['Catherine Blake'];[];[];['Computer science', 'Database', 'Artificial intelligence'];['Computer science', 'Database', 'Artificial intelligence']; +https://openalex.org/W2118020653;https://doi.org/10.1145/505282.505283;Machine learning in automated text categorization;ACM Computing Surveys;2002;review;en;7884;['Fabrizio Sebastiani'];['Consorzio Pisa Ricerche'];['https://openalex.org/W12876238', 'https://openalex.org/W22906612', 'https://openalex.org/W35304842', 'https://openalex.org/W59979790', 'https://openalex.org/W74894479', 'https://openalex.org/W81384683', 'https://openalex.org/W81511778', 'https://openalex.org/W104970664', 'https://openalex.org/W107306860', 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'https://openalex.org/W6682342947', 'https://openalex.org/W6738852829', 'https://openalex.org/W6792154874', 'https://openalex.org/W6794861161', 'https://openalex.org/W7046466245', 'https://openalex.org/W7064660697'];['Computer science', 'Categorization', 'Software portability', 'Artificial intelligence', 'Classifier (UML)', 'Machine learning', 'Text categorization', 'Natural language processing'];['Computer science', 'Categorization', 'Software portability', 'Artificial intelligence', 'Classifier (UML)', 'Machine learning', 'Text categorization', 'Natural language processing', 'Programming language'];The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation. +https://openalex.org/W1504694836;;Programs for Machine Learning;;1994;article;en;5804;['Steven L. Salzberg', 'Alberto M. Segre'];['Johns Hopkins University'];['https://openalex.org/W1534707631', 'https://openalex.org/W1573640198', 'https://openalex.org/W1594031697', 'https://openalex.org/W1604329830', 'https://openalex.org/W2149706766', 'https://openalex.org/W3085162807'];['Successor cardinal', 'Artificial intelligence', 'Computer science', 'Decision tree', 'Machine learning', 'Subject (documents)', 'ID3 algorithm', 'Decision tree learning', 'Incremental decision tree', 'World Wide Web', 'Mathematics'];['Successor cardinal', 'Artificial intelligence', 'Computer science', 'Decision tree', 'Machine learning', 'Subject (documents)', 'ID3 algorithm', 'Decision tree learning', 'Incremental decision tree', 'World Wide Web', 'Mathematics', 'Mathematical analysis'];Algorithms for constructing decision trees are among the most well known and widely used of all machine learning methods. Among decision tree algorithms, J. Ross Quinlan's ID3 and its successor, C4.5, are probably the most popular in the machine learning community. These algorithms and variations on them have been the subject of numerous research papers since Quinlan introduced ID3. Until recently, most researchers looking for an introduction to decision trees turned to Quinlan's seminal 1986 Machine Learning journal article [Quinlan, 1986]. In his new book, C4.5: Programs for Machine Learning, Quinlan has put together a definitive, much needed description of his complete system, including the latest developments. As such, this book will be a welcome addition to the library of many researchers and students. +https://openalex.org/W2271840356;https://doi.org/10.48550/arxiv.1603.04467;TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems;arXiv (Cornell University);2016;preprint;en;9772;['Martı́n Abadi', 'Ashish Agarwal', 'Paul Barham', 'Eugene Brevdo', 'Zhifeng Chen', 'Craig Citro', 'Gregory S. Corrado', 'Andy Davis', 'Jay B. Dean', 'Matthieu Devin', 'Sanjay Ghemawat', 'Ian Goodfellow', 'Andrew Harp', 'Geoffrey Irving', 'Michael Isard', 'Yangqing Jia', 'Rafał Józefowicz', 'Łukasz Kaiser', 'Manjunath Kudlur', 'Josh Levenberg', 'Dan Mané', 'Rajat Monga', 'Sherry Moore', 'Derek G. Murray', 'Chris Olah', 'Mike Schuster', 'Jonathon Shlens', 'Benoit Steiner', 'Ilya Sutskever', 'Kunal Talwar', 'Paul A. Tucker', 'Vincent Vanhoucke', 'Vijay Vasudevan', 'Fernanda Viégas', 'Oriol Vinyals', 'Pete Warden', 'Martin Wattenberg', 'Martin Wicke', 'Yuan Yu', 'Xiaoqiang Zheng'];[];['https://openalex.org/W1442374986', 'https://openalex.org/W1484210532', 'https://openalex.org/W1487337216', 'https://openalex.org/W1498436455', 'https://openalex.org/W1526734559', 'https://openalex.org/W1548328233', 'https://openalex.org/W1598866093', 'https://openalex.org/W1614298861', 'https://openalex.org/W1658008008', 'https://openalex.org/W1667652561', 'https://openalex.org/W1738019091', 'https://openalex.org/W1827297289', 'https://openalex.org/W1869752048', 'https://openalex.org/W1947291763', 'https://openalex.org/W1978660892', 'https://openalex.org/W1978924650', 'https://openalex.org/W2002257715', 'https://openalex.org/W2016053056', 'https://openalex.org/W2017351599', 'https://openalex.org/W2032036568', 'https://openalex.org/W2035424729', 'https://openalex.org/W2055312318', 'https://openalex.org/W2064675550', 'https://openalex.org/W2082171780', 'https://openalex.org/W2097117768', 'https://openalex.org/W2100830825', 'https://openalex.org/W2123024445', 'https://openalex.org/W2130942839', 'https://openalex.org/W2131975293', 'https://openalex.org/W2138243089', 'https://openalex.org/W2141992894', 'https://openalex.org/W2146757372', 'https://openalex.org/W2155893237', 'https://openalex.org/W2160815625', 'https://openalex.org/W2168231600', 'https://openalex.org/W2171532807', 'https://openalex.org/W2181607856', 'https://openalex.org/W2253807446', 'https://openalex.org/W2507756961', 'https://openalex.org/W2949117887', 'https://openalex.org/W2950789693', 'https://openalex.org/W2951781666'];['Scale (ratio)', 'Computer science', 'Artificial intelligence', 'Machine learning', 'Distributed computing', 'Geography', 'Cartography'];['Scale (ratio)', 'Computer science', 'Artificial intelligence', 'Machine learning', 'Distributed computing', 'Geography', 'Cartography'];TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery. This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. The TensorFlow API and a reference implementation were released as an open-source package under the Apache 2.0 license in November, 2015 and are available at www.tensorflow.org. +https://openalex.org/W3163993681;https://doi.org/10.1038/s42254-021-00314-5;Physics-informed machine learning;Nature Reviews Physics;2021;review;en;6362;['George Em Karniadakis', 'Ioannis G. Kevrekidis', 'Lu Lu', 'Paris Perdikaris', 'Sifan Wang', 'Liu Yang'];['Massachusetts Institute of Technology', 'University of Pennsylvania', 'Johns Hopkins University', 'Brown University', 'Applied Mathematics (United States)'];['https://openalex.org/W196871588', 'https://openalex.org/W1538131130', 'https://openalex.org/W1543241987', 'https://openalex.org/W1731081199', 'https://openalex.org/W1856502440', 'https://openalex.org/W1899249567', 'https://openalex.org/W1972005403', 'https://openalex.org/W1972576201', 'https://openalex.org/W1976068300', 'https://openalex.org/W1979769287', 'https://openalex.org/W2000359198', 'https://openalex.org/W2002355073', 'https://openalex.org/W2017880874', 'https://openalex.org/W2018159038', 'https://openalex.org/W2019465613', 'https://openalex.org/W2020561998', 'https://openalex.org/W2025444507', 'https://openalex.org/W2060229389', 'https://openalex.org/W2072072671', 'https://openalex.org/W2099471712', 'https://openalex.org/W2101371964', 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intelligence', 'Machine learning', 'Multiphysics', 'Inference', 'Artificial neural network', 'Physical law', 'Field (mathematics)', 'Discretization', 'Kernel method', 'Deep learning', 'Theoretical computer science', 'Mathematics', 'Support vector machine', 'Finite element method', 'Mathematical analysis', 'Philosophy', 'Pure mathematics', 'Physics', 'Epistemology', 'Thermodynamics']; +https://openalex.org/W1601795611;https://doi.org/10.1108/03684920710743466;Pattern Recognition and Machine Learning;Kybernetes;2007;article;en;8434;[];[];[];['Computer science', 'Cybernetics', 'Artificial intelligence', 'Machine learning'];['Computer science', 'Cybernetics', 'Artificial intelligence', 'Machine learning']; diff --git a/advanced_standardized_data.xlsx b/advanced_standardized_data.xlsx new file mode 100644 index 00000000..1006266e Binary files /dev/null and b/advanced_standardized_data.xlsx differ diff --git a/api_retriever.py b/api_retriever.py new file mode 100644 index 00000000..860e0e00 --- /dev/null +++ b/api_retriever.py @@ -0,0 +1,21 @@ +import requests +import pandas as pd + + +def fetch_from_openalex(query): + """ + Queries OpenAlex for a given search string and returns a DataFrame. + Handles basic API interaction. + """ + base_url = "https://api.openalex.org/works" + params = {"search": query, "per-page": 20} + + print(f"Fetching data for: {query}...") + response = requests.get(base_url, params=params) + + if response.status_code == 200: + data = response.json().get('results', []) + return pd.DataFrame(data) + else: + print(f"Error fetching data: {response.status_code}") + return pd.DataFrame() \ No newline at end of file diff --git a/app.py b/app.py index f0891f89..237007f7 100644 --- a/app.py +++ b/app.py @@ -1,8640 +1,8640 @@ -# Biblioshiny - A Python-based Shiny App for Bibliometrix - -# This application provides an interactive dashboard for comprehensive bibliometric analysis, inspired by the R-based Biblioshiny tool. It leverages the Shiny for Python framework to deliver a modern, web-based user interface for importing, managing, analyzing, and visualizing bibliometric data from various scientific databases. -# Main Features: -# -------------- -# - Header Bar: Displays the app logo, name, and dropdown menus for notifications (CRAN updates), help/tutorials, donations, and credits/links. -# - Sidebar Navigation: Accordion-based sidebar for navigating between main sections: Info, Data, and Settings. -# - Main Content Area: Contains hidden navigation panels for each analysis module, including: -# - Welcome/info page with supported databases and references. -# - Data import and management (raw files, Bibliometrix files, or sample datasets). -# - Data filtering by year, language, document type, citations, and Bradford's Law zones. -# - Overview and main information summary of the dataset. -# - Annual scientific production and average citations per year. -# - Three-field plot (Sankey diagram) for exploring relationships between fields. -# - Most relevant sources/authors/affiliations and their local/global impact. -# - Bradford's and Lotka's Law analyses. -# - Production over time for sources, authors, affiliations, and countries. -# - Country-level analyses (production, citations, corresponding authors). -# - Most cited documents and references (global/local). -# - Interactive Widgets: Each analysis panel provides parameter controls (e.g., number of items, impact measures) and supports exporting results as images or Excel files. -# - Report Generation: Users can add tables/plots to a report and export results for further use. -# - Custom Styling: Includes custom CSS and responsive layouts for a modern look and feel. -# Key Implementation Details: -# --------------------------- -# - Uses `shiny`, `shinywidgets`, and `shiny.express` for UI and reactivity. -# - Relies on modular functions (imported from `functions` and `www.services`) for data processing and plotting. -# - Employs `reactive.Value` and `@reactive.calc` for state management and efficient updates. -# - Integrates with Google Gemini API for AI-powered chat support. -# - Provides extensive markdown-based documentation and references within the UI. -# - Supports a wide range of bibliographic databases and file formats, with guidance for optimal usage. -# Intended Audience: -# ------------------ -# - Researchers, librarians, and analysts interested in bibliometric and scientometric studies. -# - Users seeking an open-source, Python-based alternative to R's Biblioshiny for science mapping and research evaluation. -# Usage: -# ------ -# - Run the app with Shiny for Python. -# - Navigate through the sidebar to import data, apply filters, and explore bibliometric analyses. -# - Use the AI chat for contextual help or explanations. -# - Export tables/plots or generate comprehensive reports as needed. -# Note: -# ----- -# - Some features (e.g., API integration, merge collections) are under construction. -# - Requires appropriate dependencies and access to the Gemini API for AI chat functionality. -# ----- -# Author: PRAISELab Team - - -# Import necessary libraries for better performance - avoid importing everything -import tempfile -import os -import requests -import functools -from datetime import datetime -import pandas as pd -import io -from functions import * -from www.services import * -from google import genai -from shiny import express -from shiny import render, ui -from google.genai import types -from shiny import reactive, render -from shinywidgets import render_widget -from shiny.express import ui, input, render - -# Setup the Directory for static assets - optimized for performance -base_dir = tempfile.gettempdir() # Use system temp dir instead of creating new temp file -express.app_opts(static_assets=base_dir, debug=False) - -# --- Toggle button --- -# This button toggles the visibility of the sidebar(s) in the UI. -ui.tags.button("☰", id="toggleSidebar", class_="sidebar-toggle") - -# --- Page Options --- -# Set global page options such as window title and layout. -ui.page_opts( - window_title="Bibliometrix - A tool for comprehensive science mapping analysis", - full_width=True, -) - -# --- UI and UX experience --- -# Include custom CSS for the app's appearance. -ui.include_css("www/static/biblioshiny.css") - -# --- Header --- -# The header bar contains the logo, app name, and a set of dropdown menus for notifications, help, donations, and credits. -with ui.tags.div(class_="header-bar"): - # Logo and app name section - with ui.tags.div(class_="header-logo"): - ui.markdown( - """ -
- -
- """ - ) - ui.span("Bibliometrix") - # Header icons and dropdowns section - with ui.tags.div(class_="header-icons"): - # --- Dropdown: CRAN update notification --- - with ui.tags.div(class_="dropdown"): - # Helper function to check for the latest CRAN version of bibliometrix (cached for efficiency) - @functools.lru_cache(maxsize=1) - def get_latest_cran_version(): - try: - resp = requests.get("https://crandb.r-pkg.org/bibliometrix") - if resp.status_code == 200: - data = resp.json() - return data.get("Version", None) - except Exception: - return None - - # Define the current version and check for updates - CURRENT_CRAN_VERSION = "4.0.0" - latest_version = get_latest_cran_version() - has_update = latest_version and latest_version != CURRENT_CRAN_VERSION - - # If an update is available, show a red dot on the mail icon - mail_icon = ICONS["mail"] - if has_update: - mail_icon = ui.tags.span( - ICONS["mail"], - ui.tags.span( - "", - style=( - "position: absolute; top: 2px; right: 2px; " - "width: 10px; height: 10px; background: red; border-radius: 50%; " - "display: inline-block; border: 2px solid white;" - ), - ), - style="position: relative; display: inline-block;" - ) - - # Dropdown button for notifications - ui.span(mail_icon, class_="dropbtn") - with ui.tags.div(class_="dropdown-content"): - # Show update message if a new version is available, otherwise show "no update" - if has_update: - ui.markdown(f""" - 📢 **New update available!** - ```python - bibliometrix/biblioshiny {latest_version} - is available on CRAN! - ``` - Update the package to get the latest features. - """) - else: - ui.markdown(f""" - 📢 **No new update** - ```python - bibliometrix/biblioshiny {CURRENT_CRAN_VERSION} - ``` - You are using the latest available version. - """) - - # --- Dropdown: Help and Tutorials --- - with ui.tags.div(class_="dropdown"): - ui.span(ICONS["question"], class_="dropbtn") - with ui.tags.div(class_="dropdown-content"): - ui.a( - ui.tags.i(class_="fa fa-book", style="margin-right: 16px;"), - "R - Package Tutorial", - href="https://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html", - target="_blank" - ) - ui.a( - ui.tags.i(class_="fa fa-info-circle", style="margin-right: 16px;"), - "R - Introduction to bibliometrix", - href="https://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html", - target="_blank" - ) - ui.a( - ui.tags.i(class_="fa fa-play-circle", style="margin-right: 16px;"), - "R - Biblioshiny Tutorial", - href="https://bibliometrix.org/biblioshiny/assets/player/KeynoteDHTMLPlayer.html#0", - target="_blank" - ) - - # --- Dropdown: Donations --- - with ui.tags.div(class_="dropdown"): - ui.span(ICONS["donate"], class_="dropbtn") - with ui.tags.div(class_="dropdown-content"): - ui.a( - ui.tags.i(class_="fa fa-credit-card", style="margin-right: 16px;"), - "Donate", - href="https://www.bibliometrix.org/home/index.php/donation", - target="_blank" - ) - - # --- Dropdown: Credits and Links --- - with ui.tags.div(class_="dropdown"): - ui.span(ICONS["credits"], class_="dropbtn") - with ui.tags.div(class_="dropdown-content"): - ui.a( - ui.tags.i(class_="fa fa-globe", style="margin-right: 16px;"), - "Bibliometrix", - href="https://www.bibliometrix.org/", - target="_blank" - ) - ui.a( - ui.tags.i(class_="fa fa-envelope", style="margin-right: 16px;"), - "K-Synth", - href="https://www.k-synth.unina.it/", - target="_blank" - ) - ui.a( - ui.tags.i(class_="fa fa-github", style="margin-right: 8px;"), - "GitHub", - href="https://github.com/massimoaria/bibliometrix", - target="_blank" - ) - -# --- Sidebar fictitious --- -with ui.tags.div(id="sidebar", class_="custom-sidebar"): - with ui.accordion(id="sidebar_accordion", multiple=False, open=False): - # Info Section - with ui.accordion_panel("Biblioshiny", icon=ICONS["home_colored"]): - ui.input_action_button("go_about", "Biblioshiny", class_="sidebar-button", icon=ICONS["home"]) - - # Data Section - with ui.accordion_panel("Data", icon=ICONS["database_colored"]): - ui.input_action_button("go_import", "Import or Load", class_="sidebar-button", icon=ICONS["data"]) - ui.input_action_button("go_api", "API", class_="sidebar-button", icon=ICONS["api"]) - ui.input_action_button("go_collections", "Merge Collection", class_="sidebar-button", icon=ICONS["merge"]) - - # Settings Section - with ui.accordion_panel("Settings", icon=ICONS["settings_colored"]): - ui.input_action_button("go_settings_2", "Settings", class_="sidebar-button", icon=ICONS["settings"]) - - # --- Footer --- - with ui.tags.footer(class_="custom-footer", style="background: #5567BB; color: white; text-align: center; padding: 10px 0; position: fixed; bottom: 0; width: 300px; z-index: 1000;"): - ui.markdown( - """ -
- © 2025 - - - - - Bibliometrix - -
-

Version: 1.0.0 - Shiny for Python Based Application

- """ - ) - - -# --- Main Content --- -with ui.tags.div(id="mainContent", class_="main-content"): - with ui.navset_hidden(id="hidden_tabs"): - - # --- Welcome/Info Page --- - with ui.nav_panel("None", value="info"): - ui.h1("biblioshiny: the python-based shiny app for bibliometrix", style="text-align: center; color: #5567BB;"), - ui.div( - ui.img(src="https://www.bibliometrix.org/logo_new.png", class_="logo", width="400px"), - style="text-align: center;" - ), - ui.div( - ui.input_action_button( - id="btn_import_data", - label="Import your data now", - icon=ICONS["play"], - class_="btn-primary", - style="margin-top: 20px; margin-bottom: 20px; padding: 10px 20px; font-size: 16px; background-color: #5567BB; color: white; border: none; border-radius: 5px; cursor: pointer;", - ), - ui.input_action_button( - id="btn_github", - label="R-tool on GitHub", - icon=ICONS["github"] if "github" in ICONS else None, - class_="btn-secondary", - style="margin-top: 20px; margin-bottom: 20px; margin-left: 10px; padding: 10px 20px; font-size: 16px; background-color: #24292e; color: white; border: none; border-radius: 5px; cursor: pointer;", - onclick="window.open('https://github.com/massimoaria/bibliometrix', '_blank')", - ), - style="text-align: center;" - ), - ui.markdown( - """ -
-
- For an introduction and live examples, visit the Bibliometrix website -
- 📖 biblioshiny and bibliometrix are open-source and freely available for use, distributed under the MIT license. -
- When they are used in a publication, we ask that authors to cite the following reference: -

- 
 Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. 
-                    Journal of Informetrics, 11(4), 959-975.
- ❗ Failure to properly cite the software is considered a violation of the license. -
-
-
- """ - ) - - ui.div( - ui.hr(), - ui.h3("Supported Bibliographic Databases and Suggested File Formats", style="color: #5567BB;"), - ui.p("Biblioshiny supports various bibliographic databases, allowing users to import and analyze collections exported from these sources. Below is a list of supported databases, their file formats, and the metadata they contain."), - ui.markdown( - """ -
- - - - - - - -
- """ - ), - - ui.markdown( - """ -
- ℹ️ Web of Science, Scopus, and OpenAlex allow the export of complete metadata, supporting full analysis capabilities in Biblioshiny. - Other databases (e.g., Dimensions, PubMed, Cochrane) offer limited metadata export, restricting the scope of possible analyses. - Below is a table showing supported file formats and suggested ones per database. -
- """ - ), - - ui.markdown("""

Databases, Available Metadata, and Suggested File Formats:
"""), - ui.markdown( - """ -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
📌 Source📂 Format📑 Exported Metadata✅ Suggested Format
Web of ScienceBibTeX, Plaintext, EndNoteAllPlaintext
ScopusBibTeX, CSVAll except referencesCSV
OpenAlexExcel, APIAll (limited refs)Excel
DimensionsExcel, APIAll (limited refs)Excel
The LensCSVAll (limited refs)CSV
PubMedPubMed, APIBasic MetadataPubMed
CochranePlaintextBasic MetadataPlaintext
-
- """ - ), - - ui.hr(), - ui.h3("""Main Authors’ References (about bibliometrics)""", style="color: #5567BB;"), - ui.p("A collection of scientific articles about the use of bibliometric approaches in business and management disciplines"), - ui.markdown( - """ -
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
👩‍🔬 Author(s)📝 Title🏛️ Journal/Conference📅 Year🔗 DOI
Cuccurullo, C., Aria, M., & Sarto, F.Twenty years of research on performance management in business and public administration domainsAcademy of Management Proceedings2013Link
Sarto, F., Cuccurullo, C., & Aria, M.Exploring healthcare governance literature: systematic review and paths for future researchMecosan2014Link
Cuccurullo, C., Aria, M., & Sarto, F.Twenty years of research on performance management in business and public administration domainsCorrespondence Analysis and Related Methods conference (CARME 2015)2015Link
Cuccurullo, C., Aria, M., & Sarto, F.Foundations and trends in performance management. A twenty-five years bibliometric analysis in business and public administration domainsScientometrics2016Link
Aria, M. & Cuccurullo, C.bibliometrix: An R-tool for comprehensive science mapping analysisJournal of Informetrics2017Link
Aria M., Misuraca M., Spano M.Mapping the evolution of social research and data science on 30 years of Social Indicators ResearchSocial Indicators Research2020Link
Aria M., Alterisio A., Scandurra A, Pinelli C., D’Aniello B.The scholar’s best friend: research trends in dog cognitive and behavioural studiesAnimal Cognition2021Link
Belfiore, A., Salatino, A., & Osborne, F.Characterising Research Areas in the field of AIarXiv preprint2022Link
Belfiore, A., Cuccurullo, C., & Aria, M.IoT in healthcare: A scientometric analysisTechnological Forecasting and Social Change2022Link
D'Aniello, L., Spano, M., Cuccurullo, C., & Aria, M.Academic Health Centers’ configurations, scientific productivity, and impact: insights from the Italian settingHealth Policy2022Link
Belfiore, A., Scaletti, A., Lavorato, D., & Cuccurullo, C.The long process by which HTA became a paradigm: A longitudinal conceptual structure analysisHealth Policy2022Link
Aria, M., Cuccurullo, C., D’Aniello, L., Misuraca, M., & Spano, M.Thematic Analysis as a New Culturomic Tool: The Social Media Coverage on COVID-19 Pandemic in ItalySustainability2022Link
Aria, M., Le, T., Cuccurullo, C., Belfiore, A., & Choe, J.openalexR: An R-Tool for Collecting Bibliometric Data from OpenAlexR Journal2023Link
-
-
- """ - ), - - ui.hr(), - - ui.markdown( - """ -
-
- - - -

- PRAISELab
- Dept of Electrical Engineering and Information Technology
- University of Naples Federico II
- Via Claudio, 21
- 80126 Naples, Italy
- Linkedin
-
-
- - - -

- K-Synth Srl
- Dept of Economics and Statistics
- University of Naples Federico II
- Via Cinthia, Monte Santangelo Building 3, Sector D, 2nd floor I
- 80126 Naples, Italy
- Website: https://k-synth.com/
- Email: info@k-synth.com, - info@bibliometrix.org -
-
- - - -

- - University of Naples Federico II
- All Rights Reserved
- -

-
- - LinkedIn - - - Instagram - -
-
-
- """ - ), - style="margin-left:80px; margin-right:80px; font-size: 16px" - ) - - # --- Data Management Section --- - with ui.nav_panel("None", value="import"): - ui.h3("📊 Data Management", style="color: #5567BB;") - ui.p("Easily import, load, or export your dataset.") - # ---------- INITIALIZE VARIABLES ---------- - df = reactive.Value(None) - - # Optimized function to reset analysis results when dataset changes - def reset_all_analyses(): - """Reset all analysis results to None when a new dataset is loaded - optimized version""" - # Instead of complex introspection, use a simple direct approach - # Only reset if variables exist to avoid unnecessary operations - pass # Analysis results will be reset naturally when new data is loaded - - report_choices = reactive.Value({}) - report_excel = reactive.Value(io.BytesIO()) - selection = reactive.Value([]) - dpi = reactive.Value(300) - height = reactive.Value(7) - gemini_api_key = reactive.Value("") - - # Optimized loading modal function to avoid repetition - def create_loading_modal(analysis_type="analysis"): - """Create a standardized loading modal for better performance""" - phrases = [ - "⏳ Loading... Please wait.", - f"🔍 Analyzing {analysis_type}...", - "📊 Processing data...", - "📈 Calculating metrics...", - "✨ Almost there! Preparing your dashboard...", - "🔗 Connecting data...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - # ------------------------------------------ - # Layout with sidebar - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_load_data", position="right" ): - # Section for Import or Load - ui.h5("Data Import Options", style="color: #5567BB;") - ui.input_select( - "select", - "Choose an action:", - { - "": "-", - "1A": "Import raw data file(s)", - "1B": "Load Bibliometrix file(s)", - "1C": "Use a sample dataset" - }, - ) - - @render.express() - @reactive.event(input.select) - def select_db(): - if input.select() == "1A": - ui.input_select( - "database", - "Database:", - { - "wos": "Web of Science (WoS/WoK)", - "scopus": "Scopus", - "dimensions": "Dimensions", - "lens": "Lens.org", - "pubmed": "PubMed", - "cochrane": "Cochrane Library" - }, - ) - ui.input_select( - "author", - "Author Name format:", - { - "surname": "Surname and Initials", - "fullname": "Full name" - }, - ) - ui.input_file( - "Dataset", - "Choose File(s) (multiple supported)", - accept=[ - ".csv", - ".txt", - ".ciw", - ".bib", - ".xlsx", - ".zip", - ".xls", - ".rdata", - ".rda", - ".rds" - ], - multiple=True - ) - ui.p("Load raw data file(s) in .csv, .txt, or .zip format from WoS, Scopus, Dimensions, Lens.org, PubMed, or Cochrane Library", style="color: gray; font-size: 10px; margin-top: -20px;") - ui.input_action_button("start_button", "Start", icon=ICONS["play"]) - - elif input.select() == "1B": - ui.input_file("Dataset", "Choose a File", accept=[".xlsx"], multiple=True) - ui.input_action_button("start_button", "Start", icon=ICONS["play"]) - ui.markdown("Load a collection in **XLSX** or **R** format previously exported from bibliometrix") - - elif input.select() == "1C": - ui.h6("The use of bibliometric approaches in business and management disciplines.") - ui.p("Dataset 'Management'") - - ui.markdown("*A collection of scientific articles about the use of bibliometric approaches in business and management disciplines. Period: 1985 - 2020 , Source WoS.*") - ui.input_action_button("start_button", "Start", icon=ICONS["play"]) - ui.markdown("Select a predefined sample dataset for testing purposes.") - - else: - ui.p("Please select a valid action to begin managing your data.", style="color: gray;") - ui.p("Follow the instructions below to manage your data efficiently:") - ui.markdown( - """ -
- 1. Import Raw Data: Select 'Import raw data file(s)' to upload your dataset in .csv, .txt, or .zip format. Choose the correct database and author name format based on your source.
- 2. Load Bibliometrix Data: If you are working with Bibliometrix datasets, select the corresponding option to load your files.
- 3. Use a Sample Dataset: For testing purposes, you can load a predefined sample dataset.
- 4. Export Options: After processing, export your dataset as Excel (.xlsx) or R Data Format (.RData) for further analysis. -
- """ - ) - - # Section for Export Options - # ui.markdown("
") - # ui.h5("Export Collection Options") - # ui.input_select( - # "save", - # "Export as:", - # { - # "SAVE1": "Excel (.xlsx)", - # "SAVE2": "R Data Format (.RData)" - # }, - # ) - # ui.input_action_button("export_button", "Export", icon=ICONS["download"], disabled=True) - - @render.express() - @reactive.event(input.start_button) - def mostra(): - database = get_database(input) - ui.update_sidebar("sidebar_load_data", show=False) - ui.update_action_button("export_button", disabled=False) - ui.markdown(f"

Data of {database}

") - - if database == "Sample": - data = df.set(pd.read_excel("sources/samples/sample.xlsx")) - reset_all_analyses() # Reset analysis results when sample is loaded - - @render.express() - @reactive.event(input.Dataset) - def show_data(): - text = get_data(input, database, df, reset_all_analyses) - text - ui.HTML(init_itables()) - - @render.ui - @reactive.event(input.start_button) - def show_table(): - table_ui, _, _ = get_table(database, df) - return table_ui - - # -------- ADVICE BUTTON -------- - @render.ui - @reactive.event(input.advice_modal_completeness) - def show_advice_notification(): - return ui.notification_show( - ui.div( - ui.h4("Your metadata have no critical issues", style="font-size: 30px; text-align: center;"), - ui.input_action_button("close_advice_modal_notification", "OK", - style="display: block; margin: 20px auto;") - ), - duration=None, # La notifica rimane finché non viene chiusa - close_button=False, # Disabilita la X per la chiusura - id="advice_modal_notification", - ) - - # Aggiungi l'evento di chiusura al bottone OK - @reactive.effect - @reactive.event(input.close_advice_modal_notification) - def close_advice_notification(): - ui.notification_remove(id="advice_modal_notification") - - # -------- REPORT BUTTON -------- - @render.ui - @reactive.event(input.report_modal_completeness) - def show_missing_data_report(): - _, missingData, _ = get_table(database, df, modal=False) - dataframe = pd.read_html(io.StringIO(missingData)) - report_excel.set(add_to_report(report_choices, report_excel, [dataframe[0]], [], "missingdata")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Missing data added to report", duration=5, close_button=False) - - # -------- SAVE BUTTON -------- - completeness_table_download_folder = str(Path.home() / "Downloads") - todaydate = datetime.today().strftime("%Y-%m-%d") - completeness_table_image_path = os.path.join(completeness_table_download_folder, f"missingDataTable-{todaydate}.png") - @render.ui - @reactive.event(input.save_modal_completeness) - def save_dataframe_image(): - _, _, fig = get_table(database, df, dpi=dpi.get(), modal=False) - fig.write_image(completeness_table_image_path) - return ui.notification_show(f"✅ Missing data image saved into {completeness_table_image_path}", duration=5, close_button=False) - - # Loader indicator - @render.ui - def indicator_types_ui_all(): - return ui.busy_indicators.use( - spinners=input.start_button() > 0 - ) - - ui.h4("Description", style="color: #5567BB;") - ui.p("This section allows you to import, load, or export your dataset. You can choose to import raw data files from various databases, load previously saved Bibliometrix files, or use a sample dataset for testing purposes. Once the data is loaded, you can view it in a table format and export it as an Excel file or R Data Format for further analysis. " \ - "Biblioshiny supports various bibliographic databases, allowing users to import and analyze collections exported from these sources. Click on a database below to visit its official website and download your data for analysis."), - ui.markdown( - """ -
- - - - - - - -
- """ - ), - - ui.markdown( - """ -
- ℹ️ Web of Science, Scopus, and OpenAlex allow the export of complete metadata, supporting full analysis capabilities in biblioshiny. - Other databases (e.g., Dimensions, PubMed, Cochrane) offer limited metadata export, restricting the scope of possible analyses. - Below is a table showing supported file formats and suggested ones per database. -
- """ - ), - - with ui.nav_panel("None", value="API"): - ui.h3("🚧 Warning: API is under construction 🚧") - - with ui.nav_panel("None", value="collections"): - ui.h3("🚧 Warning: Merge Collection is under construction 🚧") - - with ui.nav_panel("None", value="filters"): - ui.h3("🔎 Filters", style="color: #5567BB;") - ui.p("Select the filters to apply to the dataset.") - - df_filters = reactive.Value(None) - df_filtered = reactive.Value(None) - - @reactive.calc - def filters(): - return get_filters(df) - - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_load_filers", position="right" ): - ui.h4("Filter Options", style="color: #5567BB;") - ui.p("Select the filters to apply to the dataset.") - - @render.express() - def show_filter(): - data = filters() - tmp = df_filters.set(data) #Esce sempre TRUE - - ui.input_slider( - "year_slider", - "Select Year Range", - sep="", - ticks=True, - min=data["Min_Year"][0], - max=data["Max_Year"][0], - value=(data["Min_Year"][0], data["Max_Year"][0]), - step=1, - time_format="YYYY" - ) - ui.input_selectize( - "languages", - "Select Languages", - data["LA"].unique().tolist(), - selected=data["LA"].unique().tolist(), - multiple=True - ) - ui.input_selectize( - "document_types", - "Select Document Types", - data["DT"].unique().tolist(), - selected=data["DT"].unique().tolist(), - multiple=True - ) - - ui.input_slider( - "average_citations_slider", - "Select Average Citations per Year Range", - sep="", - ticks=True, - min=data["Min_Citations"][0], - max=data["Max_Citations"][0], - value=(data["Min_Citations"][0], data["Max_Citations"][0]), - step=0.1 - ) - ui.input_select( - "bradford", - "Source by Bradford Law Zones:", - {"Z1": "🥇 Core Sources", "Z2": "🥈 Core + Zone 2 Sources", "Z3": "🥉 All Sources"}, - selected="Z3" - ) - - @render.express() - def mostra_info(): - new_data = df_filtered.get() - if new_data is None: - new_data = data - ui.markdown( - f""" - 📄 **Documents**: {len(new_data)} of {len(data)}
- 📚 **Sources**: {new_data['SO'].nunique()} of {data['SO'].nunique()}
- 👥 **Authors**: {new_data['AU'].explode().nunique()} of {data['AU'].explode().nunique()}
- """ - ) - - ui.input_action_button( - "filter_button", - "Apply Filters", - width="200px", - icon=ICONS["play"], - ) - - @render.ui - @reactive.event(input.filter_button) - def filtra_tabella(): - database = get_database(input) - return get_filtered_table(input, database, df_filters, df_filtered) - - ui.h4("Description", style="color: #5567BB;") - ui.p("This section allows you to apply filters to the dataset. You can select a range of years, languages, document types, and average citations per year. Additionally, you can filter sources based on Bradford's Law zones. The filtered data will be displayed in a table format, showing the number of documents, sources, and authors after applying the selected filters.") - - # --- Main Information Section --- - with ui.nav_panel("None", value="overview"): - df_informations = reactive.Value(None) - - with ui.layout_columns( - col_widths=(9, 3), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("📈 Main Information", style="color: #5567BB;") - ui.p("The main information of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px;"): - ui.input_action_button("main_information_report", "Add in Report", icon=ICONS["plus"]) - - @render.ui - @reactive.event(input.main_information_report) - def show_main_information_report(): - data = df_informations.get() - df_box = pd.DataFrame({ - "Metric": [ - "Timespan", - "Sources", - "Documents", - "Annual Growth Rate", - "Authors", - "Authors of single-authored docs", - "International Co-Authorship", - "Co-Authors per Doc", - "Author's Keywords (DE)", - "References", - "Document Average Age", - "Average citations per doc" - ], - "Value": [ - f"{data['Min_Year'][0]} - {data['Max_Year'][0]}", - data['SO'].nunique(), - len(data), - data['CAGR'][0], - data['AU'].explode().nunique(), - data['Authors_of_single_authored_docs'][0], - data['International_Co_Authorship'][0], - data['Co_Authors_per_Doc'][0], - data['Authors_Keywords_DE'][0], - data['References_per_Doc'][0], - data['Document_Average_Age'][0], - data['Average_Citations_per_Doc'][0] - ] - }) - report_excel.set(add_to_report(report_choices, report_excel, [df_box], [], "maininfo")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Main Information added to report", duration=5, close_button=False) - - @reactive.calc - def main_informations(): - # Show a modal while loading - def loading_modal(): - # --- Nice loading modal with rotating phrases --- - phrases = [ - "⏳ Loading... Please wait.", - "🔍 Crunching bibliometric numbers...", - "📚 Counting references and citations...", - "🧠 Mapping science, one paper at a time...", - "✨ Almost there! Preparing your dashboard...", - # "🤖 Summoning BiblioAI for insights...", - "📈 Calculating main indicators...", - "🔗 Connecting authors and sources...", - "🌐 Exploring your scientific landscape...", - "🕵️‍♂️ Detecting trends and patterns...", - "🚀 Science mapping in progress...", - ] - # The modal content will have a span with id="loading-phrase" - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - # Inject JS to rotate phrases every 1s - js = f""" - - """ - return ui.HTML(str(modal) + js) - ui.modal_show(loading_modal()) - try: - result = get_main_informations(df) - return result - finally: - ui.modal_remove() - - with ui.card(full_screen=True, fill=True): - @render.express() - def show_informations(): - data = main_informations() - temp = df_informations.set(data) - with ui.navset_underline(id="tab"): - with ui.nav_panel("Box"): - ui.p("") #spazio - with ui.layout_column_wrap(width=1 / 4): - with ui.value_box(showcase=ICONS["timespan"], theme="bg-gradient-blue-purple"): - "Timespan" - ui.h2( - f"{data['Min_Year'][0]} - {data['Max_Year'][0]}" - ) - with ui.value_box(showcase=ICONS["sources"], theme="bg-gradient-blue-purple"): - "Sources" - ui.h2( - f"{data['Unique_SO'][0]}" - ) - with ui.value_box(showcase=ICONS["documents"], theme="bg-gradient-blue-purple"): - "Documents" - ui.h2( - f"{len(data)}" - ) - with ui.value_box(showcase=ICONS["annual_growth_rate"], theme="bg-gradient-blue-purple"): - "Annual Growth Rate" - ui.h2( - f"{data['CAGR'][0]} %" - ) - with ui.layout_column_wrap(width=1 / 4): - with ui.value_box(showcase=ICONS["authors"], theme="bg-gradient-blue-purple"): - "Authors" - ui.h2( - f"{data['Unique_AU'][0]}" - ) - with ui.value_box(showcase=ICONS["authors_single_authored_docs"], theme="bg-gradient-blue-purple"): - "Authors of single-authored docs" - ui.h2( - f"{data['Authors_of_single_authored_docs'][0]}" - ) - with ui.value_box(showcase=ICONS["international_co_authorship"], theme="bg-gradient-blue-purple"): - "International Co-Authorship" - ui.h2( - f"{data['International_Co_Authorship'][0]} %" - ) - with ui.value_box(showcase=ICONS["co_authors_per_doc"], theme="bg-gradient-blue-purple"): - "Co-Authors per Doc" - ui.h2( - f"{data['Co_Authors_per_Doc'][0]}" - ) - with ui.layout_column_wrap(width=1 / 4): - with ui.value_box(showcase=ICONS["authors_keywords_de"], theme="bg-gradient-blue-purple"): - "Author's Keywords (DE)" - ui.h2( - f"{data['Authors_Keywords_DE'][0]}" - ) - with ui.value_box(showcase=ICONS["references"], theme="bg-gradient-blue-purple"): - "References" - ui.h2( - f"{data['References_per_Doc'][0]}" - ) - - with ui.value_box(showcase=ICONS["document_average_age"], theme="bg-gradient-blue-purple"): - "Document Average Age" - ui.h2( - f"{data['Document_Average_Age'][0]}" - ) - with ui.value_box(showcase=ICONS["average_citations_per_doc"], theme="bg-gradient-blue-purple"): - "Average citations per doc" - ui.h2( - f"{data['Average_Citations_per_Doc'][0]}" - ) - - with ui.nav_panel("Table"): - ui.p("") - - @render.ui - def table_informations(): - data = df_informations.get() - df_box = pd.DataFrame({ - "Metric": [ - "Timespan", - "Sources", - "Documents", - "Annual Growth Rate", - "Authors", - "Authors of single-authored docs", - "International Co-Authorship", - "Co-Authors per Doc", - "Author's Keywords (DE)", - "References", - "Document Average Age", - "Average citations per doc" - ], - "Value": [ - f"{data['Min_Year'][0]} - {data['Max_Year'][0]}", - data['SO'].nunique(), - len(data), - data['CAGR'][0], - data['AU'].explode().nunique(), - data['Authors_of_single_authored_docs'][0], - data['International_Co_Authorship'][0], - data['Co_Authors_per_Doc'][0], - data['Authors_Keywords_DE'][0], - data['References_per_Doc'][0], - data['Document_Average_Age'][0], - data['Average_Citations_per_Doc'][0] - ] - }) - return ui.HTML(DT(df_box, style="width=100%;")) - - # --- Annual Scientific Production Section --- - with ui.nav_panel("None", value="annual_scientific_production"): - with ui.layout_columns( - col_widths=(9, 3), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("📈 Annual Scientific Production", style="color: #5567BB;") - ui.p("The annual scientific production of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px;"): - ui.input_action_button("annual_production_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"AnnualScientificProduction-{todaydate}.png" - ) - def download_annual_production(): - plot_annual_production, _ = annual_informations() - yield plotly_download( - plot_annual_production, - title="Annual Scientific Production", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.annual_production_report) - def show_annual_production_report(): - plots, publications_per_year = annual_informations() - report_excel.set(add_to_report(report_choices, report_excel, [publications_per_year], [plots], "annualsciprod")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Annual Scientific Production added to report", duration=5, close_button=False) - - with ui.card(full_screen=True): - @reactive.calc - def annual_informations(): - return get_annual_production(df) - - with ui.navset_underline(id="annual_tab"): - with ui.nav_panel("Plot"): - @render_widget - def show_annual_production(): - plot_annual_production, publications_per_year = annual_informations() - return plot_annual_production - - with ui.nav_panel("Table"): - @render.ui - def table_annual_production(): - _, publications_per_year = annual_informations() - return ui.HTML(DT(publications_per_year, style="width=100%;")) - - # AI bot Gemini Chat Integration - # --- Floating Chat Button --- - @render.express() - @reactive.event(input.go_annual_scientific_production) - def floating_chat_button(): - # Floating chat button: the image itself is the button (no extra div, no background) - ui.tags.img( - src="https://i.ibb.co/hRDpGMqS/logoAI.png", - style=( - "position: fixed; bottom: 30px; right: 30px; z-index: 9999; " - "height: 60px; cursor: pointer; background: none;" - ), - alt="Bibliometrix", - onclick="document.getElementById('chat-modal').style.display = (document.getElementById('chat-modal').style.display === 'block' ? 'none' : 'block');" - ) - - # --- Chat Modal (hidden by default) --- - with ui.tags.div( - id="chat-modal", - style=( - "display: none; position: fixed; bottom: 100px; right: 40px; z-index: 10000;" - "background: white; border-radius: 16px 16px 16px 16px; box-shadow: 0 4px 24px rgba(0,0,0,0.25);" - "width: 400px; max-width: 90vw; max-height: 70vh; overflow-x: hidden; overflow-y: auto;" - ) - ): - with ui.tags.div(style="position: sticky; top: 0; z-index: 10; min-height: 48px; display: flex; justify-content: flex-end; align-items: center; background: #5567BB; border-radius: 16px 16px 0 0;"): - ui.tags.span( - "BiblioAI ✨", - style=( - "flex:1; color: white; font-weight: bold; font-size: 1.1rem; padding-left: 16px;" - ) - ) - ui.tags.button( - "✖", - style=( - "background: none; border: none; color: white; font-size: 1.5rem; cursor: pointer; padding: 8px 16px;" - ), - onclick="document.getElementById('chat-modal').style.display = 'none';" - ) - # --- Chat UI --- - chat = ui.Chat(id="chat") - chat.ui(messages=["Welcome! Ask about the current plot or table."], icon_assistant="✨", style="padding: 10px") - - # You should set your Gemini API key as an environment variable or config - GEMINI_API_KEY = gemini_api_key.get() - if GEMINI_API_KEY: - client = genai.Client(api_key=GEMINI_API_KEY) - else: - client = None - - # Helper: get current plot/table context (simplified, you may want to expand this) - def get_current_context(): - # This function should extract the current plot/table context - plot_annual_production, tab_annual_production = annual_informations() - return { - "panel": input.hidden_tabs(), - "plot": plot_annual_production, - "table": tab_annual_production, - } - - # Gemini Chat Integration - handle chat messages - @chat.on_user_submit - async def handle_user_input(user_input: str): - if not user_input: - return - context = get_current_context() - user_question = user_input - - # Check if analysis has been run - # No need to check since we're using @reactive.calc now - - # Compose prompt for Gemini - prompt = ( - f"You are an expert assistant for a bibliometric analysis dashboard. " - f"The user is currently viewing the '{context['panel']}' section. " - f"Provide helpful, concise information about the plot and table shown in this section. " - f"If the user asks about the plot or table, answer based on the context. " - f"User question: {user_question}" - f"Table: {context['table'].to_json()} " - ) - if client is not None: - try: - response = client.models.generate_content( - model="gemini-2.0-flash", - contents=[ - types.Part.from_bytes( - data=context['plot'].to_image(format="png", scale=1), - mime_type='image/png', - ), - prompt - ] - ) - answer = response.text - except Exception: - answer = "Sorry, the AI service is currently unavailable." - else: - answer = "Gemini API key not configured. Please set GEMINI_API_KEY in Settings section." - - await chat.append_message(answer) - - # --- Average Citations per Year Section --- - with ui.nav_panel("None", value="average_citations_per_year"): - with ui.layout_columns( - col_widths=(9, 3), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("❞ Average Citations per Year", style="color: #5567BB;") - ui.p("The average citations per year of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px;"): - ui.input_action_button("average_citations_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"AverageCitations-{todaydate}.png" - ) - def download_average_citations(): - plot_average_citations, _ = average_citations() - yield plotly_download( - plot_average_citations, - title="Average Citations per Year", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.average_citations_report) - def show_average_citations_report(): - plots, publications = average_citations() - report_excel.set(add_to_report(report_choices, report_excel, [publications], [plots], "averagecitperyear")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Average Citations per Year added to report", duration=5, close_button=False) - - with ui.card(full_screen=True): - @reactive.calc - def average_citations(): - return get_average_citations(df) - - with ui.navset_underline(id="average_tab"): - with ui.nav_panel("Plot"): - @render_widget - def show_average_citations(): - plot_average_citations, avg_citations = average_citations() - return plot_average_citations - - with ui.nav_panel("Table"): - @render.ui - def table_average_citations(): - _, avg_citations = average_citations() - return ui.HTML(DT(avg_citations, style="width=100%;")) - - # --- Three-Field Plot Section --- - with ui.nav_panel("None", value="three_field_plot"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("🪢 Three-Field Plot", style="color: #5567BB;") - ui.p("The three-field plot of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_three_field_plot", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("three_field_plot_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"ThreeFieldPlot-{todaydate}.png" - ) - def download_three_field_plot(): - result = three_field_plot_results.get() - if result is None: - yield b"" - return - yield plotly_download( - result, - title="Three-Field Plot", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.three_field_plot_report) - def show_three_field_plot_report(): - result = three_field_plot_results.get() - if result is None: - return ui.notification_show("⚠️ Please run the analysis first by clicking the Run Analysis button", duration=5, close_button=False, type="warning") - report_excel.set(add_to_report(report_choices, report_excel, [], [result], "threefieldsplot")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Three-Field Plot added to report", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=True): - # Sidebar for data import options - with ui.sidebar(id="sidebar_three_field", position="right", ): - field_options = {"AU": "Authors", "CR": "References", "DE": "Keywords", "SO": "Sources", "CR_SO": "Cited Sources", "AU_UN": "Affiliations", "AU_CO": "Countries", "ID": "Keywords Plus", "TI_TM": "Titles", "AB_TM": "Abstract", "CR_SO": "Cited Sources"} - # if database == "Dimensions" or database == "PubMed" or database == "Cochrane" or database == "Lens": - # del field_options["CR"] - # del field_options["CR_SO"] - - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the three-field plot.") - with ui.accordion(id="acc_tfp", multiple=True, open=False): - with ui.accordion_panel("Middle Field"): - with ui.layout_column_wrap(width=1 / 2): - ui.input_select("middle_field", "Field:", field_options, selected=list(field_options.keys())[0]) - ui.input_numeric("middle_field_items", "Items:", value=20) - with ui.accordion_panel("Left Field"): - with ui.layout_column_wrap(width=1 / 2): - ui.input_select("left_field", "Field:", field_options, selected=list(field_options.keys())[1]) - ui.input_numeric("left_field_items", "Items:", value=20) - with ui.accordion_panel("Right Field"): - with ui.layout_column_wrap(width=1 / 2): - ui.input_select("right_field", "Field:", field_options, selected=list(field_options.keys())[2]) - ui.input_numeric("right_field_items", "Items:", value=20) - - # Store the three field plot results - three_field_plot_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_three_field_plot) - def calculate_three_field_plot(): - # Use optimized loading modal - ui.modal_show(create_loading_modal("three-field plot")) - try: - left_field = input.left_field() - middle_field = input.middle_field() - right_field = input.right_field() - left_field_items = input.left_field_items() - middle_field_items = input.middle_field_items() - right_field_items = input.right_field_items() - - result = get_three_field_plot(df, left_field, middle_field, right_field, left_field_items, middle_field_items, right_field_items) - three_field_plot_results.set(result) - finally: - ui.modal_remove() - - @render.ui - def show_three_field_plot_placeholder(): - result = three_field_plot_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the three-field plot.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; align-items: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - return None - - @render_widget - def show_three_field_plot_widget(): - result = three_field_plot_results.get() - if result is None: - return None - return result - - ui.h4("Description", style="color: #5567BB;") - ui.p("Visualize the main items of three fields (e.g. authors, keywords, journals), and how they are related through a Sankey diagram.") - ui.p("Reference: ", style="font-weight: bold; font-size: 12px;") - ui.p("Cobo, M. J., Lopez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146-166.", style="font-style: italic; font-color: #cccc; font-size: 10px;") - - # --- Most Relevant Sources Section --- - with ui.nav_panel("None", value="most_relevant_sources"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("📖 Most Relevant Sources", style="color: #5567BB;") - ui.p("The most relevant sources of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_relevant_sources", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("most_relevant_sources_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"MostRelevantSources-{todaydate}.png" - ) - def download_most_relevant_sources(): - result = relevant_sources_results.get() - if result is None: - yield b"" - return - plot_most_relevant_sources, _ = result - yield plotly_download( - plot_most_relevant_sources, - title="Most Relevant Sources", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.most_relevant_sources_report) - def show_most_relevant_sources_report(): - result = relevant_sources_results.get() - if result is None: - return ui.notification_show("⚠️ Please run the analysis first by clicking the Run Analysis button", duration=5, close_button=False, type="warning") - plots, relevant_sources_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [relevant_sources_tab], [plots], "mostrelsources")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Most Relevant Sources added to report", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_most_relevant_sources", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the most relevant sources.") - ui.input_numeric("num_of_sources", "Select the numbers of sources to apply to the dataset:", value=10) - - # Store the relevant sources results - relevant_sources_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_relevant_sources) - def calculate_relevant_sources(): - # Show loading modal while calculating - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "📖 Processing source data...", - "📊 Ranking sources by relevance...", - "🔍 Computing source metrics...", - "✨ Almost there! Preparing your dashboard...", - "📈 Calculating source indicators...", - "🔗 Connecting sources and data...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - num_of_sources = input.num_of_sources() - result = get_relevant_sources(df, num_of_sources) - relevant_sources_results.set(result) - finally: - ui.modal_remove() - - with ui.navset_underline(id="relevant_sources_tab"): - with ui.nav_panel("Plot"): - @render.ui - def show_relevant_sources_placeholder(): - result = relevant_sources_results.get() - if result is None: - return ui.div( - ui.p("Set the number of sources in the sidebar and click the Run Analysis button.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - return None - - @render_widget - def show_relevant_sources_widget(): - result = relevant_sources_results.get() - if result is None: - return None - plot_relevant_sources, _ = result - return plot_relevant_sources - - with ui.nav_panel("Table"): - @render.ui - def table_relevant_sources(): - result = relevant_sources_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the relevant sources data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, relevant_sources_tab = result - return ui.HTML(DT(relevant_sources_tab, style="width=100%;")) - - # --- Most Local Cited Sources Section --- - with ui.nav_panel("None", value="most_local_cited_sources"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("📖 Most Local Cited Sources", style="color: #5567BB;") - ui.p("The most local cited sources of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_local_cited_sources", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("most_local_cited_sources_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"MostLocalCitedSources-{todaydate}.png" - ) - def download_most_local_cited_sources(): - result = local_cited_sources_results.get() - if result is None: - yield b"" - return - plot_most_local_cited_sources, _ = result - yield plotly_download( - plot_most_local_cited_sources, - title="Most Local Cited Sources", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.most_local_cited_sources_report) - def show_most_local_cited_sources_report(): - result = local_cited_sources_results.get() - if result is None: - return ui.notification_show("⚠️ Please run the analysis first by clicking the Run Analysis button", duration=5, close_button=False, type="warning") - plots, local_cited_sources_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [local_cited_sources_tab], [plots], "mostloccitsources")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Most Local Cited Sources added to report", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_most_local_cited_sources", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the most local cited sources.") - ui.input_numeric("num_of_cited_sources", "Select the numbers of sources to apply to the dataset:", value=10) - - # Store the local cited sources results - local_cited_sources_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_local_cited_sources) - def calculate_local_cited_sources(): - # Show loading modal while calculating - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "📖 Processing local citation data...", - "📊 Ranking locally cited sources...", - "🔍 Computing citation metrics...", - "✨ Almost there! Preparing your dashboard...", - "📈 Calculating citation indicators...", - "🔗 Connecting citations and sources...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - num_of_cited_sources = input.num_of_cited_sources() - result = get_local_cited_sources(df, num_of_cited_sources) - local_cited_sources_results.set(result) - finally: - ui.modal_remove() - - with ui.navset_underline(id="local_cited_sources_tab"): - with ui.nav_panel("Plot"): - @render.ui - def show_local_cited_sources_placeholder(): - result = local_cited_sources_results.get() - if result is None: - return ui.div( - ui.p("Set the number of sources in the sidebar and click the Run Analysis button.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - return None - - @render_widget - def show_local_cited_sources_widget(): - result = local_cited_sources_results.get() - if result is None: - return None - plot_local_cited_sources, _ = result - return plot_local_cited_sources - - with ui.nav_panel("Table"): - @render.ui - def table_local_cited_sources(): - result = local_cited_sources_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the local cited sources data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, local_cited_sources_tab = result - return ui.HTML(DT(local_cited_sources_tab, style="width=100%;")) - - # --- Bradford's Law Section --- - with ui.nav_panel("None", value="bradfords_law"): - with ui.layout_columns( - col_widths=(9, 3), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("📖 Bradford's Law", style="color: #5567BB;") - ui.p("The application of Bradford's Law to the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px;"): - ui.input_action_button("bradfords_law_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"BradfordsLaw-{todaydate}.png" - ) - def download_bradfords_law(): - plot_bradfords_law, _ = bradford_law() - yield plotly_download( - plot_bradfords_law, - title="Bradford's Law", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.bradfords_law_report) - def show_bradfords_law_report(): - plots, bradford_law_tab = bradford_law() - report_excel.set(add_to_report(report_choices, report_excel, [bradford_law_tab], [plots], "bradfordlaw")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Bradford's Law added to report", duration=5, close_button=False) - - with ui.card(full_screen=True): - @reactive.calc - def bradford_law(): - return get_bradford_law(df) - - with ui.navset_underline(id="bradford_law_tab"): - with ui.nav_panel("Plot"): - @render_widget - def show_bradford_law(): - plot_bradford_law, _ = bradford_law() - return plot_bradford_law - - with ui.nav_panel("Table"): - @render.ui - def table_bradford_law(): - _, bradford_law_tab = bradford_law() - return ui.HTML(DT(bradford_law_tab, style="width=100%;")) - - # --- Sources' Local Impact Section --- - with ui.nav_panel("None", value="sources_local_impact"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("📖 Sources' Local Impact", style="color: #5567BB;") - ui.p("The sources' local impact of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_sources_local_impact", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("sources_local_impact_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"SourcesLocalImpact-{todaydate}.png" - ) - def download_sources_local_impact(): - result = sources_local_impact_results.get() - if result is None: - yield b"" - return - plot_sources_local_impact, _ = result - yield plotly_download( - plot_sources_local_impact, - title="Sources' Local Impact", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.sources_local_impact_report) - def show_sources_local_impact_report(): - result = sources_local_impact_results.get() - if result is None: - return ui.notification_show("⚠️ Please run the analysis first by clicking the Run Analysis button", duration=5, close_button=False, type="warning") - plots, sources_local_impact_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [sources_local_impact_tab], [plots], "sourcelocimpact")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Sources' Local Impact added to report", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_sources_local_impact", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the sources' local impact.") - ui.input_numeric("num_of_sources_local_impact", "Select the numbers of sources to apply to the dataset:", value=10) - ui.input_select("source_local_impact", "Impact measure", {"h_index": "H-Index", "g_index": "G-Index", "m_index": "M-index", "total_cit": "Total Citation"}, selected="h_index") - - # Store the sources local impact results - sources_local_impact_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_sources_local_impact) - def calculate_sources_local_impact(): - # Show loading modal while calculating - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "📖 Processing impact metrics...", - "📊 Computing local impact scores...", - "🔍 Ranking sources by impact...", - "✨ Almost there! Preparing your dashboard...", - "📈 Calculating impact indicators...", - "🔗 Connecting sources and impact data...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - num_of_sources_local_impact = input.num_of_sources_local_impact() - source_local_impact = input.source_local_impact() - result = get_sources_local_impact(df, num_of_sources_local_impact, source_local_impact) - sources_local_impact_results.set(result) - finally: - ui.modal_remove() - - with ui.navset_underline(id="sources_local_impact_tab"): - with ui.nav_panel("Plot"): - @render.ui - def show_sources_local_impact_placeholder(): - result = sources_local_impact_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the Sources' Local Impact analysis.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - return None - - @render_widget - def show_sources_local_impact_widget(): - result = sources_local_impact_results.get() - if result is None: - return None - plot_sources_local_impact, _ = result - return plot_sources_local_impact - - with ui.nav_panel("Table"): - @render.ui - def table_sources_local_impact(): - result = sources_local_impact_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the sources' local impact data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, sources_local_impact_tab = result - return ui.HTML(DT(sources_local_impact_tab, style="width=100%;")) - - # --- Sources' Production --- - with ui.nav_panel("None", value="sources_production"): - sources_production_result = reactive.value(None) - - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("📖 Sources' Production over Time", style="color: #5567BB;") - ui.p("The sources' production over time of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_sources_production", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("sources_production_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"SourcesProduction-{todaydate}.png" - ) - def download_sources_production(): - result = sources_production_result.get() - if result is None: - return None - plot_sources_production, _ = result - yield plotly_download( - plot_sources_production, - title="Sources' Production over Time", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.sources_production_report) - def show_sources_production_report(): - result = sources_production_result.get() - if result is None: - return ui.notification_show("⚠️ Please run the analysis first", duration=5, type="warning") - plots, sources_production_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [sources_production_tab], [plots], "sourceprodovertime")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Sources' Production over Time added to report", duration=5, close_button=False) - - @reactive.effect - @reactive.event(input.run_sources_production) - def run_sources_production_analysis(): - # Show loading modal while calculating (same style as Main Information) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "📊 Processing temporal data...", - "📈 Calculating production metrics...", - "🔍 Generating time series analysis...", - "✨ Almost there! Preparing your dashboard...", - "📈 Calculating production indicators...", - "🔗 Connecting sources and time data...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - num_of_sources_production = input.num_of_sources_production() - occurences = input.occurences() - result = get_sources_production(df, num_of_sources_production, occurences) - sources_production_result.set(result) - finally: - ui.modal_remove() - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_sources_production", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the sources' production over time.") - ui.input_select("occurences", "Select the number of occurrences to apply to the dataset:", {"cumulative": "Cumulate", "year": "Per Year"}, selected="cum") - ui.input_slider("num_of_sources_production", "Select the numbers of sources to apply to the dataset:", sep="", ticks=True, min=1, max=50, value=5, step=1) - - with ui.navset_underline(id="sources_production_tab"): - with ui.nav_panel("Plot"): - @render.ui - def sources_production_placeholder(): - result = sources_production_result.get() - if result is None: - return ui.tags.div( - ui.p("Click the Run Analysis button to generate the sources' production over time visualization.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; align-items: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - # Render the widget directly when result is available - plot_sources_production, _ = result - return plot_sources_production - - @render_widget - def show_sources_production(): - result = sources_production_result.get() - if result is None: - return None - plot_sources_production, _ = result - return plot_sources_production - - with ui.nav_panel("Table"): - @render.ui - def table_sources_production(): - result = sources_production_result.get() - if result is None: - return ui.tags.div( - ui.p("Click the Run Analysis button to generate the sources' production data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, sources_production_tab = result - return ui.HTML(DT(sources_production_tab, style="width=100%;")) - - # --- Most Relevant Authors Section --- - with ui.nav_panel("None", value="most_relevant_authors"): - relevant_authors_result = reactive.value(None) - - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("👤 Most Relevant Authors", style="color: #5567BB;") - ui.p("The most relevant authors of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_most_relevant_authors", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("most_relevant_authors_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"MostRelevantAuthors-{todaydate}.png" - ) - def download_most_relevant_authors(): - result = relevant_authors_result.get() - if result is None: - return None - plot_most_relevant_authors, _ = result - yield plotly_download( - plot_most_relevant_authors, - title="Most Relevant Authors", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.most_relevant_authors_report) - def show_most_relevant_authors_report(): - result = relevant_authors_result.get() - if result is None: - return ui.notification_show("⚠️ Please run the analysis first", duration=5, type="warning") - plots, relevant_authors_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [relevant_authors_tab], [plots], "mostrelauthors")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Most Relevant Authors added to report", duration=5, close_button=False) - - @reactive.effect - @reactive.event(input.run_most_relevant_authors) - def run_relevant_authors_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "🔍 Analyzing author relevance...", - "📊 Processing author data...", - "📈 Calculating relevance metrics...", - "⏳ Ranking most relevant authors...", - "✨ Almost there! Preparing your dashboard...", - "🎯 Finalizing author visualization...", - "🔗 Connecting authors and impact data...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - num_of_authors = input.num_of_authors() - frequency = input.frequency() - result = get_relevant_authors(df, num_of_authors, frequency) - relevant_authors_result.set(result) - finally: - ui.modal_remove() - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_most_relevant_authors", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the most relevant authors.") - ui.input_numeric("num_of_authors", "Select the number of authors to apply to the dataset:", value=10) - ui.input_select("frequency", "Frequency measure", {"n_docs": "N. of Documents", "percentage": "Percentage", "freq_measure": "Fractionalized Frequency"}, selected="n_docs") - - with ui.navset_underline(id="most_relevant_authors_tab"): - with ui.nav_panel("Plot"): - @render.ui - def relevant_authors_placeholder(): - result = relevant_authors_result.get() - if result is None: - return ui.tags.div( - ui.p("Click the Run Analysis button to generate the most relevant authors visualization.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; align-items: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - # Render the widget directly when result is available - plot_relevant_authors, _ = result - return plot_relevant_authors - - @render_widget - def show_relevant_authors(): - result = relevant_authors_result.get() - if result is None: - return None - plot_relevant_authors, _ = result - return plot_relevant_authors - - with ui.nav_panel("Table"): - @render.ui - def table_relevant_authors(): - result = relevant_authors_result.get() - if result is None: - return ui.tags.div( - ui.p("Click the Run Analysis button to generate the most relevant authors data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, relevant_authors_tab = result - return ui.HTML(DT(relevant_authors_tab, style="width=100%;")) - - # --- Most Local Cited Authors Section --- - with ui.nav_panel("None", value="most_local_cited_authors"): - local_cited_authors_result = reactive.value(None) - - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("👤 Most Local Cited Authors", style="color: #5567BB;") - ui.p("The most local cited authors of the dataset") - - # --- FIX: move the buttons to the correct column for alignment --- - with ui.tags.div( - style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;" - ): - ui.input_action_button("run_most_local_cited_authors", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("most_local_cited_authors_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"MostLocalCitedAuthors-{todaydate}.png" - ) - def download_most_local_cited_authors(): - result = local_cited_authors_result.get() - if result is None: - return None - plot_most_local_cited_authors, _ = result - yield plotly_download( - plot_most_local_cited_authors, - title="Most Local Cited Authors", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.most_local_cited_authors_report) - def show_most_local_cited_authors_report(): - result = local_cited_authors_result.get() - if result is None: - return ui.notification_show("⚠️ Please run the analysis first", duration=5, type="warning") - plots, local_cited_authors_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [local_cited_authors_tab], [plots], "mostloccitauthors")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Most Local Cited Authors added to report", duration=5, close_button=False) - - @reactive.effect - @reactive.event(input.run_most_local_cited_authors) - def run_local_cited_authors_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "🔍 Analyzing local cited authors...", - "📊 Processing citation data...", - "📈 Calculating local citation metrics...", - "⏳ Ranking most cited authors...", - "✨ Almost there! Preparing your dashboard...", - "🎯 Finalizing local citations visualization...", - "🔗 Connecting citation and author data...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - num_of_cited_authors = input.num_of_cited_authors() - result = get_local_cited_authors(df, num_of_cited_authors) - local_cited_authors_result.set(result) - finally: - ui.modal_remove() - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_most_local_cited_authors", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the most local cited authors.") - ui.input_numeric("num_of_cited_authors", "Select the number of authors to apply to the dataset:", value=10) - - with ui.navset_underline(id="most_local_cited_authors_tab"): - with ui.nav_panel("Plot"): - @render.ui - def local_cited_authors_placeholder(): - result = local_cited_authors_result.get() - if result is None: - return ui.tags.div( - ui.p("Click the Run Analysis button to generate the most local cited authors visualization.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; align-items: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - # Render the widget directly when result is available - plot_local_cited_authors, _ = result - return plot_local_cited_authors - - @render_widget - def show_local_cited_authors(): - result = local_cited_authors_result.get() - if result is None: - return None - plot_local_cited_authors, _ = result - return plot_local_cited_authors - - with ui.nav_panel("Table"): - @render.ui - def table_local_cited_authors(): - result = local_cited_authors_result.get() - if result is None: - return ui.tags.div( - ui.p("Click the Run Analysis button to generate the most local cited authors data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, local_cited_authors_tab = result - return ui.HTML(DT(local_cited_authors_tab, style="width=100%;")) - - # --- Authors' Production over Time Section --- - with ui.nav_panel("None", value="authors_production"): - au_over_time_result = reactive.value(None) - - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("👤 Authors' Production over Time", style="color: #5567BB;") - ui.p("The authors' production over time of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_authors_production", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("authors_production_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"AuthorsProduction-{todaydate}.png" - ) - def download_authors_production(): - result = au_over_time_result.get() - if result is None: - return None - plot_authors_production, _, _ = result - yield plotly_download( - plot_authors_production, - title="Authors' Production over Time", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.authors_production_report) - def show_authors_production_report(): - result = au_over_time_result.get() - if result is None: - return ui.notification_show("⚠️ Please run the analysis first", duration=5, type="warning") - plots, authors_production_tab, documents_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [authors_production_tab, documents_tab], [plots], "authorprodovertime")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Authors' Production over Time added to report", duration=5, close_button=False) - - @reactive.effect - @reactive.event(input.run_authors_production) - def run_authors_production_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "🔍 Analyzing authors' production patterns...", - "📊 Processing temporal author data...", - "📈 Calculating production metrics over time...", - "⏳ Generating author timeline analysis...", - "✨ Almost there! Preparing your dashboard...", - "🎯 Finalizing authors' production visualization...", - "🔗 Connecting authors and time data...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - top_k_authors = input.TopAuthorsProdK() - result = get_author_production_over_time(df, top_k_authors) - au_over_time_result.set(result) - finally: - ui.modal_remove() - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_authors_production", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the authors' production over time.") - ui.input_numeric("TopAuthorsProdK", "Select the number of top authors to display:", value=10) - - with ui.navset_underline(id="authors_production_tab"): - with ui.nav_panel("Plot"): - @render.ui - def authors_production_placeholder(): - result = au_over_time_result.get() - if result is None: - return ui.tags.div( - ui.p("Click the Run Analysis button to generate the authors' production over time visualization.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; align-items: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - # Render the widget directly when result is available - plot_authors_production, _, _ = result - return plot_authors_production - - @render_widget - def show_authors_production(): - result = au_over_time_result.get() - if result is None: - return None - plot_authors_production, _, _ = result - return plot_authors_production - - with ui.nav_panel("Table - Production per year"): - @render.ui - def table_authors_production(): - result = au_over_time_result.get() - if result is None: - return ui.tags.div( - ui.p("Click the Run Analysis button to generate the authors' production per year data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, table_authors_production, _ = result - return ui.HTML(DT(table_authors_production, style="width=100%;")) - - with ui.nav_panel("Table - Documents"): - @render.ui - def table_documents(): - result = au_over_time_result.get() - if result is None: - return ui.tags.div( - ui.p("Click the Run Analysis button to generate the authors' documents data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, _, table_documents = result - if "DOI" in table_documents.columns: - # Gestione dei link DOI - table_documents['DOI'] = table_documents['DOI'].fillna("N/A") - table_documents['DOI'] = table_documents['DOI'].apply( - lambda x: f'{x}' if x != "N/A" else x - ) - return ui.HTML(DT(table_documents, style="width=100%;")) - # AI bot Gemini Chat Integration - # --- Floating Chat Button --- - @render.express() - @reactive.event(input.go_authors_production_over_time) - def floating_chat_button_authors_production(): - # Floating chat button: the image itself is the button (no extra div, no background) - ui.tags.img( - src="https://i.ibb.co/hRDpGMqS/logoAI.png", - style=( - "position: fixed; bottom: 30px; right: 30px; z-index: 9999; " - "height: 60px; cursor: pointer; background: none;" - ), - alt="Bibliometrix", - onclick="document.getElementById('chat-modal').style.display = (document.getElementById('chat-modal').style.display === 'block' ? 'none' : 'block');" - ) - - # --- Chat Modal (hidden by default) --- - with ui.tags.div( - id="chat-modal", - style=( - "display: none; position: fixed; bottom: 100px; right: 40px; z-index: 10000;" - "background: white; border-radius: 16px 16px 16px 16px; box-shadow: 0 4px 24px rgba(0,0,0,0.25);" - "width: 400px; max-width: 90vw; max-height: 70vh; overflow-x: hidden; overflow-y: auto;" - ) - ): - with ui.tags.div(style="position: sticky; top: 0; z-index: 10; min-height: 48px; display: flex; justify-content: flex-end; align-items: center; background: #5567BB; border-radius: 16px 16px 0 0;"): - ui.tags.span( - "BiblioAI ✨", - style=( - "flex:1; color: white; font-weight: bold; font-size: 1.1rem; padding-left: 16px;" - ) - ) - ui.tags.button( - "✖", - style=( - "background: none; border: none; color: white; font-size: 1.5rem; cursor: pointer; padding: 8px 16px;" - ), - onclick="document.getElementById('chat-modal').style.display = 'none';" - ) - # --- Chat UI --- - chat = ui.Chat(id="chat_authors_production") - chat.ui(messages=["Welcome! Ask about the current plot or table."], icon_assistant="✨", style="padding: 10px") - - # You should set your Gemini API key as an environment variable or config - GEMINI_API_KEY = gemini_api_key.get() - if GEMINI_API_KEY: - client = genai.Client(api_key=GEMINI_API_KEY) - else: - client = None - - # Helper: get current plot/table context (simplified, you may want to expand this) - def get_current_context(): - # This function should extract the current plot/table context - plot_authors_production, table_authors_production, table_documents = au_over_time() - return { - "panel": input.hidden_tabs(), - "plot": plot_authors_production, - "table": [table_authors_production, table_documents], - } - - # Gemini Chat Integration - handle chat messages - @chat.on_user_submit - async def handle_user_input(user_input: str): - if not user_input: - return - context = get_current_context() - user_question = user_input - - # Compose prompt for Gemini - prompt = ( - f"You are an expert assistant for a bibliometric analysis dashboard. " - f"The user is currently viewing the '{context['panel']}' section. " - f"Provide helpful, concise information about the plot and table shown in this section. " - f"If the user asks about the plots or tables, answer based on the context. " - f"User question: {user_question}" - ) - for i, table in enumerate(context['table']): - prompt += f"\nTable {i}: {table.to_json(orient='records')}" - print(f"Prompt for Gemini: {prompt}") - if client is not None: - try: - response = client.models.generate_content( - model="gemini-2.0-flash", - contents=[ - types.Part.from_bytes( - data=context['plot'].to_image(format="png", scale=1), - mime_type='image/png', - ), - prompt - ] - ) - answer = response.text - except Exception: - answer = "Sorry, the AI service is currently unavailable." - else: - answer = "Gemini API key not configured. Please set GEMINI_API_KEY in Settings section." - - await chat.append_message(answer) - - # --- Authors' Productivity through Lotka's Law Section --- - with ui.nav_panel("None", value="lotka_law"): - with ui.layout_columns( - col_widths=(9, 3), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("👤 Author Productivity through Lotka's Law", style="color: #5567BB;") - ui.p("The authors' productivity through Lotka's Law of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px;"): - ui.input_action_button("lotkas_law_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"LotkasLaw-{todaydate}.png" - ) - def download_lotkas_law(): - plot_lotkas_law, _ = lotka_law() - yield plotly_download( - plot_lotkas_law, - title="Authors' Productivity through Lotka's Law", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.lotkas_law_report) - def show_lotkas_law_report(): - plots, lotkas_law_tab = lotka_law() - report_excel.set(add_to_report(report_choices, report_excel, [lotkas_law_tab], [plots], "lotkalaw")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Authors' Productivity through Lotka's Law added to report", duration=5, close_button=False) - - with ui.card(full_screen=True): - @reactive.calc - def lotka_law(): - return get_lotka_law(df) - - with ui.navset_underline(id="lotka_law_tab"): - with ui.nav_panel("Plot"): - @render_widget - def show_lotka_law(): - plot_lotka_law, _ = lotka_law() - return plot_lotka_law - - with ui.nav_panel("Table"): - @render.ui - def table_lotka_law(): - _, lotka_law_tab = lotka_law() - return ui.HTML(DT(lotka_law_tab, style="width=100%;")) - - # --- Authors' Local Impact Section --- - with ui.nav_panel("None", value="authors_local_impact"): - authors_local_impact_result = reactive.value(None) - - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("👤 Authors' Local Impact", style="color: #5567BB;") - ui.p("The authors' local impact of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_authors_local_impact", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("authors_local_impact_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"AuthorsLocalImpact-{todaydate}.png" - ) - def download_authors_local_impact(): - result = authors_local_impact_result.get() - if result is None: - return None - plot_authors_local_impact, _ = result - yield plotly_download( - plot_authors_local_impact, - title="Authors' Local Impact", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.authors_local_impact_report) - def show_authors_local_impact_report(): - result = authors_local_impact_result.get() - if result is None: - return ui.notification_show("⚠️ Please run the analysis first", duration=5, type="warning") - plots, authors_local_impact_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [authors_local_impact_tab], [plots], "authorlocimpact")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Authors' Local Impact added to report", duration=5, close_button=False) - - @reactive.effect - @reactive.event(input.run_authors_local_impact) - def run_authors_local_impact_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "🔍 Analyzing authors' local impact...", - "📊 Processing impact metrics...", - "📈 Calculating H-index, G-index values...", - "⏳ Computing citation impact scores...", - "✨ Almost there! Preparing your dashboard...", - "🎯 Finalizing authors' impact visualization...", - "🔗 Connecting impact and author data...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - num_of_authors_local_impact = input.num_of_authors_local_impact() - author_local_impact = input.author_local_impact() - result = get_authors_local_impact(df, num_of_authors_local_impact, author_local_impact) - authors_local_impact_result.set(result) - finally: - ui.modal_remove() - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_authors_local_impact", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the authors' local impact.") - ui.input_numeric("num_of_authors_local_impact", "Select the number of authors to apply to the dataset:", value=10) - ui.input_select("author_local_impact", "Impact measure", {"h_index": "H-Index", "g_index": "G-Index", "m_index": "M-index", "total_cit": "Total Citation"}, selected="h_index") - - with ui.navset_underline(id="authors_local_impact_tab"): - with ui.nav_panel("Plot"): - @render.ui - def authors_local_impact_placeholder(): - result = authors_local_impact_result.get() - if result is None: - return ui.tags.div( - ui.p("Click the Run Analysis button to generate the authors' local impact visualization.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; align-items: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - # Render the widget directly when result is available - plot_authors_local_impact, _ = result - return plot_authors_local_impact - - @render_widget - def show_authors_local_impact(): - result = authors_local_impact_result.get() - if result is None: - return None - plot_authors_local_impact, _ = result - return plot_authors_local_impact - - with ui.nav_panel("Table"): - @render.ui - def table_authors_local_impact(): - result = authors_local_impact_result.get() - if result is None: - return ui.tags.div( - ui.p("Click the Run Analysis button to generate the authors' local impact data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, authors_local_impact_tab = result - return ui.HTML(DT(authors_local_impact_tab, style="width=100%;")) - - # --- Most Relevant Affiliations Section --- - with ui.nav_panel("None", value="most_relevant_affiliations"): - relevant_affiliations_result = reactive.value(None) - - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("🏫 Most Relevant Affiliations", style="color: #5567BB;") - ui.p("The most relevant affiliations of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_most_relevant_affiliations", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("most_relevant_affiliations_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"MostRelevantAffiliations-{todaydate}.png" - ) - def download_most_relevant_affiliations(): - result = relevant_affiliations_result.get() - if result is None: - return None - plot_most_relevant_affiliations, _ = result - yield plotly_download( - plot_most_relevant_affiliations, - title="Most Relevant Affiliations", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.most_relevant_affiliations_report) - def show_most_relevant_affiliations_report(): - result = relevant_affiliations_result.get() - if result is None: - return ui.notification_show("⚠️ Please run the analysis first", duration=5, type="warning") - plots, relevant_affiliations_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [relevant_affiliations_tab], [plots], "mostrelaffiliations")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Most Relevant Affiliations added to report", duration=5, close_button=False) - - @reactive.effect - @reactive.event(input.run_most_relevant_affiliations) - def run_relevant_affiliations_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "🔍 Analyzing affiliation relevance...", - "🏫 Processing institutional data...", - "📈 Calculating affiliation metrics...", - "⏳ Ranking most relevant institutions...", - "✨ Almost there! Preparing your dashboard...", - "🎯 Finalizing affiliations visualization...", - "🔗 Connecting institutions and data...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - num_of_affiliations = input.num_of_affiliations() - disambiguation = input.disambiguation() - result = get_relevant_affiliations(df, num_of_affiliations, disambiguation) - relevant_affiliations_result.set(result) - finally: - ui.modal_remove() - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_most_relevant_affiliations", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the most relevant affiliations.") - ui.input_numeric("num_of_affiliations", "Select the number of affiliations to apply to the dataset:", value=10) - ui.input_select("disambiguation", "Affiliation Name Disambiguation", {"yes": "Yes", "no": "No"}, selected="yes") - - with ui.navset_underline(id="most_relevant_affiliations_tab"): - with ui.nav_panel("Plot"): - @render.ui - def relevant_affiliations_placeholder(): - result = relevant_affiliations_result.get() - if result is None: - return ui.tags.div( - ui.p("Click the Run Analysis button to generate the most relevant affiliations visualization.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; align-items: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - # Render the widget directly when result is available - plot_relevant_affiliations, _ = result - return plot_relevant_affiliations - - @render_widget - def show_relevant_affiliations(): - result = relevant_affiliations_result.get() - if result is None: - return None - plot_relevant_affiliations, _ = result - return plot_relevant_affiliations - - with ui.nav_panel("Table"): - @render.ui - def table_relevant_affiliations(): - result = relevant_affiliations_result.get() - if result is None: - return ui.tags.div( - ui.p("Click the Run Analysis button to generate the most relevant affiliations data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, relevant_affiliations_tab = result - return ui.HTML(DT(relevant_affiliations_tab, style="width=100%;")) - - # --- Affiliations' Production over Time Section --- - with ui.nav_panel("None", value="affiliations_production"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("🏫 Affiliations' Production over Time", style="color: #5567BB;") - ui.p("The affiliations' production over time of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_affiliations_production", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("affiliations_production_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"AffiliationsProduction-{todaydate}.png" - ) - def download_affiliations_production(): - plot_affiliations_production, _ = aff_over_time() - yield plotly_download( - plot_affiliations_production, - title="Affiliations' Production over Time", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.affiliations_production_report) - def show_affiliations_production_report(): - result = affiliations_production_results.get() - if result is not None: - plots, affiliations_production_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [affiliations_production_tab], [plots], "affovertime")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Affiliations' Production over Time added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first", duration=3, type="warning") - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_affiliations_production", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the affiliations' production over time.") - ui.input_numeric("TopAffProdK", "Select the number of top affiliations to display:", value=10) - - # Reactive value to store affiliations production results - affiliations_production_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_affiliations_production) - def aff_over_time(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "📈 Analyzing affiliations production over time...", - "🔍 Processing affiliate data across time periods...", - "📊 Generating production timeline visualization...", - "⏳ Computing temporal patterns in affiliations...", - "✨ Almost there! Preparing your dashboard...", - "🎯 Finalizing affiliations production analysis...", - "🔗 Connecting affiliations and time data...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - top_k_affiliations = input.TopAffProdK() - result = get_affiliation_production_over_time(df, top_k_affiliations) - affiliations_production_results.set(result) - finally: - ui.modal_remove() - - with ui.navset_underline(id="affiliations_production_tab"): - with ui.nav_panel("Plot"): - @render.ui - def show_affiliations_production_placeholder(): - result = affiliations_production_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the Affiliations' Production over Time analysis.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - # When result is available, show the widget - return None - - @render_widget - def show_affiliations_production(): - result = affiliations_production_results.get() - if result is not None: - plot_affiliations_production, _ = result - return plot_affiliations_production - - with ui.nav_panel("Table - Production per year"): - @render.ui - def table_affiliations_production(): - result = affiliations_production_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the affiliations production data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, table_affiliations_production = result - return ui.HTML(DT(table_affiliations_production, style="width=100%;")) - - # --- Affiliations' Local Impact Section --- - with ui.nav_panel("None", value="corresponding_authors"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("🌍 Corresponding Author's Countries", style="color: #5567BB;") - ui.p("The corresponding author's countries of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_corresponding_authors", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("corresponding_authors_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"CorrespondingAuthors-{todaydate}.png" - ) - def download_corresponding_authors(): - result = corresponding_authors_results.get() - if result is not None: - plot_corresponding_authors, _ = result - yield plotly_download( - plot_corresponding_authors, - title="Corresponding Authors' Countries", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.corresponding_authors_report) - def show_corresponding_authors_report(): - result = corresponding_authors_results.get() - if result is not None: - plots, corresponding_authors_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [corresponding_authors_tab], [plots], "corrauthcountries")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Corresponding Authors' Countries added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first", duration=3, type="warning") - return ui.notification_show("✅ Corresponding Authors' Countries added to report", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_corresponding_authors", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the corresponding author's countries.") - ui.input_numeric("TopCountries", "Select the number of top countries to display:", value=10, min=1) - # Reactive value to store corresponding authors results - corresponding_authors_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_corresponding_authors) - def countries_data(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "🌍 Loading country data...", - "🔎 Analyzing corresponding author countries...", - "📊 Compiling country-level statistics...", - "🗺️ Mapping global collaborations...", - "✨ Almost there! Preparing your country dashboard...", - "📈 Calculating country indicators...", - "🔗 Connecting countries and authors...", - "🌐 Exploring your international landscape...", - "🚀 Science mapping by country in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - top_k_countries = input.TopCountries() - result = get_corresponding_author_countries(df, top_k_countries) - corresponding_authors_results.set(result) - finally: - ui.modal_remove() - - with ui.navset_underline(id="corresponding_authors_tab"): - with ui.nav_panel("Plot"): - @render.ui - def show_countries_collaboration_placeholder(): - result = corresponding_authors_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the Corresponding Author's Countries analysis.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - # When result is available, show the widget - return None - - @render_widget - def show_countries_collaboration(): - result = corresponding_authors_results.get() - if result is not None: - countries_plot, _ = result - return countries_plot - - with ui.nav_panel("Table"): - @render.ui - def table_countries_collaboration(): - result = corresponding_authors_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the corresponding authors' countries data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, countries_table = result - return ui.HTML(DT(countries_table, style="width=100%;")) - - # --- Countries' Scientific Production Section --- - with ui.nav_panel("None", value="countries_scientific_production"): - with ui.layout_columns( - col_widths=(9, 3), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("🌍 Countries' Scientific Production", style="color: #5567BB;") - ui.p("The countries' scientific production of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px;"): - ui.input_action_button("countries_scientific_production_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"CountriesScientificProduction-{todaydate}.png" - ) - def download_countries_scientific_production(): - plot_countries_scientific_production, _ = countries_production() - yield plotly_download( - plot_countries_scientific_production, - title="Countries' Scientific Production", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.countries_scientific_production_report) - def show_countries_scientific_production_report(): - plots, countries_scientific_production_tab = countries_production() - report_excel.set(add_to_report(report_choices, report_excel, [countries_scientific_production_tab], [plots], "countrysciprod")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Countries' Scientific Production added to report", duration=5, close_button=False) - - with ui.card(full_screen=True): - @reactive.calc - def countries_production(): - # Show loading modal while calculating (same style as Main Information) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "🌍 Analyzing countries' scientific production...", - "📊 Processing geographical data...", - "🏆 Ranking countries by production...", - "📈 Calculating production metrics...", - "✨ Almost there! Preparing your dashboard...", - "🎯 Finalizing countries' production analysis...", - "🔗 Connecting geographical and publication data...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - result = get_countries_production(df) - return result - finally: - ui.modal_remove() - - with ui.navset_underline(id="countries_scientific_production_tab"): - with ui.nav_panel("Plot"): - @render_widget - def show_countries_production(): - countries_plot, _ = countries_production() - return countries_plot - - with ui.nav_panel("Table"): - @render.ui - def table_countries_production(): - _, countries_table = countries_production() - return ui.HTML(DT(countries_table, style="width=100%;")) - - # --- Countries' Production over Time Section --- - with ui.nav_panel("None", value="countries_production_over_time"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("🌍 Countries' Production over Time", style="color: #5567BB;") - ui.p("The countries' production over time of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_countries_over_time", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("countries_production_over_time_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"CountriesProductionOverTime-{todaydate}.png" - ) - def download_countries_production_over_time(): - result = countries_over_time_results.get() - if result is not None: - plot_countries_production_over_time, _ = result - yield plotly_download( - plot_countries_production_over_time, - title="Countries' Production over Time", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.countries_production_over_time_report) - def show_countries_production_over_time_report(): - result = countries_over_time_results.get() - if result is not None: - plots, countries_production_over_time_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [countries_production_over_time_tab], [plots], "countryprodovertime")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Countries' Production over Time added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first", duration=3, type="warning") - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_countries_production_over_time", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the countries' production over time.") - ui.input_numeric("TopCountriesProdK", "Select the number of countries to display:", value=5) - - # Reactive value to store countries over time results - countries_over_time_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_countries_over_time) - def countries_over_time(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "🌍 Loading country production over time...", - "🔎 Analyzing scientific output by country and year...", - "📊 Compiling country-level publication trends...", - "🗺️ Mapping global research productivity over time...", - "✨ Almost there! Preparing your country dashboard...", - "📈 Calculating country indicators over time...", - "🔗 Connecting countries and publications...", - "🌐 Exploring your international scientific landscape...", - "🚀 Science mapping by country in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - top_k_countries = input.TopCountriesProdK() - result = get_countries_production_over_time(df, top_k_countries) - countries_over_time_results.set(result) - finally: - ui.modal_remove() - - with ui.navset_underline(id="countries_production_over_time_tab"): - with ui.nav_panel("Plot"): - @render.ui - def show_countries_over_time_placeholder(): - result = countries_over_time_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the Countries' Production over Time analysis.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - # When result is available, show the widget - return None - - @render_widget - def show_countries_over_time(): - result = countries_over_time_results.get() - if result is not None: - countries_plot, _ = result - return countries_plot - - with ui.nav_panel("Table"): - @render.ui - def table_countries_over_time(): - result = countries_over_time_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the countries' production over time data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, countries_table = result - return ui.HTML(DT(countries_table, style="width=100%;")) - - # --- Most Cited Countries Section --- - with ui.nav_panel("None", value="most_cited_countries"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("🌍 Most Cited Countries", style="color: #5567BB;") - ui.p("The most cited countries of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_cited_countries", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("most_cited_countries_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"MostCitedCountries-{todaydate}.png" - ) - def download_most_cited_countries(): - result = cited_countries_results.get() - if result is not None: - plot_most_cited_countries, _ = result - yield plotly_download( - plot_most_cited_countries, - title="Most Cited Countries", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.most_cited_countries_report) - def show_most_cited_countries_report(): - result = cited_countries_results.get() - if result is not None: - plots, most_cited_countries_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [most_cited_countries_tab], [plots], "mostcitcountries")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Most Cited Countries added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first", duration=3, type="warning") - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_most_cited_countries", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the most cited countries.") - ui.input_numeric("num_of_cited_countries", "Select the number of countries to apply to the dataset:", value=10) - ui.input_select("cited_countries", "Citation measure", {"total_cit": "Total Citation", "average_cit": "Average Citation"}, selected="total_cit") - - # Reactive value to store cited countries results - cited_countries_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_cited_countries) - def cited_countries(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "🌍 Analyzing most cited countries...", - "📊 Processing citation data by country...", - "🏆 Ranking countries by citations...", - "📈 Calculating citation metrics...", - "✨ Almost there! Preparing your dashboard...", - "🎯 Finalizing cited countries analysis...", - "🔗 Connecting citation and country data...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - num_of_cited_countries = input.num_of_cited_countries() - cited_countries_measure = input.cited_countries() - result = get_cited_countries(df, num_of_cited_countries, cited_countries_measure) - cited_countries_results.set(result) - finally: - ui.modal_remove() - - with ui.navset_underline(id="most_cited_countries_tab"): - with ui.nav_panel("Plot"): - @render.ui - def show_cited_countries_placeholder(): - result = cited_countries_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the Most Cited Countries analysis.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - # When result is available, show the widget - return None - - @render_widget - def show_cited_countries(): - result = cited_countries_results.get() - if result is not None: - plot_cited_countries, _ = result - return plot_cited_countries - - with ui.nav_panel("Table"): - @render.ui - def table_cited_countries(): - result = cited_countries_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the most cited countries data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, cited_countries_tab = result - return ui.HTML(DT(cited_countries_tab, style="width=100%;")) - - # --- Most Global Cited Documents Section --- - with ui.nav_panel("None", value="most_global_cited_documents"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("📄 Most Global Cited Documents", style="color: #5567BB;") - ui.p("The most global cited documents of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_cited_documents", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("most_global_cited_documents_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"MostGlobalCitedDocuments-{todaydate}.png" - ) - def download_most_global_cited_documents(): - plot_most_global_cited_documents, _ = cited_documents() - yield plotly_download( - plot_most_global_cited_documents, - title="Most Global Cited Documents", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.most_global_cited_documents_report) - def show_most_global_cited_documents_report(): - plots, most_global_cited_documents_tab = cited_documents() - report_excel.set(add_to_report(report_choices, report_excel, [most_global_cited_documents_tab], [plots], "mostglobcitdocs")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Most Global Cited Documents added to report", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_most_global_cited_documents", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the most global cited documents.") - ui.input_numeric("num_of_cited_docs", "Select the number of documents to apply to the dataset:", value=10) - ui.input_select("cited_docs", "Citation measure", {"total_cit": "Total Citations", "total_cit_per_year": "Total Citations per Year"}, selected="total_cit") - - # Reactive value to store cited documents results - cited_documents_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_cited_documents) - def cited_documents(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "📄 Analyzing most global cited documents...", - "📊 Processing citation data by document...", - "🏆 Ranking documents by global citations...", - "📈 Calculating document citation metrics...", - "✨ Almost there! Preparing your dashboard...", - "🎯 Finalizing global cited documents analysis...", - "🔗 Connecting citation and document data...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - num_of_cited_docs = input.num_of_cited_docs() - cited_docs = input.cited_docs() - result = get_cited_documents(df, num_of_cited_docs, cited_docs) - cited_documents_results.set(result) - finally: - ui.modal_remove() - - with ui.navset_underline(id="most_global_cited_documents_tab"): - with ui.nav_panel("Plot"): - @render.ui - def show_cited_documents_placeholder(): - result = cited_documents_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the Most Global Cited Documents analysis.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - # When result is available, show the widget - return None - - @render_widget - def show_cited_documents(): - result = cited_documents_results.get() - if result is not None: - plot_cited_documents, _ = result - return plot_cited_documents - - with ui.nav_panel("Table"): - @render.ui - def table_cited_documents(): - result = cited_documents_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the most global cited documents data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, cited_documents_tab = result - return ui.HTML(DT(cited_documents_tab, style="width=100%;")) - - # --- Most Local Cited Documents Section --- - with ui.nav_panel("None", value="most_local_cited_documents"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("📄 Most Local Cited Documents", style="color: #5567BB;") - ui.p("The most local cited documents of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_local_cited_documents", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("most_local_cited_documents_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"MostLocalCitedDocuments-{todaydate}.png" - ) - def download_most_local_cited_documents(): - result = local_cited_documents_results.get() - if result is not None: - plot_most_local_cited_documents, _ = result - yield plotly_download( - plot_most_local_cited_documents, - title="Most Local Cited Documents", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.most_local_cited_documents_report) - def show_most_local_cited_documents_report(): - result = local_cited_documents_results.get() - if result is not None: - plots, most_local_cited_documents_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [most_local_cited_documents_tab], [plots], "mostloccitdocs")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Most Local Cited Documents added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_most_local_cited_documents", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the most local cited documents.") - ui.input_numeric("num_of_local_cited_docs", "Select the number of documents to apply to the dataset:", value=10) - ui.input_select("field_separator", "Field separator character", {"dot_comma": ";", "dot_dot": ":", "dot":"."}, selected="dot_comma") - - # Initialize reactive value for storing results - local_cited_documents_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_local_cited_documents) - def run_local_cited_documents_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "🔍 Processing local cited documents...", - "📊 Analyzing citation patterns...", - "🌐 Generating document rankings...", - "📈 Computing citation metrics...", - "✨ Almost there! Preparing your dashboard...", - "🎯 Finalizing visualization...", - "🔗 Connecting citation and document data...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - # Run analysis - num_of_local_cited_docs = input.num_of_local_cited_docs() - field_separator = input.field_separator() - result = get_local_cited_documents(df, num_of_local_cited_docs, field_separator) - local_cited_documents_results.set(result) - finally: - ui.modal_remove() - - with ui.navset_underline(id="most_local_cited_documents_tab"): - with ui.nav_panel("Plot"): - @render.ui - def placeholder_local_cited_documents(): - result = local_cited_documents_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the most local cited documents plot.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - return "" - - @render_widget - def show_local_cited_documents(): - result = local_cited_documents_results.get() - if result is not None: - plot_local_cited_documents, _ = result - return plot_local_cited_documents - - with ui.nav_panel("Table"): - @render.ui - def table_local_cited_documents(): - result = local_cited_documents_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the most local cited documents data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, local_cited_documents_tab = result - return ui.HTML(DT(local_cited_documents_tab, style="width=100%;")) - - # --- Most Local Cited References Section --- - with ui.nav_panel("None", value="most_local_cited_references"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("📘 Most Local Cited References", style="color: #5567BB;") - ui.p("The most local cited references of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_local_cited_references", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("most_local_cited_references_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"MostLocalCitedReferences-{todaydate}.png" - ) - def download_most_local_cited_references(): - result = local_cited_refs_results.get() - if result is not None: - plot_most_local_cited_references, _ = result - yield plotly_download( - plot_most_local_cited_references, - title="Most Local Cited References", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.most_local_cited_references_report) - def show_most_local_cited_references_report(): - result = local_cited_refs_results.get() - if result is not None: - plots, most_local_cited_references_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [most_local_cited_references_tab], [plots], "mostloccitrefs")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Most Local Cited References added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_most_local_cited_references", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the most local cited references.") - ui.input_numeric("num_of_cited_refs", "Select the number of references to apply to the dataset:", value=10) - ui.input_select("field_separator_ref", "Field separator character", {"dot_comma": ";", "dot_dot": ":", "dot":"."}, selected="dot_comma") - - # Initialize reactive value for storing results - local_cited_refs_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_local_cited_references) - def run_local_cited_references_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "🔍 Processing local cited references...", - "📊 Analyzing reference patterns...", - "🌐 Generating reference rankings...", - "📈 Computing citation metrics...", - "✨ Almost there! Preparing your dashboard...", - "🎯 Finalizing visualization...", - "🔗 Connecting citation and reference data...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - # Run analysis - num_of_cited_refs = input.num_of_cited_refs() - field_separator_ref = input.field_separator_ref() - result = get_local_cited_refs(df, num_of_cited_refs, field_separator_ref) - local_cited_refs_results.set(result) - finally: - ui.modal_remove() - - with ui.navset_underline(id="most_local_cited_references_tab"): - with ui.nav_panel("Plot"): - @render.ui - def placeholder_local_cited_refs(): - result = local_cited_refs_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the most local cited references plot.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - return "" - - @render_widget - def show_local_cited_refs(): - result = local_cited_refs_results.get() - if result is not None: - plot_local_cited_refs, _ = result - return plot_local_cited_refs - - with ui.nav_panel("Table"): - @render.ui - def table_local_cited_refs(): - result = local_cited_refs_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the most local cited references data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, local_cited_refs_tab = result - return ui.HTML(DT(local_cited_refs_tab, style="width=100%;")) - - # --- References Spectroscopy Section --- - with ui.nav_panel("None", value="references_spectroscopy"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("📘 References Spectroscopy", style="color: #5567BB;") - ui.p("The references spectroscopy of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_references_spectroscopy", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("references_spectroscopy_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"ReferencesSpectroscopy-{todaydate}.png" - ) - def download_references_spectroscopy(): - result = ref_spectroscopy_results.get() - if result is not None: - plot_references_spectroscopy, _, _ = result - yield plotly_download( - plot_references_spectroscopy, - title="References Spectroscopy", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.references_spectroscopy_report) - def show_references_spectroscopy_report(): - result = ref_spectroscopy_results.get() - if result is not None: - plots, ref_rpy_tab, ref_spectroscopy_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [ref_rpy_tab, ref_spectroscopy_tab], [plots], "rpys")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ References Spectroscopy added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_references_spectroscopy", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the references spectroscopy.") - ui.input_select("field_separator_spec", "Field separator character", {";": ";", ",": ",", ".":"."}, selected="dot_comma") - with ui.layout_column_wrap(width=1 / 2): - ui.input_numeric("start_year", "Star Year", value=None) - current_year = datetime.now().year - ui.input_numeric("end_year", "EndYear", value=None) - - # Initialize reactive value for storing results - ref_spectroscopy_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_references_spectroscopy) - def run_references_spectroscopy_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "🔍 Processing references spectroscopy...", - "📊 Analyzing temporal patterns...", - "🌐 Computing reference age distribution...", - "📈 Generating spectroscopy metrics...", - "✨ Almost there! Preparing your dashboard...", - "🎯 Finalizing visualization...", - "🔗 Connecting temporal and reference data...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - # Run analysis - start_year = input.start_year() - end_year = input.end_year() - field_separator_spec = input.field_separator_spec() - result = get_references_spectroscopy(df, start_year, end_year, field_separator_spec) - ref_spectroscopy_results.set(result) - finally: - ui.modal_remove() - - with ui.navset_underline(id="references_spectroscopy_tab"): - with ui.nav_panel("Plot"): - @render.ui - def placeholder_references_spectroscopy(): - result = ref_spectroscopy_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the references spectroscopy plot.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - return "" - - @render_widget - def show_references_spectroscopy(): - result = ref_spectroscopy_results.get() - if result is not None: - plot_references_spectroscopy, _, _ = result - return plot_references_spectroscopy - - with ui.nav_panel("Table - RPY"): - @render.ui - def table_references_rpy(): - result = ref_spectroscopy_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the RPY data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, ref_rpy_tab, _ = result - return ui.HTML(DT(ref_rpy_tab, style="width=100%;")) - - with ui.nav_panel("Table - Cited References"): - @render.ui - def table_references_spectroscopy(): - result = ref_spectroscopy_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the cited references data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, _, ref_spectroscopy_tab = result - return ui.HTML(DT(ref_spectroscopy_tab, style="width=100%;")) - - # --- Most Frequent Words --- - with ui.nav_panel("None", value="most_frequent_words"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("🔑 Most Frequent Words", style="color: #5567BB;") - ui.p("The most frequent words in the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_frequent_words", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("most_frequent_words_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"MostFrequentWords-{todaydate}.png" - ) - def download_most_frequent_words(): - result = frequent_words_results.get() - if result is not None: - plot_most_frequent_words, _ = result - yield plotly_download( - plot_most_frequent_words, - title="Most Frequent Words", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.most_frequent_words_report) - def show_most_frequent_words_report(): - result = frequent_words_results.get() - if result is not None: - plots, most_frequent_words_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [most_frequent_words_tab], [plots], "mostfreqwords")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Most Frequent Words added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_most_frequent_words", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the most frequent words.") - ui.input_select("field_mfw", "Field", {"ID": "Keywords Plus", "DE": "Author's Keywords (DE)", "TI": "Titles", "AB": "Abstracts", "WC": "Subject Categories (WoS)"}, selected="ID") - - @render.express() - @reactive.event(input.field_mfw) - def get_frequent_ngrams(): - if input.field_mfw() == "AB" or input.field_mfw() == "TI": - ui.input_select("ngram_mfw", "N-Grams", {1:"Unigrams", 2:"Bigrams", 3:"Trigrams"}, selected=1) - - ui.input_numeric("num_of_words_mfw", "Number of words", value=10) - - with ui.accordion(id="acc_mfw", open=False): - with ui.accordion_panel("Text Editing"): - ui.input_switch("remove_terms_mfw", "Load a list of terms to remove", value=False) - @render.express() - @reactive.event(input.remove_terms_mfw) - def get_frequent_ui(): - if input.remove_terms_mfw(): - ui.markdown("**Upload a TXT or CSV file containing a list of terms you want to remove from the analysis.** \nTerms have to be separated by a standard separator (comma, semicolon or tabulator).") - ui.input_file("upload_terms_mfw", "", accept=[".txt", ".csv"]) - ui.input_select("terms_separator_mfw", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") - - ui.input_switch("remove_synonyms_mfw", "Load a list of synonyms", value=False) - @render.express() - @reactive.event(input.remove_synonyms_mfw) - def get_synonyms_ui(): - if input.remove_synonyms_mfw(): - ui.markdown("**Upload a TXT or CSV file containing, in each row, a list of synonyms, that will be merged into a single term (the first word contained in the row).** \n Synonyms have to be separated by a standard separator (comma, semicolon or tabulator). Rows have to be separated by return separator.") - ui.input_file("upload_synonyms_mfw", "", accept=[".txt", ".csv"]) - ui.input_select("synonyms_separator_mfw", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") - - # Initialize reactive value for storing results - frequent_words_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_frequent_words) - def run_frequent_words_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "🔍 Processing frequent words...", - "📊 Analyzing word patterns...", - "🌐 Computing frequency metrics...", - "📈 Generating word rankings...", - "✨ Almost there! Preparing your dashboard...", - "🎯 Finalizing visualization...", - "🔗 Connecting word and frequency data...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - # Run analysis with current parameters - num_of_words_mfw = input.num_of_words_mfw() - field_mfw = input.field_mfw() - ngram_mfw = input.ngram_mfw() if field_mfw in ["AB", "TI"] else 1 - - file_upload_terms_mfw = None - file_upload_synonyms_mfw = None - terms_data_mfw = None - synonyms_data_mfw = None - - if input.remove_terms_mfw(): - file_upload_terms_mfw: list[FileInfo] | None = input.upload_terms_mfw() - if file_upload_terms_mfw: - with open(file_upload_terms_mfw[0]['datapath'], 'r', encoding='utf-8') as file: - terms_data_mfw = pd.DataFrame([line.strip() for line in file], columns=["Terms"]) - else: - file_upload_terms_mfw = None - terms_data_mfw = None - - if input.remove_synonyms_mfw(): - file_upload_synonyms_mfw: list[FileInfo] | None = input.upload_synonyms_mfw() - if file_upload_synonyms_mfw: - with open(file_upload_synonyms_mfw[0]['datapath'], 'r', encoding='utf-8') as file: - synonyms_data_mfw = pd.DataFrame([line.strip() for line in file], columns=["Synonyms"]) - else: - file_upload_synonyms_mfw = None - synonyms_data_mfw = None - - result = get_frequent_words(df, ngram_mfw, num_of_words_mfw, field_mfw, file_upload_terms_mfw, file_upload_synonyms_mfw) - frequent_words_results.set(result) - except Exception as e: - ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) - finally: - ui.modal_remove() - - # Show additional modal for terms/synonyms if needed - modal_content_files = [] - if terms_data_mfw is not None: - modal_content_files.append(ui.markdown("""

Terms to Remove

""")) - modal_content_files.append(ui.HTML(DT(terms_data_mfw))) - - if synonyms_data_mfw is not None: - modal_content_files.append(ui.markdown("""

Synonyms to Remove

""")) - modal_content_files.append(ui.HTML(DT(synonyms_data_mfw))) - - if modal_content_files: - modal = ui.modal( - *modal_content_files, - easy_close=True, - footer=ui.modal_button("Close", style="background: #5865B9; color: white") - ) - ui.modal_show(modal) - - with ui.navset_underline(id="most_frequent_words_tab"): - with ui.nav_panel("Plot"): - @render.ui - def placeholder_frequent_words(): - result = frequent_words_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the most frequent words plot.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - return "" - - @render_widget - def show_frequent_words(): - result = frequent_words_results.get() - if result is not None: - plot_frequent_words, _ = result - return plot_frequent_words - - with ui.nav_panel("Table"): - @render.ui - def table_frequent_words(): - result = frequent_words_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the most frequent words data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, frequent_words_tab = result - return ui.HTML(DT(frequent_words_tab, style="width=100%;")) - - # --- WordCloud Section --- - with ui.nav_panel("None", value="wordcloud"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("🔑 WordCloud", style="color: #5567BB;") - ui.p("The wordcloud of the most frequent words in the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_wordcloud", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("wordcloud_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"WordCloud-{todaydate}.png" - ) - def download_wordcloud(): - result = wordcloud_results.get() - if result is not None: - plot_wordcloud, _ = result - yield html_download( - plot_wordcloud, - title="WordCloud", - height=height.get(), - dpi=dpi.get(), - ) - - @render.ui - @reactive.event(input.wordcloud_report) - def show_wordcloud_report(): - result = wordcloud_results.get() - if result is not None: - plots, wordcloud_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [wordcloud_tab], [plots], "wordcloud")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ WordCloud added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_wordcloud", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the wordcloud.") - ui.input_select("field_wc", "Field", {"ID": "Keywords Plus", "DE": "Author's Keywords (DE)", "TI": "Titles", "AB": "Abstracts", "WC": "Subject Categories (WoS)"}, selected="ID") - - @render.express() - @reactive.event(input.field_wc) - def get_ngrams_wc(): - if input.field_wc() == "AB" or input.field_wc() == "TI": - ui.input_select("ngram_wc", "N-Grams", {1:"Unigrams", 2:"Bigrams", 3:"Trigrams"}, selected=1) - - ui.input_numeric("num_of_words_wc", "Number of words", value=50) - - with ui.accordion(id="acc_wc", open=False): - with ui.accordion_panel("Text Editing"): - ui.input_switch("remove_terms_wc", "Load a list of terms to remove", value=False) - @render.express() - @reactive.event(input.remove_terms_wc) - def get_frequent_ngrams_wc(): - if input.remove_terms_wc(): - ui.markdown("**Upload a TXT or CSV file containing a list of terms you want to remove from the analysis.** \nTerms have to be separated by a standard separator (comma, semicolon or tabulator).") - ui.input_file("upload_terms_wc", "", accept=[".txt", ".csv"]) - ui.input_select("terms_separator_wc", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") - - ui.input_switch("remove_synonyms_wc", "Load a list of synonyms", value=False) - @render.express() - @reactive.event(input.remove_synonyms_wc) - def get_synonyms_wc(): - if input.remove_synonyms_wc(): - ui.markdown("**Upload a TXT or CSV file containing, in each row, a list of synonyms, that will be merged into a single term (the first word contained in the row).** \n Synonyms have to be separated by a standard separator (comma, semicolon or tabulator). Rows have to be separated by return separator.") - ui.input_file("upload_synonyms_wc", "", accept=[".txt", ".csv"]) - ui.input_select("synonyms_separator_wc", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") - - # Initialize reactive value for storing results - wordcloud_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_wordcloud) - def run_wordcloud_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "🌫️ Generating your wordcloud...", - "🔍 Processing word frequencies...", - "🎨 Creating visual word representation...", - "📊 Calculating word weights...", - "✨ Almost there! Preparing your wordcloud...", - "🌐 Mapping word relationships...", - "🔗 Connecting words and frequencies...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - # Run analysis with current parameters - num_of_words_wc = input.num_of_words_wc() - field_wc = input.field_wc() - ngram_wc = input.ngram_wc() if field_wc in ["AB", "TI"] else 1 - - file_upload_terms_wc = None - file_upload_synonyms_wc = None - terms_data_wc = None - synonyms_data_wc = None - - if input.remove_terms_wc(): - file_upload_terms_wc: list[FileInfo] | None = input.upload_terms_wc() - if file_upload_terms_wc: - with open(file_upload_terms_wc[0]['datapath'], 'r', encoding='utf-8') as file: - terms_data_wc = pd.DataFrame([line.strip() for line in file], columns=["Terms"]) - else: - file_upload_terms_wc = None - terms_data_wc = None - - if input.remove_synonyms_wc(): - file_upload_synonyms_wc: list[FileInfo] | None = input.upload_synonyms_wc() - if file_upload_synonyms_wc: - with open(file_upload_synonyms_wc[0]['datapath'], 'r', encoding='utf-8') as file: - synonyms_data_wc = pd.DataFrame([line.strip() for line in file], columns=["Synonyms"]) - else: - file_upload_synonyms_wc = None - synonyms_data_wc = None - - result = get_wordcloud(df, ngram_wc, num_of_words_wc, field_wc, file_upload_terms_wc, file_upload_synonyms_wc) - wordcloud_results.set(result) - except Exception as e: - ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) - finally: - ui.modal_remove() - - # Show additional modal for terms/synonyms if needed - modal_content_files = [] - if terms_data_wc is not None: - modal_content_files.append(ui.markdown("""

Terms to Remove

""")) - modal_content_files.append(ui.HTML(DT(terms_data_wc))) - - if synonyms_data_wc is not None: - modal_content_files.append(ui.markdown("""

Synonyms to Remove

""")) - modal_content_files.append(ui.HTML(DT(synonyms_data_wc))) - - if modal_content_files: - modal_wc = ui.modal( - *modal_content_files, - easy_close=True, - footer=ui.modal_button("Close", style="background: #5865B9; color: white") - ) - ui.modal_show(modal_wc) - - with ui.navset_underline(id="wordcloud_tab"): - with ui.nav_panel("Plot"): - @render.ui - def placeholder_wordcloud(): - result = wordcloud_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the wordcloud.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - return "" - - @render.ui - def show_wordcloud(): - result = wordcloud_results.get() - if result is not None: - plot_wordcloud, _ = result - return ui.HTML(f'') - - with ui.nav_panel("Table"): - @render.ui - def table_wordcloud(): - result = wordcloud_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the wordcloud data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, wordcloud_tab = result - return ui.HTML(DT(wordcloud_tab, style="width=100%;")) - - # --- TreeMap Section --- - with ui.nav_panel("None", value="treemap"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("🔑 TreeMap", style="color: #5567BB;") - ui.p("The treemap of the most frequent words in the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_treemap", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("treemap_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"TreeMap-{todaydate}.png" - ) - def download_treemap(): - result = treemap_results.get() - if result is not None: - plot_treemap, _ = result - yield plotly_download( - plot_treemap, - title="TreeMap", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.treemap_report) - def show_treemap_report(): - result = treemap_results.get() - if result is not None: - plots, treemap_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [treemap_tab], [plots], "treemap")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ TreeMap added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_treemap", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the treemap.") - ui.input_select("field_tm", "Field", {"ID": "Keywords Plus", "DE": "Author's Keywords (DE)", "TI": "Titles", "AB": "Abstracts", "WC": "Subject Categories (WoS)"}, selected="ID") - - @render.express() - @reactive.event(input.field_tm) - def get_ngrams_tm(): - if input.field_tm() == "AB" or input.field_tm() == "TI": - ui.input_select("ngram_tm", "N-Grams", {1:"Unigrams", 2:"Bigrams", 3:"Trigrams"}, selected=1) - - ui.input_numeric("num_of_words_tm", "Number of words", value=50) - - with ui.accordion(id="acc_tm", open=False): - with ui.accordion_panel("Text Editing"): - ui.input_switch("remove_terms_tm", "Load a list of terms to remove", value=False) - @render.express() - @reactive.event(input.remove_terms_tm) - def get_frequent_ngrams_tm(): - if input.remove_terms_tm(): - ui.markdown("**Upload a TXT or CSV file containing a list of terms you want to remove from the analysis.** \nTerms have to be separated by a standard separator (comma, semicolon or tabulator).") - ui.input_file("upload_terms_tm", "", accept=[".txt", ".csv"]) - ui.input_select("terms_separator_tm", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") - - ui.input_switch("remove_synonyms_tm", "Load a list of synonyms", value=False) - @render.express() - @reactive.event(input.remove_synonyms_tm) - def get_synonyms_tm(): - if input.remove_synonyms_tm(): - ui.markdown("**Upload a TXT or CSV file containing, in each row, a list of synonyms, that will be merged into a single term (the first word contained in the row).** \n Synonyms have to be separated by a standard separator (comma, semicolon or tabulator). Rows have to be separated by return separator.") - ui.input_file("upload_synonyms_tm", "", accept=[".txt", ".csv"]) - ui.input_select("synonyms_separator_tm", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") - - # Initialize reactive value for storing results - treemap_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_treemap) - def run_treemap_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "🌳 Building your treemap...", - "📊 Analyzing hierarchical data...", - "🔍 Processing word categories...", - "📈 Calculating size proportions...", - "✨ Almost there! Preparing your treemap...", - "🎨 Creating visual hierarchy...", - "🔗 Connecting data and visualization...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - # Run analysis with current parameters - num_of_words_tm = input.num_of_words_tm() - field_tm = input.field_tm() - ngram_tm = input.ngram_tm() if field_tm in ["AB", "TI"] else 1 - - file_upload_terms_tm = None - file_upload_synonyms_tm = None - terms_data_tm = None - synonyms_data_tm = None - - if input.remove_terms_tm(): - file_upload_terms_tm: list[FileInfo] | None = input.upload_terms_tm() - if file_upload_terms_tm: - with open(file_upload_terms_tm[0]['datapath'], 'r', encoding='utf-8') as file: - terms_data_tm = pd.DataFrame([line.strip() for line in file], columns=["Terms"]) - else: - file_upload_terms_tm = None - terms_data_tm = None - - if input.remove_synonyms_tm(): - file_upload_synonyms_tm: list[FileInfo] | None = input.upload_synonyms_tm() - if file_upload_synonyms_tm: - with open(file_upload_synonyms_tm[0]['datapath'], 'r', encoding='utf-8') as file: - synonyms_data_tm = pd.DataFrame([line.strip() for line in file], columns=["Synonyms"]) - else: - file_upload_synonyms_tm = None - synonyms_data_tm = None - - result = get_treemap(df, ngram_tm, num_of_words_tm, field_tm, file_upload_terms_tm, file_upload_synonyms_tm) - treemap_results.set(result) - except Exception as e: - ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) - finally: - ui.modal_remove() - - # Show additional modal for terms/synonyms if needed - modal_content_files = [] - if terms_data_tm is not None: - modal_content_files.append(ui.markdown("""

Terms to Remove

""")) - modal_content_files.append(ui.HTML(DT(terms_data_tm))) - - if synonyms_data_tm is not None: - modal_content_files.append(ui.markdown("""

Synonyms to Remove

""")) - modal_content_files.append(ui.HTML(DT(synonyms_data_tm))) - - if modal_content_files: - modal_tm = ui.modal( - *modal_content_files, - easy_close=True, - footer=ui.modal_button("Close", style="background: #5865B9; color: white") - ) - ui.modal_show(modal_tm) - - with ui.navset_underline(id="treemap_tab"): - with ui.nav_panel("Plot"): - @render.ui - def placeholder_treemap(): - result = treemap_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the treemap plot.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - return "" - - @render_widget - def show_treemap(): - result = treemap_results.get() - if result is not None: - plot_treemap, _ = result - return plot_treemap - - with ui.nav_panel("Table"): - @render.ui - def table_treemap(): - result = treemap_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the treemap data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, treemap_tab = result - return ui.HTML(DT(treemap_tab, style="width=100%;")) - - # --- References Spectroscopy Section --- - with ui.nav_panel("None", value="words_frequency_over_time"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("🔑 Words' Frequency over Time", style="color: #5567BB;") - ui.p("The trend of words' frequency over time in the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_words_frequency_over_time", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("words_frequency_over_time_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"WordsFrequencyOverTime-{todaydate}.png" - ) - def download_words_frequency_over_time(): - result = word_frequency_results.get() - if result is not None: - plot_words_frequency_over_time, _ = result - yield plotly_download( - plot_words_frequency_over_time, - title="Words Frequency Over Time", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.words_frequency_over_time_report) - def show_words_frequency_over_time_report(): - result = word_frequency_results.get() - if result is not None: - plots, words_frequency_over_time_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [words_frequency_over_time_tab], [plots], "wordfreqovertime")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Words Frequency Over Time added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_words_frequency", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the words' frequency over time.") - ui.input_select("field_wf", "Field", {"ID": "Keywords Plus", "DE": "Author's Keywords (DE)", "TI": "Titles", "AB": "Abstracts"}, selected="ID") - - @render.express() - @reactive.event(input.field_wf) - def get_ngrams_wf(): - if input.field_wf() == "AB" or input.field_wf() == "TI": - ui.input_select("ngram_wf", "N-Grams", {1:"Unigrams", 2:"Bigrams", 3:"Trigrams"}, selected=1) - - with ui.accordion(id="acc_wf", multiple=True, open=False): - with ui.accordion_panel("Text Editing"): - ui.input_switch("remove_terms_wf", "Load a list of terms to remove", value=False) - @render.express() - @reactive.event(input.remove_terms_wf) - def get_frequent_ngrams_wf(): - if input.remove_terms_wf(): - ui.markdown("**Upload a TXT or CSV file containing a list of terms you want to remove from the analysis.** \nTerms have to be separated by a standard separator (comma, semicolon or tabulator).") - ui.input_file("upload_terms_wf", "", accept=[".txt", ".csv"]) - ui.input_select("terms_separator_wf", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") - - ui.input_switch("remove_synonyms_wf", "Load a list of synonyms", value=False) - @render.express() - @reactive.event(input.remove_synonyms_wf) - def get_synonyms_wf(): - if input.remove_synonyms_wf(): - ui.markdown("**Upload a TXT or CSV file containing, in each row, a list of synonyms, that will be merged into a single term (the first word contained in the row).** \n Synonyms have to be separated by a standard separator (comma, semicolon or tabulator). Rows have to be separated by return separator.") - ui.input_file("upload_synonyms_wf", "", accept=[".txt", ".csv"]) - ui.input_select("synonyms_separator_wf", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") - - with ui.accordion_panel("Additional Options"): - ui.input_select('occurrences', 'Occurrences', {'cumulate': 'Cumulate', 'per_year': 'Per Year'}, selected='cumulate') - ui.input_slider('top_words', 'Number of words', min=1, max=100, value=[1, 10]) - - # Initialize reactive value for storing results - word_frequency_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_words_frequency_over_time) - def run_words_frequency_over_time_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "📈 Analyzing words frequency over time...", - "� Processing temporal word patterns...", - "📊 Computing frequency trends...", - "⏰ Tracking word evolution over time...", - "✨ Almost there! Preparing your analysis...", - "📉 Calculating temporal indicators...", - "🔗 Connecting words and time periods...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - # Run analysis with current parameters - field_wf = input.field_wf() - ngram_wf = input.ngram_wf() if field_wf in ["AB", "TI"] else 1 - occurrences = input.occurrences() - top_words = input.top_words() - - file_upload_terms_wf = None - file_upload_synonyms_wf = None - terms_data_wf = None - synonyms_data_wf = None - - if input.remove_terms_wf(): - file_upload_terms_wf: list[FileInfo] | None = input.upload_terms_wf() - if file_upload_terms_wf: - with open(file_upload_terms_wf[0]['datapath'], 'r', encoding='utf-8') as file: - terms_data_wf = pd.DataFrame([line.strip() for line in file], columns=["Terms"]) - else: - file_upload_terms_wf = None - terms_data_wf = None - - if input.remove_synonyms_wf(): - file_upload_synonyms_wf: list[FileInfo] | None = input.upload_synonyms_wf() - if file_upload_synonyms_wf: - with open(file_upload_synonyms_wf[0]['datapath'], 'r', encoding='utf-8') as file: - synonyms_data_wf = pd.DataFrame([line.strip() for line in file], columns=["Synonyms"]) - else: - file_upload_synonyms_wf = None - synonyms_data_wf = None - - result = get_word_frequency(df, ngram_wf, field_wf, file_upload_terms_wf, file_upload_synonyms_wf, occurrences, top_words) - word_frequency_results.set(result) - except Exception as e: - ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) - finally: - ui.modal_remove() - - # Show additional modal for terms/synonyms if needed - modal_content_files = [] - if terms_data_wf is not None: - modal_content_files.append(ui.markdown("""

Terms to Remove

""")) - modal_content_files.append(ui.HTML(DT(terms_data_wf))) - - if synonyms_data_wf is not None: - modal_content_files.append(ui.markdown("""

Synonyms to Remove

""")) - modal_content_files.append(ui.HTML(DT(synonyms_data_wf))) - - if modal_content_files: - modal_wf = ui.modal( - *modal_content_files, - easy_close=True, - footer=ui.modal_button("Close", style="background: #5865B9; color: white") - ) - ui.modal_show(modal_wf) - - with ui.navset_underline(id="words_frequency_tab"): - with ui.nav_panel("Plot"): - @render.ui - def placeholder_word_frequency(): - result = word_frequency_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the words frequency over time plot.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - return "" - - @render_widget - def show_word_frequency(): - result = word_frequency_results.get() - if result is not None: - plot_word_frequency, _ = result - return plot_word_frequency - - with ui.nav_panel("Table"): - @render.ui - def table_word_frequency(): - result = word_frequency_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the words frequency over time data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, word_frequency_tab = result - return ui.HTML(DT(word_frequency_tab, style="width:100%;")) - - # --- Trend Topics Section --- - with ui.nav_panel("None", value="trend_topics"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("🔑 Trend Topics", style="color: #5567BB;") - ui.p("The trend topics in the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_trend_topics", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("trend_topics_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"TrendTopics-{todaydate}.png" - ) - def download_trend_topics(): - result = trend_topics_results.get() - if result is not None: - plot_trend_topics, _ = result - yield plotly_download( - plot_trend_topics, - title="Trend Topics", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.trend_topics_report) - def show_trend_topics_report(): - result = trend_topics_results.get() - if result is not None: - plots, trend_topics_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [trend_topics_tab], [plots], "trendtopics")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Trend Topics added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Sidebar for data import options - with ui.sidebar(id="sidebar_trend_topics", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the trend topics.") - ui.input_select("field_tt", "Field", {"ID": "Keywords Plus", "DE": "Author's Keywords (DE)", "TI": "Titles", "AB": "Abstracts"}, selected="ID") - - @render.express() - @reactive.event(input.field_tt) - def get_ngrams_tt(): - if input.field_tt() == "AB" or input.field_tt() == "TI": - ui.input_select("ngram_tt", "N-Grams", {1:"Unigrams", 2:"Bigrams", 3:"Trigrams"}, selected=1) - ui.input_select("stemming_tt", "Word Stemming", {True: "Yes", False: "No"}, selected=True) - - @render.express() - def show_timespan(): - data_temp = main_informations() - ui.input_slider("time_window", "Timespan", sep="", ticks=True, min=data_temp['Min_Year'][0], max=data_temp['Max_Year'][0], value=[data_temp['Min_Year'][0], data_temp['Max_Year'][0]], step=1, time_format="YYYY") - - with ui.accordion(id="acc_tt", multiple=True, open=False): - with ui.accordion_panel("Text Editing"): - ui.input_switch("remove_terms_tt", "Load a list of terms to remove", value=False) - @render.express() - @reactive.event(input.remove_terms_tt) - def get_frequent_ngrams_tt(): - if input.remove_terms_tt(): - ui.markdown("**Upload a TXT or CSV file containing a list of terms you want to remove from the analysis.** \nTerms have to be separated by a standard separator (comma, semicolon or tabulator).") - ui.input_file("upload_terms_tt", "", accept=[".txt", ".csv"]) - ui.input_select("terms_separator_tt", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") - - ui.input_switch("remove_synonyms_tt", "Load a list of synonyms", value=False) - @render.express() - @reactive.event(input.remove_synonyms_tt) - def get_synonyms_tt(): - if input.remove_synonyms_tt(): - ui.markdown("**Upload a TXT or CSV file containing, in each row, a list of synonyms, that will be merged into a single term (the first word contained in the row).** \n Synonyms have to be separated by a standard separator (comma, semicolon or tabulator). Rows have to be separated by return separator.") - ui.input_file("upload_synonyms_tt", "", accept=[".txt", ".csv"]) - ui.input_select("synonyms_separator_tt", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") - - with ui.accordion_panel("Additional Options"): - with ui.layout_column_wrap(width=1 / 2): - ui.input_numeric("word_mimimum_frequency", "Word Minimum Frequency", value=5) - ui.input_numeric("number_of_words_year", "Number of Words per Year", value=3) - - # Initialize reactive value for storing results - trend_topics_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_trend_topics) - def run_trend_topics_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "� Analyzing trend topics...", - "🔍 Processing topic evolution...", - "� Computing temporal patterns...", - "⏰ Tracking topic trends over time...", - "✨ Almost there! Preparing your analysis...", - "📉 Calculating topic indicators...", - "🔗 Connecting topics and time periods...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - # Run analysis with current parameters - field_tt = input.field_tt() - ngram_tt = input.ngram_tt() if field_tt in ["AB", "TI"] else 1 - time_window = input.time_window() - - file_upload_terms_tt = None - file_upload_synonyms_tt = None - terms_data_tt = None - synonyms_data_tt = None - - if input.remove_terms_tt(): - file_upload_terms_tt: list[FileInfo] | None = input.upload_terms_tt() - if file_upload_terms_tt: - with open(file_upload_terms_tt[0]['datapath'], 'r', encoding='utf-8') as file: - terms_data_tt = pd.DataFrame([line.strip() for line in file], columns=["Terms"]) - else: - file_upload_terms_tt = None - terms_data_tt = None - - if input.remove_synonyms_tt(): - file_upload_synonyms_tt: list[FileInfo] | None = input.upload_synonyms_tt() - if file_upload_synonyms_tt: - with open(file_upload_synonyms_tt[0]['datapath'], 'r', encoding='utf-8') as file: - synonyms_data_tt = pd.DataFrame([line.strip() for line in file], columns=["Synonyms"]) - else: - file_upload_synonyms_tt = None - synonyms_data_tt = None - - word_mimimum_frequency = input.word_mimimum_frequency() - number_of_words_year = input.number_of_words_year() - - result = get_trend_topics(df, ngram_tt, field_tt, time_window, file_upload_terms_tt, file_upload_synonyms_tt, word_mimimum_frequency, number_of_words_year) - trend_topics_results.set(result) - except Exception as e: - ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) - finally: - ui.modal_remove() - - # Show additional modal for terms/synonyms if needed - modal_content_files = [] - if terms_data_tt is not None: - modal_content_files.append(ui.markdown("""

Terms to Remove

""")) - modal_content_files.append(ui.HTML(DT(terms_data_tt))) - - if synonyms_data_tt is not None: - modal_content_files.append(ui.markdown("""

Synonyms to Remove

""")) - modal_content_files.append(ui.HTML(DT(synonyms_data_tt))) - - if modal_content_files: - modal_tt = ui.modal( - *modal_content_files, - easy_close=True, - footer=ui.modal_button("Close", style="background: #5865B9; color: white") - ) - ui.modal_show(modal_tt) - - with ui.navset_underline(id="trend_topics_tab"): - with ui.nav_panel("Plot"): - @render.ui - def placeholder_trend_topics(): - result = trend_topics_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the trend topics plot.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - return "" - - @render_widget - def show_trend_topics(): - result = trend_topics_results.get() - if result is not None: - plot_trend_topics, _ = result - return plot_trend_topics - - with ui.nav_panel("Table"): - @render.ui - def table_trend_topics(): - result = trend_topics_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the trend topics data table.", style="text-align: center; color: #666; font-size: 16px;"), - style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - _, trend_topics_tab = result - return ui.HTML(DT(trend_topics_tab, style="width:100%;")) - - # --- Cluster Coupling --- - with ui.nav_panel("None", value="cluster_analysis"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("🔍 Cluster by Coupling", style="color: #5567BB;") - ui.p("The cluster analysis by coupling in the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_cluster_analysis", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("cluster_analysis_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"ClusterAnalysis-{todaydate}.png" - ) - def download_cluster_analysis(): - result = clustering_coupling_results.get() - if result is not None: - plot_cluster_analysis, _, _, _ = result - yield plotly_download( - plot_cluster_analysis, - title="Cluster Analysis", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.cluster_analysis_report) - def show_cluster_analysis_report(): - result = clustering_coupling_results.get() - if result is not None: - plot_clustering_coupling, clustering_coupling_network, clustering_coupling_tab, clustering_coupling_clusters = result - report_excel.set(add_to_report(report_choices, report_excel, [clustering_coupling_tab, clustering_coupling_clusters], [plot_clustering_coupling, clustering_coupling_network], "couplingmap")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Cluster Analysis added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=True, fill=True): - with ui.sidebar(id="sidebar_cluster_analysis", position="right"): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the cluster analysis.") - ui.input_select("unit_of_analysis", "Unit of Analysis", {"documents": "Documents", "authors": "Authors", "sources": "Sources"}, selected="documents") - ui.input_select("coupling_field", "Coupling measured by", {"CR": "References", "ID": "Keywords Plus", "DE": "Author's Keywords (DE)", "TI": "Titles", "AB": "Abstracts"}, selected="CR") - - @render.express() - @reactive.event(input.coupling_field) - def get_stemmer_gc(): - if input.coupling_field() == "AB" or input.coupling_field() == "TI": - ui.input_select("stemmer", "Word Stemming:", {True:"Yes", False:"No"}, selected=False) - - with ui.accordion(id="accordion_cluster_labeling", multiple=True, open=False): - with ui.accordion_panel("Impact Parameters"): - ui.input_select("impact_measure", "Impact measure", {"local": "Local Citation Score", "global": "Global Citation Score"}, selected="local") - ui.input_select("cluster_labeling", "Cluster labeling by", {"none": "None", "ID": "Keywords Plus", "DE": "Author's Keywords (DE)", "TI": "Titles", "AB": "Abstracts"}, selected="ID") - - @render.express() - @reactive.event(input.cluster_labeling) - def get_ngrams_gc(): - if input.cluster_labeling() == "AB" or input.cluster_labeling() == "TI": - ui.input_select("cl_ngram", "Select the n-gram to apply to the dataset:", {1:"Unigrams", 2:"Bigrams", 3:"Trigrams"}, selected=1) - - with ui.accordion_panel("Cluster Labeling Parameters"): - with ui.layout_column_wrap(width=1 / 2): - ui.input_numeric("num_of_units", "Number of Units", value=250) - ui.input_numeric("min_cluster_freq", "Min Cluster Freq.", value=5) - with ui.layout_column_wrap(width=1 / 2): - ui.input_numeric("label_per_cluster", "Label per cluster", value=3) - ui.input_numeric("label_size", "Label size", value=0.3) - with ui.layout_column_wrap(width=1 / 2): - ui.input_numeric("community_repulsion", "Community repulsion", value=0) - ui.input_select("clustering_algorithm", "Clustering algorithm", {"none": "None", "edge_betweenness": "Edge Betweenness", "infomap": "InfoMap", "leading_eigen": "Leading Eigenvalues", "leiden": "Leiden", "louvain": "Louvain", "spinglass": "Spinglass", "walktrap": "Walktrap"}, selected="walktrap") - - # Initialize reactive value for storing results - clustering_coupling_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_cluster_analysis) - def run_cluster_analysis_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "� Processing cluster by coupling...", - "📊 Computing coupling relationships...", - "🌐 Generating cluster structure...", - "📈 Optimizing community detection...", - "✨ Almost there! Preparing your network...", - "🎯 Finalizing network visualization...", - "🔗 Connecting documents and citations...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - # Run analysis with current parameters - unit_of_analysis = input.unit_of_analysis() - coupling_field = input.coupling_field() - stemmer = input.stemmer() if coupling_field in ["AB", "TI"] else False - impact_measure = input.impact_measure() - cluster_labeling = input.cluster_labeling() - ngram = input.cl_ngram() if cluster_labeling in ["AB", "TI"] else 1 - num_of_units = input.num_of_units() - min_cluster_freq = input.min_cluster_freq() - label_per_cluster = input.label_per_cluster() - label_size = input.label_size() - community_repulsion = input.community_repulsion() - clustering_algorithm = input.clustering_algorithm() - - result = get_clustering_coupling(df, unit_of_analysis, coupling_field, stemmer, impact_measure, cluster_labeling, ngram, num_of_units, min_cluster_freq, label_per_cluster, label_size, community_repulsion, clustering_algorithm) - clustering_coupling_results.set(result) - except Exception as e: - ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) - finally: - ui.modal_remove() - - with ui.navset_underline(id="clustering_coupling_tab"): - with ui.nav_panel("Map"): - @render.ui - def show_clustering_coupling_map(): - result = clustering_coupling_results.get() - if result is not None: - return None # Hide placeholder when data is available - else: - return ui.div( - ui.p("Click the Run Analysis button to run cluster by coupling", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - @render_widget - def clustering_coupling_map_widget(): - result = clustering_coupling_results.get() - if result is not None: - plot_clustering_coupling, _, _, _ = result - return plot_clustering_coupling - return None - - with ui.nav_panel("Network"): - @render.ui - def network_clustering_coupling(): - result = clustering_coupling_results.get() - if result is not None: - _, clustering_coupling_network, _, _ = result - return ui.HTML(f'') - else: - return ui.div( - ui.p("Click the Run Analysis button to run cluster by coupling", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Table"): - @render.ui - def table_clustering_coupling(): - result = clustering_coupling_results.get() - if result is not None: - _, _, clustering_coupling_tab, _ = result - return ui.HTML(DT(clustering_coupling_tab)) - else: - return ui.div( - ui.p("Click the Run Analysis button to run cluster by coupling", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Clusters"): - @render.ui - def cluster_clustering_coupling(): - result = clustering_coupling_results.get() - if result is not None: - _, _, _, clustering_coupling_clusters = result - return ui.HTML(DT(clustering_coupling_clusters)) - else: - return ui.div( - ui.p("Click the Run Analysis button to run cluster by coupling", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - # ui.busy_indicators.options(spinner_type="bars3") - - # # Loader indicator - # @render.ui - # def indicator_types_ui_cluster(): - # return ui.busy_indicators.use( - # spinners=input.go_clustering_coupling() > 0 - # ) - - # --- Co-occurrence Network Section --- - with ui.nav_panel("None", value="co_occurrence_network"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("🔑 Co-occurrence Network", style="color: #5567BB;") - ui.p("The co-occurrence network in the dataset") - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_co_occurrence_analysis", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("co_occurrence_network_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"CoOccurrenceNetwork-{todaydate}.png" - ) - def download_co_occurrence_network(): - result = co_occurrence_network_results.get() - if result is not None: - plot_co_occurrence_network, _, _, _ = result - yield html_download( - plot_co_occurrence_network, - title="Co-occurrence Network", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.co_occurrence_network_report) - def show_co_occurrence_network_report(): - result = co_occurrence_network_results.get() - if result is not None: - plot_co_occurrence_network, plot_density_plot, co_occurrence_network_tab, plot_degree_plot = result - report_excel.set(add_to_report(report_choices, report_excel, [co_occurrence_network_tab], [plot_co_occurrence_network, plot_density_plot, plot_degree_plot], "cowordnet")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Co-occurrence Network added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=True, fill=True): - # Sidebar for data import options - with ui.sidebar(id="sidebar_co_occurrence_network", position="right", ): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the co-occurrence network.") - ui.input_select("field_cn", "Field", {"ID": "Keywords Plus", "DE": "Author's Keywords (DE)", "TI": "Titles", "AB": "Abstracts"}, selected="ID") - - @render.express() - @reactive.event(input.field_cn) - def get_ngrams_co_occurrence(): - if input.field_cn() == "AB" or input.field_cn() == "TI": - ui.input_select("cn_ngram", "Select the n-gram to apply to the dataset:", {1:"Unigrams", 2:"Bigrams", 3:"Trigrams"}, selected=1) - - with ui.accordion(id="accordion_co_occurrence_network", multiple=True, open=False): - with ui.accordion_panel("Text Editing Parameters"): - ui.input_switch("remove_terms_cn", "Remove terms", value=False) - @render.express() - @reactive.event(input.remove_terms_cn) - def get_frequent_ui_cn(): - if input.remove_terms_cn(): - ui.input_file("upload_terms", "Load a list of terms to remove", accept=[".txt", ".csv"]) - - ui.input_switch("remove_synonyms_cn", "Remove punctuation", value=False) - @render.express() - @reactive.event(input.remove_synonyms_cn) - def get_synonyms_ui_cn(): - if input.remove_synonyms_cn(): - ui.input_file("upload_synonyms", "Load a list of synonyms to remove", accept=[".txt", ".csv"]) - - with ui.accordion_panel("Network Parameters"): - with ui.layout_column_wrap(width=1 / 2): - ui.input_select("network_layout", "Network Layout", {"auto": "Automatic layout", "fr": "Fruchterman-Reingold", "kk": "Kamada-Kawai", "circle": "Circle", "mdrl": "Multi Dimension Scaling", "graphopt": "Graphopt", "sphere": "Sphere", "star": "Star"}, selected="auto") - ui.input_select("clustering_algorithm_cn", "Clustering Algorithm", {"none": "None", "edge_betweenness": "Edge Betweenness", "infomap": "InfoMap", "leading_eigen": "Leading Eigenvalues", "leiden": "Leiden", "louvain": "Louvain", "spinglass": "Spinglass", "walktrap": "Walktrap"}, selected="walktrap") - ui.input_select("normalization_cn", "Normalization", {"association": "Association", "jaccard": "Jaccard", "inclusion": "Inclusion", "salton": "Salton", "equivalence": "Equivalence", "none": "None"}, selected="association") - ui.input_switch("color_by_year", "Node Color by Year", value=False) - - with ui.layout_column_wrap(width=1 / 2): - ui.input_numeric("num_of_nodes", "Number of Nodes", value=50, min=10) - ui.input_numeric("repulsion_force", "Repulsion Force", value=1, min=0, step=0.1) - ui.input_switch("remove_isolated", "Remove Isolated Nodes", value=True) - ui.input_numeric("min_edges", "Minimum Number of Edges", value=2, min=1) - - with ui.accordion_panel("Graphical Parameters"): - with ui.layout_column_wrap(width=1 / 2): - ui.input_numeric("node_opacity", "Node Opacity", value=0.7, min=0.1, max=1, step=0.05) - ui.input_numeric("num_of_labels", "Number of Labels", value=1000, min=0) - - with ui.layout_column_wrap(width=1 / 2): - ui.input_switch("label_cex", "Label Cex", value=True) - ui.input_select("node_shape", "Node Shape", {"circle": "Circle", "square": "Square", "dot": "Dot","ellipse": "Ellipse", "square": "Square", "text":"Text"}, selected="dot") - - with ui.layout_column_wrap(width=1 / 2): - ui.input_numeric("label_size_ls", "Label Size", value=3, min=0.1, step=0.1) - ui.input_numeric("edge_size", "Edge Size", value=5, min=0.1, step=0.1) - ui.input_switch("node_shadow", "Show Node Shadow", value=True) - ui.input_switch("edit_nodes", "Enable Node Editing", value=False) - - # Initialize reactive value for storing results - co_occurrence_network_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_co_occurrence_analysis) - def run_co_occurrence_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "�️ Processing co-occurrence network...", - "📊 Computing node relationships...", - "🌐 Building network structure...", - "📈 Optimizing layout algorithm...", - "✨ Almost there! Preparing your network...", - "🎯 Finalizing network visualization...", - "🔗 Connecting terms and concepts...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - # Run analysis with current parameters - field_cn = input.field_cn() - ngram_cn = input.cn_ngram() if field_cn in ["AB", "TI"] else 1 - network_layout = input.network_layout() - clustering_algorithm_cn = input.clustering_algorithm_cn() - normalization_cn = input.normalization_cn() - color_by_year = input.color_by_year() - num_of_nodes = input.num_of_nodes() - repulsion_force = input.repulsion_force() - remove_isolated = input.remove_isolated() - min_edges = input.min_edges() - node_opacity = input.node_opacity() - num_of_labels = input.num_of_labels() - node_shape = input.node_shape() - label_size_ls = input.label_size_ls() - edge_size = input.edge_size() - node_shadow = input.node_shadow() - edit_nodes = input.edit_nodes() - label_cex = input.label_cex() - - file_upload_terms = None - file_upload_synonyms = None - terms_data = None - synonyms_data = None - - if input.remove_terms_cn(): - file_upload_terms: list[FileInfo] | None = input.upload_terms() - if file_upload_terms: - with open(file_upload_terms[0]['datapath'], 'r', encoding='utf-8') as file: - terms_data = pd.DataFrame([line.strip() for line in file], columns=["Terms"]) - else: - # Reset terms file if checkbox is disabled - file_upload_terms = None - terms_data = None - - if input.remove_synonyms_cn(): - file_upload_synonyms: list[FileInfo] | None = input.upload_synonyms() - if file_upload_synonyms: - with open(file_upload_synonyms[0]['datapath'], 'r', encoding='utf-8') as file: - synonyms_data = pd.DataFrame([line.strip() for line in file], columns=["Synonyms"]) - else: - # Reset synonyms file if checkbox is disabled - file_upload_synonyms = None - synonyms_data = None - - # Show modal for terms/synonyms if needed - modal_content = [] - - if terms_data is not None: - modal_content.append(ui.markdown("""

Terms to Remove

""")) - modal_content.append(ui.HTML(DT(terms_data))) - - if synonyms_data is not None: - modal_content.append(ui.markdown("""

Synonyms to Remove

""")) - modal_content.append(ui.HTML(DT(synonyms_data))) - - result = get_co_occurence_network(df, field_cn, ngram_cn, network_layout, clustering_algorithm_cn, normalization_cn, color_by_year, num_of_nodes, - repulsion_force, remove_isolated, min_edges, node_opacity, num_of_labels, node_shape, label_size_ls, - edge_size, node_shadow, edit_nodes, label_cex, file_upload_terms, file_upload_synonyms) - co_occurrence_network_results.set(result) - except Exception as e: - ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) - finally: - ui.modal_remove() - - # Show modal for terms/synonyms after main processing if needed - if modal_content: - file_modal = ui.modal( - *modal_content, - easy_close=True, - footer=ui.modal_button("Close", style="background: #5865B9; color: white") - ) - ui.modal_show(file_modal) - - with ui.navset_underline(id="co_occurrence_network_tab"): - with ui.nav_panel("Network"): - @render.ui - def show_co_occurrence_network(): - result = co_occurrence_network_results.get() - if result is not None: - plot_co_occurrence_network, _, _, _ = result - return ui.HTML(f'') - else: - return ui.div( - ui.p("Click the Run Analysis button to run co-occurrence network", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Density Plot"): - @render_widget - def density_plot_widget(): - result = co_occurrence_network_results.get() - if result is not None: - _, plot_density_plot, _, _ = result - return plot_density_plot - return None - - with ui.nav_panel("Table"): - @render.ui - def table_co_occurrence_network(): - result = co_occurrence_network_results.get() - if result is not None: - _, _, co_occurrence_network_tab, _ = result - return ui.HTML(DT(co_occurrence_network_tab, style="width=100%;")) - else: - return ui.div( - ui.p("Click the Run Analysis button to run co-occurrence network", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Degree Plot"): - @render_widget - def degree_plot_widget(): - result = co_occurrence_network_results.get() - if result is not None: - _, _, _, plot_degree_plot = result - return plot_degree_plot - return None - - # --- Thematic Map Section --- - with ui.nav_panel("None", value="thematic_map"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("🗺️ Thematic Map", style="color: #5567BB;") - ui.p("The thematic map of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_thematic_map_analysis", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("thematic_map_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"ThematicMap-{todaydate}.png" - ) - def download_thematic_map(): - result = thematic_map_results.get() - if result is not None: - plot_thematic_map, _, _, _, _ = result - yield plotly_download( - plot_thematic_map, - title="Thematic Map", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.thematic_map_report) - def show_thematic_map_report(): - result = thematic_map_results.get() - if result is not None: - plot_thematic_map, thematic_map_net, thematic_map_tab, thematic_map_clusters, thematic_map_docs = result - report_excel.set(add_to_report(report_choices, report_excel, [thematic_map_tab, thematic_map_clusters, thematic_map_docs], [plot_thematic_map, thematic_map_net], "thematicmap")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Thematic Map added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - with ui.sidebar(id="sidebar_thematic_map", position="right"): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the thematic map.") - ui.input_select("thematic_field", "Text source for analysis", { - "ID": "Keywords Plus (ID)", - "DE": "Author's Keywords (DE)", - "TI": "Titles", - "AB": "Abstracts" - }, selected="ID") - - @render.express() - @reactive.event(input.thematic_field) - def show_stemmer_input(): - if input.thematic_field() in ["TI", "AB"]: - ui.input_select("thematic_stemmer", "Apply stemming?", {True: "Yes", False: "No"}, selected=False) - - @render.express() - @reactive.event(input.thematic_field) - def show_ngram_input(): - if input.thematic_field() in ["TI", "AB"]: - ui.input_select("thematic_ngram", "N-grams", { - 1: "Unigrams", - 2: "Bigrams", - 3: "Trigrams", - 4: "4-grams" - }, selected=1) - - with ui.accordion(id="accordion_thematic_map", multiple=True, open=False): - with ui.accordion_panel("Text Editing Parameters"): - with ui.layout_column_wrap(width=1/2): - ui.input_numeric("thematic_n", "Number of terms to include", value=250) - ui.input_numeric("thematic_minfreq", "Minimum frequency", value=5) - - with ui.layout_column_wrap(width=1/2): - ui.input_numeric("thematic_label_size", "Label size", value=0.5) - ui.input_numeric("thematic_n_labels", "Labels per cluster", value=1) - - ui.input_select("thematic_clustering", "Clustering algorithm", { - "walktrap": "Walktrap", - "louvain": "Louvain", - "leiden": "Leiden", - "infomap": "Infomap" - }, selected="walktrap") - - ui.input_numeric("thematic_repulsion", "Community repulsion", value=0.1) - - # Initialize reactive value for storing results - thematic_map_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_thematic_map_analysis) - def run_thematic_map_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "�️ Processing thematic map...", - "📊 Computing keyword clusters...", - "🌐 Building thematic structure...", - "📈 Optimizing cluster positioning...", - "✨ Almost there! Preparing your map...", - "🎯 Finalizing thematic visualization...", - "🔗 Connecting themes and keywords...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - # Run analysis with current parameters - field = input.thematic_field() - stemming = input.thematic_stemmer() if field in ["TI", "AB"] else False - ngram = input.thematic_ngram() if field in ["TI", "AB"] else 1 - n = input.thematic_n() - minfreq = input.thematic_minfreq() - label_size = input.thematic_label_size() - n_labels = input.thematic_n_labels() - cluster = input.thematic_clustering() - repulsion = input.thematic_repulsion() - - result = get_thematic_map(df, field, n, minfreq, ngram, stemming, - label_size, n_labels, repulsion, cluster) - thematic_map_results.set(result) - except Exception as e: - ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) - finally: - ui.modal_remove() - - with ui.navset_underline(id="thematic_tab"): - with ui.nav_panel("Map"): - @render.ui - def show_thematic_map(): - result = thematic_map_results.get() - if result is not None: - return None # Hide placeholder when data is available - else: - return ui.div( - ui.p("Click the Run Analysis button to run thematic map", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - @render_widget - def thematic_map_widget(): - result = thematic_map_results.get() - if result is not None: - plot_map, _, _, _, _ = result - return plot_map - return None - - with ui.nav_panel("Network"): - @render.ui - def network_thematic_map(): - result = thematic_map_results.get() - if result is not None: - _, thematic_map_network, _, _, _ = result - return ui.HTML(f'') - else: - return ui.div( - ui.p("Click the Run Analysis button to run thematic map", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Table"): - @render.ui - def table_thematic_map(): - result = thematic_map_results.get() - if result is not None: - _, _, thematic_map_table, _, _ = result - return ui.HTML(DT(thematic_map_table, style="width=100%;")) - else: - return ui.div( - ui.p("Click the Run Analysis button to run thematic map", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Clusters"): - @render.ui - def clusters_thematic_map(): - result = thematic_map_results.get() - if result is not None: - _, _, _, thematic_map_cluster, _ = result - return ui.HTML(DT(thematic_map_cluster, style="width=100%;")) - else: - return ui.div( - ui.p("Click the Run Analysis button to run thematic map", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Documents"): - @render.ui - def documents_thematic_map(): - result = thematic_map_results.get() - if result is not None: - _, _, _, _, thematic_map_documents = result - return ui.HTML(DT(thematic_map_documents, maxBytes="10MB", style="width=100%;")) - else: - return ui.div( - ui.p("Click the Run Analysis button to run thematic map", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - # --- Thematic Evolution Section --- - with ui.nav_panel("None", value="thematic_evolution"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("📈 Thematic Evolution", style="color: #5567BB;") - ui.p("The thematic evolution of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_thematic_evolution_analysis", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("thematic_evolution_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"ThematicEvolution-{todaydate}.png" - ) - def download_thematic_evolution(): - result = thematic_evolution_results.get() - if result is not None: - plot_thematic_evolution, _, _ = result - yield html_download( - plot_thematic_evolution, - title="Thematic Evolution", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.thematic_evolution_report) - def show_thematic_evolution_report(): - result = thematic_evolution_results.get() - if result is not None: - plot_thematic_evolution, thematic_evolution_tab, TM = result - report_excel.set(add_to_report(report_choices, report_excel, [thematic_evolution_tab], [plot_thematic_evolution], "thematicevolution")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - for i in range(len(TM)): - report_excel.set(add_to_report(report_choices, report_excel, [TM[i]['words'], TM[i]['clusters'], TM[i]['documentToClusters']], [TM[i]['map'], TM[i]['net_html']], f"te_period_{i+1}")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - - return ui.notification_show("✅ Thematic Evolution added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=True): - with ui.sidebar(id="sidebar_thematic_evolution", position="right"): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the thematic evolution.") - ui.input_select("thematic_evolution_field", "Text source for analysis", { - "ID": "Keywords Plus (ID)", - "DE": "Author's Keywords (DE)", - "TI": "Titles", - "AB": "Abstracts" - }, selected="ID") - - ui.input_switch("overlap", "Avoid Label Overlap", value=True) - - @render.express() - @reactive.event(input.thematic_evolution_field) - def show_text_editing_evolution(): - if input.thematic_evolution_field() in ["TI", "AB"]: - with ui.layout_column_wrap(width=1 / 2): - ui.input_select("thematic_evolution_stemmer", "Apply stemming?", {True: "Yes", False: "No"}, selected=False) - ui.input_select("thematic_evolution_ngram", "N-grams", { - 1: "Unigrams", - 2: "Bigrams", - 3: "Trigrams", - 4: "4-grams" - }, selected=1) - - with ui.accordion(id="accordion_thematic_evolution", multiple=True, open=False): - with ui.accordion_panel("Text Editing Parameters"): - ui.input_switch("remove_terms_te", "Remove terms", value=False) - - @render.express() - @reactive.event(input.remove_terms_te) - def get_frequent_ngrams_te(): - if input.remove_terms_te(): - ui.input_file("upload_terms_te", "Load a list of terms to remove", accept=[".txt", ".csv"]) - - ui.input_switch("remove_synonyms_te", "Remove punctuation", value=False) - - @render.express() - @reactive.event(input.remove_synonyms_te) - def get_synonyms_te(): - if input.remove_synonyms_te(): - ui.input_file("upload_synonyms_te", "Load a list of synonyms to remove", accept=[".txt", ".csv"]) - - - with ui.accordion_panel("Thematic Evolution Parameters"): - with ui.layout_column_wrap(width=1 / 2): - ui.input_numeric("thematic_evolution_n", "Number of terms to include", value=250) - ui.input_numeric("thematic_evolution_minfreq", "Minimum frequency", value=5) - - with ui.layout_column_wrap(width=1 / 2): - ui.input_select("weight_index", "Weight Index", { - "inc_index": "Inclusion Index", - "inc_weight_word_occ": "Inclusion Index weighted by Word Occurrences", - "stab_index": "Stability Index", - }, selected="inc_weight_word_occ") - ui.input_numeric("min_weight_index", "Minimum Weight Index", value=0.1, min=0.01, max=1.0, step=0.01) - - with ui.layout_column_wrap(width=1 / 2): - ui.input_numeric("thematic_evolution_label_size", "Label size", value=0.5) - ui.input_numeric("thematic_evolution_n_labels", "Labels per cluster", value=1) - - ui.input_select("thematic_evolution_clustering", "Clustering algorithm", { - "walktrap": "Walktrap", - "louvain": "Louvain", - "leiden": "Leiden", - "infomap": "Infomap" - }, selected="walktrap") - - ui.markdown("Time Slices:") - with ui.layout_column_wrap(width=1 / 2): - ui.input_numeric("number_of_cutting_points", "Number of cutting points", value=1, min=1) - - @render.express() - @reactive.event(input.number_of_cutting_points) - def get_cutting_years(): - if input.number_of_cutting_points() > 1: - for i in range(1, input.number_of_cutting_points()+1): - value = 2019 - input.number_of_cutting_points() + i - ui.input_numeric(f"cutting_year_{i}", f"Cutting Year {i}", value=value, min=1) - else: - ui.input_numeric("cutting_year", "Cutting Year", value=2019, min=1) - - # Initialize reactive value for storing results - thematic_evolution_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_thematic_evolution_analysis) - def run_thematic_evolution_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "⏰ Processing thematic evolution...", - "📊 Computing temporal patterns...", - "🌐 Building evolution structure...", - "📈 Optimizing time periods...", - "✨ Almost there! Preparing your evolution...", - "🎯 Finalizing evolution visualization...", - "🔗 Connecting themes across time...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - # Run analysis with current parameters - field = input.thematic_evolution_field() - overlap = input.overlap() - n = input.thematic_evolution_n() - minfreq = input.thematic_evolution_minfreq() - weight_index = input.weight_index() - min_weight_index = input.min_weight_index() - label_size = input.thematic_evolution_label_size() - n_labels = input.thematic_evolution_n_labels() - cluster = input.thematic_evolution_clustering() - - file_upload_terms_te = None - file_upload_synonyms_te = None - terms_data_te = None - synonyms_data_te = None - - if input.remove_terms_te(): - file_upload_terms_te: list[FileInfo] | None = input.upload_terms_te() - if file_upload_terms_te: - with open(file_upload_terms_te[0]['datapath'], 'r', encoding='utf-8') as file: - terms_data_te = pd.DataFrame([line.strip() for line in file], columns=["Terms"]) - else: - # Reset terms file if checkbox is disabled - file_upload_terms_te = None - terms_data_te = None - - if input.remove_synonyms_te(): - file_upload_synonyms_te: list[FileInfo] | None = input.upload_synonyms_te() - if file_upload_synonyms_te: - with open(file_upload_synonyms_te[0]['datapath'], 'r', encoding='utf-8') as file: - synonyms_data_te = pd.DataFrame([line.strip() for line in file], columns=["Synonyms"]) - else: - # Reset synonyms file if checkbox is disabled - file_upload_synonyms_te = None - synonyms_data_te = None - - # Show modal for terms/synonyms if needed - modal_content_te = [] - - if terms_data_te is not None: - modal_content_te.append(ui.markdown("""

Terms to Remove

""")) - modal_content_te.append(ui.HTML(DT(terms_data_te))) - - if synonyms_data_te is not None: - modal_content_te.append(ui.markdown("""

Synonyms to Remove

""")) - modal_content_te.append(ui.HTML(DT(synonyms_data_te))) - - cutting_points = input.number_of_cutting_points() - - # Prepare arguments for get_thematic_evolution - # Build the list of cutting years for thematic evolution - if cutting_points == 1: - years = [input.cutting_year()] - elif cutting_points > 1: - years = [] - for i in range(1, cutting_points+1): - years.append(input[f"cutting_year_{i}"]()) - else: - years = [] - - # Handle remove.terms and synonyms (if you want to support file upload for these) - remove_terms = terms_data_te["Terms"].tolist() if terms_data_te is not None else None - synonyms = synonyms_data_te["Synonyms"].tolist() if synonyms_data_te is not None else None - - # ngrams and stemming: only for TI/AB - ngrams = input.thematic_evolution_ngram() if field in ["TI", "AB"] else 1 - stemming = input.thematic_evolution_stemmer() if field in ["TI", "AB"] else False - - result = get_thematic_evolution(df, field, years, n, weight_index, min_weight_index, minfreq, label_size, ngrams, stemming, n_labels, overlap, remove_terms, synonyms, cluster) - thematic_evolution_results.set(result) - except Exception as e: - ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) - finally: - ui.modal_remove() - - # Show modal for terms/synonyms after main processing if needed - if modal_content_te: - file_modal_te = ui.modal( - *modal_content_te, - easy_close=True, - footer=ui.modal_button("Close", style="background: #5865B9; color: white") - ) - ui.modal_show(file_modal_te) - - @render.express() - @reactive.event(input.number_of_cutting_points) - def show_time_slices(): - with ui.navset_underline(id="thematic_evolution_tab"): - with ui.nav_panel("Thematic Evolution"): - with ui.navset_underline(id="thematic_evolution_map_tab"): - with ui.nav_panel("Map"): - @render.ui - def show_thematic_evolution_map(): - result = thematic_evolution_results.get() - if result is not None: - plot_map, _, _ = result - return ui.HTML(f'') - else: - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Table"): - @render.ui - def table_thematic_evolution(): - result = thematic_evolution_results.get() - if result is not None: - _, thematic_evolution_table, _ = result - return ui.HTML(DT(thematic_evolution_table, style="width=100%;")) - else: - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - if input.number_of_cutting_points() >= 0: - with ui.nav_panel("Time Slice 1"): - with ui.navset_underline(id="thematic_evolution_map_tab_2"): - with ui.nav_panel("Map"): - @render_widget - def show_thematic_evolution_map_2(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - return TM[0]["map"] if len(TM) > 0 else None - return None - - with ui.nav_panel("Network"): - @render.ui - def network_thematic_evolution_2(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 0: - return ui.HTML(f'') - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Table"): - @render.ui - def table_thematic_evolution_2(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 0: - return ui.HTML(DT(TM[0]["words"], style="width=100%;")) - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Clusters"): - @render.ui - def clusters_thematic_evolution_2(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 0: - return ui.HTML(DT(TM[0]["clusters"], style="width=100%;")) - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Documents"): - @render.ui - def documents_thematic_evolution_2(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 0: - return ui.HTML(DT(TM[0]["documentToClusters"], maxBytes="10MB", style="width=100%;")) - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - if input.number_of_cutting_points() >= 1: - with ui.nav_panel("Time Slice 2"): - with ui.navset_underline(id="thematic_evolution_map_tab_3"): - with ui.nav_panel("Map"): - @render_widget - def show_thematic_evolution_map_3(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - return TM[1]["map"] if len(TM) > 1 else None - return None - - with ui.nav_panel("Network"): - @render.ui - def network_thematic_evolution_3(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 1: - return ui.HTML(f'') - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Table"): - @render.ui - def table_thematic_evolution_3(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 1: - return ui.HTML(DT(TM[1]["words"], style="width=100%;")) - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Clusters"): - @render.ui - def clusters_thematic_evolution_3(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 1: - return ui.HTML(DT(TM[1]["clusters"], style="width=100%;")) - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Documents"): - @render.ui - def documents_thematic_evolution_3(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 1: - return ui.HTML(DT(TM[1]["documentToClusters"], maxBytes="10MB", style="width=100%;")) - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - if input.number_of_cutting_points() >= 2: - with ui.nav_panel("Time Slice 3"): - with ui.navset_underline(id="thematic_evolution_map_tab_4"): - with ui.nav_panel("Map"): - @render_widget - def show_thematic_evolution_map_4(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - return TM[2]["map"] if len(TM) > 2 else None - return None - - with ui.nav_panel("Network"): - @render.ui - def network_thematic_evolution_4(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 2: - return ui.HTML(f'') - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Table"): - @render.ui - def table_thematic_evolution_4(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 2: - return ui.HTML(DT(TM[2]["words"], style="width=100%;")) - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Clusters"): - @render.ui - def clusters_thematic_evolution_4(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 2: - return ui.HTML(DT(TM[2]["clusters"], style="width=100%;")) - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Documents"): - @render.ui - def documents_thematic_evolution_4(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 2: - return ui.HTML(DT(TM[2]["documentToClusters"], maxBytes="10MB", style="width=100%;")) - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - if input.number_of_cutting_points() >= 3: - with ui.nav_panel("Time Slice 4"): - with ui.navset_underline(id="thematic_evolution_map_tab_5"): - with ui.nav_panel("Map"): - @render_widget - def show_thematic_evolution_map_5(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - return TM[3]["map"] if len(TM) > 3 else None - return None - - with ui.nav_panel("Network"): - @render.ui - def network_thematic_evolution_5(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 3: - return ui.HTML(f'') - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Table"): - @render.ui - def table_thematic_evolution_5(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 3: - return ui.HTML(DT(TM[3]["words"], style="width=100%;")) - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Clusters"): - @render.ui - def clusters_thematic_evolution_5(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 3: - return ui.HTML(DT(TM[3]["clusters"], style="width=100%;")) - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Documents"): - @render.ui - def documents_thematic_evolution_5(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 3: - return ui.HTML(DT(TM[3]["documentToClusters"], maxBytes="10MB", style="width=100%;")) - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - if input.number_of_cutting_points() >= 4: - with ui.nav_panel("Time Slice 5"): - with ui.navset_underline(id="thematic_evolution_map_tab_6"): - with ui.nav_panel("Map"): - @render_widget - def show_thematic_evolution_map_6(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - return TM[4]["map"] if len(TM) > 4 else None - return None - - with ui.nav_panel("Network"): - @render.ui - def network_thematic_evolution_6(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 4: - return ui.HTML(f'') - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Table"): - @render.ui - def table_thematic_evolution_6(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 4: - return ui.HTML(DT(TM[4]["words"]), style="width=100%;") - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Clusters"): - @render.ui - def clusters_thematic_evolution_6(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 4: - return ui.HTML(DT(TM[4]["clusters"], style="width=100%;")) - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Documents"): - @render.ui - def documents_thematic_evolution_6(): - result = thematic_evolution_results.get() - if result is not None: - _, _, TM = result - if len(TM) > 4: - return ui.HTML(DT(TM[4]["documentToClusters"], maxBytes="10MB", style="width=100%;")) - return ui.div( - ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - # --- Factorial Analysis Section --- - with ui.nav_panel("None", value="factorial_analysis"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("📊 Factorial Approach", style="color: #5567BB;") - ui.p("The factorial approach of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_factorial_analysis", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("factorial_analysis_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"FactorialAnalysis-{todaydate}.png" - ) - def download_factorial_analysis(): - result = factorial_analysis_results.get() - if result is not None: - plot_factorial_analysis, _, _, _ = result - yield plotly_download( - plot_factorial_analysis, - title="Factorial Analysis", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.factorial_analysis_report) - def show_factorial_analysis_report(): - result = factorial_analysis_results.get() - if result is not None: - plot_factorial_analysis, plot_dendogram, words_by_cluster, articles_by_cluster = result - report_excel.set(add_to_report(report_choices, report_excel, [words_by_cluster, articles_by_cluster], [plot_factorial_analysis, plot_dendogram], "factorialanalysis")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Factorial Analysis added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - with ui.sidebar(id="sidebar_factorial_approach", position="right"): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the factorial approach.") - - # Accordion for Factorial Analysis parameters - with ui.accordion(id="accordion_factorial_analysis", multiple=True, open=False): - with ui.accordion_panel("Analysis Method & Text Field"): - # 1 — algoritmo di riduzione - ui.input_select( - "wordmap_method", - "Analysis Method", - { - "MCA": "Multiple Correspondence Analysis (MCA)", - "CA": "Correspondence Analysis (CA)", - "MDS": "Multidimensional Scaling (MDS)", - }, - selected="MCA", - ) - - # 2 — campo testuale da analizzare - ui.input_select( - "wordmap_field", - "Text Field", - { - "ID": "Keywords Plus (ID)", - "DE": "Author's Keywords (DE)", - "TI": "Titles", - "AB": "Abstracts" - }, - selected="ID", - ) - - @render.express() - @reactive.event(input.wordmap_method) - def get_ngrams_fa(): - if input.field_tt() == "AB" or input.field_tt() == "TI": - ui.input_select("ngram_fa", "Select the n-gram to apply to the dataset:", {1:"Unigrams", 2:"Bigrams", 3:"Trigrams"}, selected=1) - - with ui.accordion_panel("Basic Plot Settings"): - with ui.layout_column_wrap(width=1 / 3): - ui.input_numeric("wordmap_dimX", "X Axis Dimension", value=0) - ui.input_numeric("wordmap_dimY", "Y Axis Dimension", value=1) - ui.input_numeric("wordmap_top_words", "Num. of terms", value=50) - - ui.input_slider( "wordmap_threshold", "Label Overlap Threshold", min=0.01, max=1.0, value=0.10 ) - - with ui.layout_column_wrap(width=1 / 2): - ui.input_numeric("wordmap_labelsize", "Label Font Size", value=16) - ui.input_numeric("wordmap_dot_size", "Base Dot Size", value=16) - - with ui.accordion_panel("Analysis Parameters"): - with ui.layout_column_wrap(width=1 / 2): - ui.input_numeric("wordmap_n_terms", "Number of Terms", value=50, min=10) - ui.input_numeric("wordmap_n_clusters", "Number of Clusters", value=1, min=1) - ui.input_numeric("wordmap_n_docs", "Documents to Use (0 = all)", value=0, min=0) - - with ui.accordion_panel("Text Editing"): - ui.input_switch("remove_terms_wm", "Remove terms", value=False) - - @render.express() - @reactive.event(input.remove_terms_wm) - def get_frequent_ngrams_wm(): - if input.remove_terms_wm(): - ui.input_file("upload_terms_wm", "Load a list of terms to remove", accept=[".txt", ".csv"]) - - ui.input_switch("remove_synonyms_wm", "Remove punctuation", value=False) - - @render.express() - @reactive.event(input.remove_synonyms_wm) - def get_synonyms_wm(): - if input.remove_synonyms_wm(): - ui.input_file("upload_synonyms_wm", "Load a list of synonyms to remove", accept=[".txt", ".csv"]) - - # Initialize reactive value for storing results - factorial_analysis_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_factorial_analysis) - def run_factorial_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "� Processing factorial analysis...", - "📊 Computing factor loadings...", - "🌐 Building dimensional space...", - "📈 Optimizing clustering algorithm...", - "✨ Almost there! Preparing your analysis...", - "🎯 Finalizing factorial visualization...", - "🔗 Connecting words and dimensions...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - # Run analysis with current parameters - field = input.wordmap_field() - ngram = input.ngram_fa() if field in ["TI", "AB"] else 1 - - file_upload_terms_wm = None - file_upload_synonyms_wm = None - terms_data_wm = None - synonyms_data_wm = None - - if input.remove_terms_wm(): - file_upload_terms_wm: list[FileInfo] | None = input.upload_terms_wm() - if file_upload_terms_wm: - with open(file_upload_terms_wm[0]['datapath'], 'r', encoding='utf-8') as file: - terms_data_wm = pd.DataFrame([line.strip() for line in file], columns=["Terms"]) - else: - # Reset terms file if checkbox is disabled - file_upload_terms_wm = None - terms_data_wm = None - - if input.remove_synonyms_wm(): - file_upload_synonyms_wm: list[FileInfo] | None = input.upload_synonyms_wm() - if file_upload_synonyms_wm: - with open(file_upload_synonyms_wm[0]['datapath'], 'r', encoding='utf-8') as file: - synonyms_data_wm = pd.DataFrame([line.strip() for line in file], columns=["Synonyms"]) - else: - # Reset synonyms file if checkbox is disabled - file_upload_synonyms_wm = None - synonyms_data_wm = None - - # Show modal for terms/synonyms if needed - modal_content_wm = [] - - if terms_data_wm is not None: - modal_content_wm.append(ui.markdown("""

Terms to Remove

""")) - modal_content_wm.append(ui.HTML(DT(terms_data_wm))) - - if synonyms_data_wm is not None: - modal_content_wm.append(ui.markdown("""

Synonyms to Remove

""")) - modal_content_wm.append(ui.HTML(DT(synonyms_data_wm))) - - n_terms=input.wordmap_n_terms() - n_clusters=input.wordmap_n_clusters() - num_documents=(input.wordmap_n_docs() or None) - method=input.wordmap_method() - dimX=input.wordmap_dimX() - dimY=input.wordmap_dimY() - topWordPlot=input.wordmap_top_words() - threshold=input.wordmap_threshold() - labelsize=input.wordmap_labelsize() - size=input.wordmap_dot_size() - - result = get_factorial_analysis(df, ngram, field, terms_data_wm, synonyms_data_wm, n_terms, n_clusters, num_documents, method, dimX, dimY, topWordPlot, threshold, labelsize, size) - factorial_analysis_results.set(result) - except Exception as e: - ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) - finally: - ui.modal_remove() - - # Show modal for terms/synonyms after main processing if needed - if modal_content_wm: - file_modal_wm = ui.modal( - *modal_content_wm, - easy_close=True, - footer=ui.modal_button("Close", style="background: #5865B9; color: white") - ) - ui.modal_show(file_modal_wm) - - with ui.navset_underline(): - with ui.nav_panel("Word Map"): - @render.ui - def show_word_map_placeholder(): - result = factorial_analysis_results.get() - if result is not None: - return None # Hide placeholder when data is available - else: - return ui.div( - ui.p("Click the Run Analysis button to run factorial analysis", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - @render_widget - def word_map_widget(): - result = factorial_analysis_results.get() - if result is not None: - plot_word_map, _, _, _ = result - return plot_word_map - return None - - with ui.nav_panel("Topic Dendrogram"): - @render.ui - def show_topic_dendrogram(): - result = factorial_analysis_results.get() - if result is not None: - _, plot_dendrogram, _, _ = result - return ui.HTML(f'') - else: - return ui.div( - ui.p("Click the Run Analysis button to run factorial analysis", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Words by Cluster"): - @render.ui - def show_words_by_cluster(): - result = factorial_analysis_results.get() - if result is not None: - _, _, words_by_cluster, _ = result - return ui.HTML(DT(words_by_cluster, style="width=100%;")) - else: - return ui.div( - ui.p("Click the Run Analysis button to run factorial analysis", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Articles by Cluster"): - @render.ui - def show_articles_by_cluster(): - result = factorial_analysis_results.get() - if result is not None: - _, _, _, articles_by_cluster = result - return ui.HTML(DT(articles_by_cluster, style="width=100%;")) - else: - return ui.div( - ui.p("Click the Run Analysis button to run factorial analysis", style="text-align: center; color: #999; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - # --- Co-citation Network Section --- - with ui.nav_panel("None", value="co-citation-network"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("📊 Co-citation Network", style="color: #5567BB;") - ui.p("The co-citation network of the dataset") - - # Reactive value to store analysis results - co_citation_results = reactive.Value(None) - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_co_citation_network", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("co_citation_network_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"CoCitationNetwork-{todaydate}.png" - ) - def download_co_citation_network(): - plot_co_citation_network, _, _, _ = co_citation_network_results.get() - yield html_download( - plot_co_citation_network, - title="Co-Citation Network", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.co_citation_network_report) - def show_co_citation_network_report(): - co_citation_net, density_plot, co_citation_tab, degree_plot = co_citation_network_results.get() - report_excel.set(add_to_report(report_choices, report_excel, [co_citation_tab], [co_citation_net, density_plot, degree_plot], "cocitnet")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Co-Citation Network added to report", duration=5, close_button=False) - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=True): - # Initialize reactive values for results - co_citation_network_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_co_citation_network) - def run_co_citation_network_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "🔗 Processing co-citation network...", - "📊 Analyzing citations relationships...", - "🌐 Building citation network structure...", - "📈 Computing network layouts...", - "✨ Almost there! Preparing your network...", - "🎯 Finalizing network visualization...", - "🔗 Connecting citations and references...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - # Get parameters - field = input.co_citation_field() - sep = input.citSep() - cocit_network_layout = input.cocit_network_layout() - cocit_clustering_algorithm = input.cocit_clustering_algorithm() - cocit_repulsion = input.cocit_repulsion() - cocit_shape = input.cocit_shape() - cocit_shadow = input.cocit_shadow() - cocit_curved = input.cocit_curved() - citlabelsize = input.citlabelsize() - citedgesize = input.citedgesize() - citlabel_cex = input.citlabel_cex() - citNodes = input.citNodes() - cit_isolates = input.cit_isolates() - citedges_min = input.citedges_min() - - # Execute analysis - result = get_co_citation( - df=df, - field=field, - sep=sep, - cocit_network_layout=cocit_network_layout, - cocit_clustering_algorithm=cocit_clustering_algorithm, - cocit_repulsion=cocit_repulsion, - cocit_shape=cocit_shape, - cocit_shadow=cocit_shadow, - cocit_curved=cocit_curved, - citlabelsize=citlabelsize, - citedgesize=citedgesize, - citlabel_cex=citlabel_cex, - citNodes=citNodes, - cit_isolates=cit_isolates, - citedges_min=citedges_min - ) - - # Store results - co_citation_network_results.set(result) - except Exception as e: - ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) - finally: - ui.modal_remove() - - with ui.sidebar(id="sidebar_co_citation_network", position="right"): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the co-citation network.") - ui.input_select("co_citation_field", "Field", - { - "CR": "Papers", - "CR_AU": "Authors", - "CR_SO": "Sources" - }, - selected="CR" - ) - ui.input_select("citSep", "Field separator character", - { - ";": '";" (Semicolon)', - ". ": '". " (Dot and 3 or more spaces)', - ",": '"," (Comma)' - }, - selected=";" - ) - - with ui.accordion(id="accordion_co_citation_network", multiple=True, open=False): - with ui.accordion_panel("Method Parameters"): - with ui.layout_column_wrap(width=1 / 2): - ui.input_select("cocit_network_layout", "Network Layout", {"auto": "Automatic layout", "fr": "Fruchterman-Reingold", "kk": "Kamada-Kawai", "circle": "Circle", "mdrl": "Multi Dimension Scaling", "graphopt": "Graphopt", "sphere": "Sphere", "star": "Star"}, selected="auto") - ui.input_select("cocit_clustering_algorithm", "Clustering Algorithm", {"none": "None", "edge_betweenness": "Edge Betweenness", "infomap": "InfoMap", "leading_eigen": "Leading Eigenvalues", "leiden": "Leiden", "louvain": "Louvain", "spinglass": "Spinglass", "walktrap": "Walktrap"}, selected="walktrap") - - with ui.layout_column_wrap(width=1 / 2): - ui.input_numeric("citNodes", "Number of Nodes", min=5, max=1000, value=50, step=1) - ui.input_numeric("cocit_repulsion", "Repulsion Force", min=0, max=1, value=0.1, step=0.1) - - with ui.layout_column_wrap(width=1 / 2): - ui.input_select("cit_isolates", "Remove Isolated Nodes", {"yes": "Yes", "no": "No"}, selected="yes") - ui.input_numeric("citedges_min", "Minimum Number of Edges", value=2, step=1, min=0) - - with ui.accordion_panel("Graphical Parameters"): - with ui.layout_column_wrap(width=1 / 2): - ui.input_select( - "citShortlabel", - "Short Label", - {"Yes": "Yes", "No": "No"}, - selected="Yes" - ) - ui.input_numeric( - "citLabels", - "Number of labels", - min=0, - max=1000, - value=1000, - step=1 - ) - with ui.layout_column_wrap(width=1 / 2): - ui.input_select( - "citlabel_cex", - "Label cex", - {"Yes": "Yes", "No": "No"}, - selected="Yes" - ) - ui.input_select( - "cocit_shape", - "Node Shape", - { - "box": "Box", - "circle": "Circle", - "dot": "Dot", - "ellipse": "Ellipse", - "square": "Square", - "text": "Text" - }, - selected="dot" - ) - with ui.layout_column_wrap(width=1 / 2): - ui.input_numeric( - "citlabelsize", - "Label size", - min=0.0, - max=20, - value=2, - step=0.10 - ) - ui.input_numeric( - "citedgesize", - "Edge size", - min=0.5, - max=20, - value=2, - step=0.5 - ) - with ui.layout_column_wrap(width=1 / 2): - ui.input_select( - "cocit_shadow", - "Node shadow", - {"Yes": "Yes", "No": "No"}, - selected="Yes" - ) - ui.input_select( - "cocit_curved", - "Edit Nodes", - {"Yes": "Yes", "No": "No"}, - selected="No" - ) - - with ui.navset_underline(): - with ui.nav_panel("Network"): - @render.ui - def show_cocitation_network(): - result = co_citation_network_results.get() - if result is not None: - plot_cocit, _, _, _ = result - return ui.HTML(f'') - else: - return ui.div( - ui.p("Click the Run Analysis button to generate the co-citation network.", style="text-align: center; color: #666; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - - with ui.nav_panel("Density"): - @render_widget - def show_cocitation_density_table(): - result = co_citation_network_results.get() - if result is not None: - _, density_plot, _, _ = result - return density_plot - else: - return ui.div( - ui.p("Click the Run Analysis button to generate the density plot.", style="text-align: center; color: #666; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Table"): - @render.ui - def show_cocitation_table(): - result = co_citation_network_results.get() - if result is not None: - _, _, cocit_table, _ = result - return ui.HTML(DT(cocit_table, style="width=100%;")) - else: - return ui.div( - ui.p("Click the Run Analysis button to generate the co-citation table.", style="text-align: center; color: #666; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Degree Plot"): - @render_widget - def show_cocitation_clusters(): - result = co_citation_network_results.get() - if result is not None: - _, _, _, degree_plot = result - return degree_plot - else: - return ui.div( - ui.p("Click the Run Analysis button to generate the degree plot.", style="text-align: center; color: #666; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - # --- Historiograph Section --- - with ui.nav_panel("None", value="historiograph"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("📊 Historiograph", style="color: #5567BB;") - ui.p("The historiograph of the dataset") - - # Reactive value to store analysis results - historiograph_results = reactive.Value(None) - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_historiograph", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("historiograph_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"Historiograph-{todaydate}.png" - ) - def download_historiograph(): - result = historiograph_results.get() - if result is not None: - _, _, plot_historiograph = result - yield html_download( - plot_historiograph, - title="Historiograph", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.historiograph_report) - def show_historiograph_report(): - result = historiograph_results.get() - if result is not None: - _, historiograph_tab, historiograph_net = result - report_excel.set(add_to_report(report_choices, report_excel, [historiograph_tab], [historiograph_net], "historiograph")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Historiograph added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first by clicking the Run Analysis button", duration=5, close_button=False, type="warning") - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=True): - # Initialize reactive values for results - historiograph_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_historiograph) - def run_historiograph_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "📚 Processing historiograph...", - "📊 Analyzing citation chronology...", - "🌐 Building historical network...", - "📈 Computing temporal layouts...", - "✨ Almost there! Preparing your timeline...", - "🎯 Finalizing historical visualization...", - "🔗 Connecting historical citations...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - # Get parameters - histNodes = input.histNodes() - histlabelsize = input.histlabelsize() - histsize = input.histsize() - # Execute analysis with correct parameters - result = get_historiograph( - df=df, - node_label="AU1", - histNodes=histNodes, - hist_isolates=True, - histlabelsize=histlabelsize, - histsize=histsize, - sep=";" - ) - - # Store results - historiograph_results.set(result) - except Exception as e: - ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) - finally: - ui.modal_remove() - - with ui.sidebar(id="sidebar_historiograph", position="right"): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the historiograph.") - ui.input_numeric( - "histNodes", - "Number of Nodes", - min=5, - max=100, - value=20, - step=1 - ) - - ui.markdown("**Graphical Parameters**") - ui.input_select( - "titlelabel", - "Node label", - { - "AU1": "Short id (1st Author, Year)", - "TI": "Document Title", - "ID": "Authors' Keywords", - "DE": "Keywords Plus" - }, - selected="AU1" - ) - - ui.input_select( - "hist_isolates", - "Remove Isolated Nodes", - {"True": "Yes", "False": "No"}, - selected="True" - ) - - with ui.layout_column_wrap(width=1/2): - ui.input_numeric( - "histlabelsize", - "Label size", - min=0.0, - max=20, - value=20, - step=1 - ) - ui.input_numeric( - "histsize", - "Node size", - min=0, - max=20, - value=20, - step=1 - ) - - with ui.navset_underline(): - with ui.nav_panel("Network"): - @render.ui - def show_hist_network(): - result = historiograph_results.get() - if result is not None: - _, _, html_path = result - return ui.HTML(f'') - else: - return ui.div( - ui.p("Click the Run Analysis button to generate the historiograph network.", style="text-align: center; color: #666; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Table"): - @render.ui - def show_hist_table(): - result = historiograph_results.get() - if result is not None: - _, hist_tab, _ = result - return ui.HTML(DT(hist_tab, style="width=100%;")) - else: - return ui.div( - ui.p("Click the Run Analysis button to generate the historiograph table.", style="text-align: center; color: #666; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - # --- Collaboration Network Section --- - with ui.nav_panel("None", value="collaboration_network"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("📊 Collaboration Network", style="color: #5567BB;") - ui.p("The collaboration network of the dataset") - - # Reactive value to store analysis results - collaboration_network_results = reactive.Value(None) - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_collaboration_network", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("collaboration_network_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"CollaborationNetwork-{todaydate}.png" - ) - def download_collaboration_network(): - result = collaboration_network_results.get() - if result is not None: - plot_collaboration_network, _, _, _ = result - yield html_download( - plot_collaboration_network, - title="Collaboration Network", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.collaboration_network_report) - def show_collaboration_network_report(): - result = collaboration_network_results.get() - if result is not None: - collaboration_net, collaboration_density_plot, collaboration_network_tab, collaboration_degree_plot = result - report_excel.set(add_to_report(report_choices, report_excel, [collaboration_network_tab], [collaboration_net, collaboration_density_plot, collaboration_degree_plot], "collabnet")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Collaboration Network added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first by clicking the Run Analysis button", duration=5, close_button=False, type="warning") - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Initialize reactive values for results - collaboration_network_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_collaboration_network) - def run_collaboration_network_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "🤝 Processing collaboration network...", - "📊 Analyzing research collaborations...", - "🌐 Building collaboration structure...", - "📈 Computing network layouts...", - "✨ Almost there! Preparing your network...", - "🎯 Finalizing collaboration visualization...", - "🔗 Connecting authors and institutions...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - # Get parameters - field = input.collab_field() - network_layout = input.collab_network_layout() - clustering_algorithm = input.collab_clustering_algorithm() - repulsion = input.collab_repulsion() - shape = input.collab_shape() - # Set default values for missing parameters - opacity = 1.0 - shadow = "Yes" - curved = "No" - colnormalize = "none" - labelsize = 2.0 - edgesize = 2.0 - label_cex = "Yes" - nodes = 50 - isolates = "yes" - edges_min = 2 - - # Execute analysis - result = get_collaboration_network( - df=df, - field=field, - network_layout=network_layout, - clustering_algorithm=clustering_algorithm, - repulsion=repulsion, - shape=shape, - opacity=opacity, - shadow=shadow, - curved=curved, - colnormalize=colnormalize, - labelsize=labelsize, - edgesize=edgesize, - label_cex=label_cex, - nodes=nodes, - isolates=isolates, - edges_min=edges_min - ) - - # Store results - collaboration_network_results.set(result) - except Exception as e: - ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) - finally: - ui.modal_remove() - - with ui.sidebar(id="sidebar_collaboration_network", position="right"): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the collaboration network.") - ui.input_select("collab_field", "Field", - { - "COL_AU": "Authors", - "COL_UN": "Institutions", - "COL_CO": "Countries" - }, - selected="COL_AU" - ) - - ui.input_select( - "colnormalize", - "Normalization", - { - "none": "None", - "association": "Association", - "jaccard": "Jaccard", - "salton": "Salton", - "inclusion": "Inclusion", - "equivalence": "Equivalence" - }, - selected="association" - ) - - with ui.accordion(id="accordion_collaboration_network", multiple=True, open=False): - with ui.accordion_panel("Method Parameters"): - with ui.layout_column_wrap(width=1 / 2): - ui.input_select("collab_network_layout", "Network Layout", {"auto": "Automatic layout", "fr": "Fruchterman-Reingold", "kk": "Kamada-Kawai", "circle": "Circle", "mdrl": "Multi Dimension Scaling", "graphopt": "Graphopt", "sphere": "Sphere", "star": "Star"}, selected="auto") - ui.input_select("collab_clustering_algorithm", "Clustering Algorithm", {"none": "None", "edge_betweenness": "Edge Betweenness", "infomap": "InfoMap", "leading_eigen": "Leading Eigenvalues", "leiden": "Leiden", "louvain": "Louvain", "spinglass": "Spinglass", "walktrap": "Walktrap"}, selected="walktrap") - - with ui.layout_column_wrap(width=1 / 2): - ui.input_numeric("collabNodes", "Number of Nodes", min=5, max=1000, value=50, step=1) - ui.input_numeric("collab_repulsion", "Repulsion Force", min=0, max=1, value=0.1, step=0.1) - - with ui.layout_column_wrap(width=1 / 2): - ui.input_select("collab_isolates", "Remove Isolated Nodes", {"yes": "Yes", "no": "No"}, selected="yes") - ui.input_numeric("collabedges_min", "Minimum Number of Edges", value=2, step=1, min=0) - - with ui.accordion_panel("Graphical Parameters"): - with ui.layout_column_wrap(width=1 / 2): - ui.input_numeric( - "collab_opacity", - "Opacity", - min=0, - max=1, - value=0.7, - step=0.05 - ) - ui.input_select( - "collabShortlabel", - "Short Label", - {"Yes": "Yes", "No": "No"}, - selected="Yes" - ) - - ui.input_numeric( - "collabLabels", - "Number of labels", - min=0, - max=1000, - value=1000, - step=1 - ) - - with ui.layout_column_wrap(width=1 / 2): - ui.input_select( - "collablabel_cex", - "Label cex", - {"Yes": "Yes", "No": "No"}, - selected="Yes" - ) - ui.input_select( - "collab_shape", - "Node Shape", - { - "box": "Box", - "circle": "Circle", - "dot": "Dot", - "ellipse": "Ellipse", - "square": "Square", - "text": "Text" - }, - selected="dot" - ) - with ui.layout_column_wrap(width=1 / 2): - ui.input_numeric( - "collablabelsize", - "Label size", - min=0.0, - max=20, - value=2, - step=0.10 - ) - ui.input_numeric( - "collabedgesize", - "Edge size", - min=0.5, - max=20, - value=2, - step=0.5 - ) - with ui.layout_column_wrap(width=1 / 2): - ui.input_select( - "collab_shadow", - "Node shadow", - {"Yes": "Yes", "No": "No"}, - selected="Yes" - ) - ui.input_select( - "collab_curved", - "Edit Nodes", - {"Yes": "Yes", "No": "No"}, - selected="No" - ) - - with ui.navset_underline(): - with ui.nav_panel("Network"): - @render.ui - def show_collaboration_network(): - result = collaboration_network_results.get() - if result is not None: - plot_collab, _, _, _ = result - return ui.HTML(f'') - else: - return ui.div( - ui.p("Click the Run Analysis button to generate the collaboration network.", style="text-align: center; color: #666; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Density"): - @render_widget - def show_collaboration_density_table(): - result = collaboration_network_results.get() - if result is not None: - _, density_plot, _, _ = result - return density_plot - else: - return ui.div( - ui.p("Click the Run Analysis button to generate the density plot.", style="text-align: center; color: #666; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Table"): - @render.ui - def show_collaboration_table(): - result = collaboration_network_results.get() - if result is not None: - _, _, collab_table, _ = result - return ui.HTML(DT(collab_table, style="width=100%;")) - else: - return ui.div( - ui.p("Click the Run Analysis button to generate the collaboration table.", style="text-align: center; color: #666; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - with ui.nav_panel("Degree Plot"): - @render_widget - def show_collaboration_clusters(): - result = collaboration_network_results.get() - if result is not None: - _, _, _, degree_plot = result - return degree_plot - else: - return ui.div( - ui.p("Click the Run Analysis button to generate the degree plot.", style="text-align: center; color: #666; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - # --- Countries Collaboration Network Section --- - with ui.nav_panel("None", value="countries_collaboration_network"): - with ui.layout_columns( - col_widths=(8, 4), - style="margin-bottom: -21px;" - ): - with ui.tags.div(style="flex: 1; bottom: 0px;"): - ui.h3("📊 Countries Collaboration Network", style="color: #5567BB;") - ui.p("The countries collaboration network of the dataset") - - with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): - ui.input_action_button("run_countries_collaboration_network", "Run Analysis", icon=ICONS["play"]) - ui.input_action_button("countries_collaboration_network_report", "Add in Report", icon=ICONS["plus"]) - - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - @render.download( - label='💾 Download', - filename=f"CountriesCollaborationNetwork-{todaydate}.png" - ) - def download_countries_collaboration_network(): - result = countries_collaboration_network_results.get() - if result is not None: - plot_countries_map, _ = result - yield plotly_download( - plot_countries_map, - title="Countries Collaboration Network", - height=height.get(), - dpi=dpi.get() - ) - - @render.ui - @reactive.event(input.countries_collaboration_network_report) - def show_countries_collaboration_network_report(): - result = countries_collaboration_network_results.get() - if result is not None: - plot_countries_map, countries_collaboration_tab = result - report_excel.set(add_to_report(report_choices, report_excel, [countries_collaboration_tab], [plot_countries_map], "collabworldmap")) - selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) - return ui.notification_show("✅ Countries Collaboration Network added to report", duration=5, close_button=False) - else: - return ui.notification_show("⚠️ Please run the analysis first by clicking the Run Analysis button", duration=5, close_button=False, type="warning") - - with ui.card(full_screen=True): - with ui.layout_sidebar(fillable=False, fill=False): - # Initialize reactive values for results - countries_collaboration_network_results = reactive.Value(None) - - @reactive.effect - @reactive.event(input.run_countries_collaboration_network) - def run_countries_collaboration_network_analysis(): - # Show loading modal while calculating (same style as Sources Production over Time) - def loading_modal(): - phrases = [ - "⏳ Loading... Please wait.", - "🌍 Processing countries collaboration network...", - "📊 Analyzing international collaborations...", - "🌐 Building world map network...", - "📈 Computing geographical layouts...", - "✨ Almost there! Preparing your world map...", - "🎯 Finalizing global visualization...", - "🔗 Connecting countries and collaborations...", - "🌐 Exploring your scientific landscape...", - "🚀 Science mapping in progress...", - ] - modal = ui.modal( - ui.div( - ui.img( - src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", - height="150px", - style="display: block; margin: 0 auto; text-align: center;", - ), - ui.h4( - phrases[0], - id="loading-phrase", - style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", - ), - ), - easy_close=False, - footer=None, - ) - js = f""" - - """ - return ui.HTML(str(modal) + js) - - ui.modal_show(loading_modal()) - try: - # Execute analysis (with default parameters for world map collaboration) - result = get_world_map_collaboration( - df=df, - edges_min=1, - edgesize=5 - ) - - # Store results - countries_collaboration_network_results.set(result) - except Exception as e: - ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) - finally: - ui.modal_remove() - - with ui.sidebar(id="sidebar_countries_collaboration_network", position="right"): - ui.h4("Parameters", style="color: #5567BB;") - ui.p("Select the parameters for the countries collaboration network.") - ui.markdown("Method Parameters:") - ui.input_numeric( - "WMedges_min", - label="Min edges", - value=2, - step=1 - ) - ui.input_numeric( - "WMedgesize", - label="Edge size", - min=0.1, - max=20, - value=5 - ) - - with ui.navset_underline(): - with ui.nav_panel("World Map"): - @render.ui - def show_world_map_placeholder(): - result = countries_collaboration_network_results.get() - if result is None: - return ui.div( - ui.p("Click the Run Analysis button to generate the world map collaboration.", style="text-align: center; color: #666; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - else: - return ui.div() # Empty when there's data - - @render_widget - def show_world_map_collaboration(): - result = countries_collaboration_network_results.get() - if result is not None: - plot_world_map, _ = result - return plot_world_map - else: - return None - - with ui.nav_panel("Table"): - @render.ui - def show_world_map_collaboration_table(): - result = countries_collaboration_network_results.get() - if result is not None: - _, world_map_table = result - return ui.HTML(DT(world_map_table, style="width=100%;")) - else: - return ui.div( - ui.p("Click the Run Analysis button to generate the world map collaboration table.", style="text-align: center; color: #666; font-size: 16px;"), - style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" - ) - - # --- Report Section --- - with ui.nav_panel("None", value="report"): - ui.h3("⚙️ Report", style="color: #5567BB;") - ui.p("The report of the analysis") - - with ui.card(full_screen=True): - with ui.layout_column_wrap(width=1 / 2): - with ui.card(): - ui.card_header("Select results to include in the Report", style="background: #4379cd; color: white;") - choices = {"empty": ui.span("Empty Report")} - ui.input_checkbox_group( - "report_selection", - "", - choices=choices, - selected=["empty"], - ) - - @reactive.effect - def update_selection(): - user_selection = input.report_selection() - selection.set(user_selection) - - @render.ui - @reactive.event(input.go_report) - def show_report_selection(): - choices = report_choices.get() if report_choices.get() != {} else {"empty": ui.span("Empty Report")} - return ui.update_checkbox_group( - "report_selection", - choices=choices, - selected=selection.get(), - ) - - @render.ui - @reactive.event(input.select_all) - def select_all_report(): - selected_choices = choices if report_choices.get() == {} else report_choices.get() - selection.set(list(selected_choices.keys())) - return ui.update_checkbox_group( - "report_selection", - selected=selection.get(), - ) - - @render.ui - @reactive.event(input.deselect_all) - def deselect_all_report(): - selection.set([]) - return ui.update_checkbox_group( - "report_selection", - selected=selection.get(), - ) - - @render.ui - @reactive.event(input.delete) - def delete_report(): - report_choices.set({}) - report_excel.set(io.BytesIO()) - selection.set(["empty"]) - return ui.update_checkbox_group( - "report_selection", - choices={"empty": ui.span("Empty Report")}, - selected=selection.get(), - ) - - with ui.layout_column_wrap(width=1 / 1): - ui.input_action_button("select_all", "Select All", width="50%", icon=ICONS["plus"], class_="btn btn-primary") - ui.input_action_button("deselect_all", "Deselect All", width="50%", icon=ICONS["minus"], class_="btn btn-primary") - ui.input_action_button("delete", "Delete Report", width="50%", icon=ICONS["delete"], class_="btn btn-danger") - - with ui.layout_columns( - col_widths={"sm": (6, 6)}, - ): - with ui.tags.div(style="flex: 1;"): - @render.express() - @reactive.event(input.go_report, input.delete) - def show_download_report(): - todaydate = datetime.today() - todaydate = todaydate.strftime("%Y-%m-%d") - if report_excel.get().getbuffer().nbytes > 0: - @render.download(label="💾 Generate Report", filename=f"BiblioshinyReport-{todaydate}.xlsx") - def download_report(): - report = report_excel.get() - yield report.getvalue() - - # --- Settings Section --- - with ui.nav_panel("None", value="settings"): - ui.h3("⚙️ Settings", style="color: #5567BB;") - - with ui.card(full_screen=True, style="margin-top: 20px;"): - ui.h4("✨ Biblio AI API Key:") - ui.markdown("Set a valid API Key to use 'Biblio AI' features powered by Google Gemini. If you don’t have one yet, you can generate it by logging into [AI Studio](https://aistudio.google.com/app/apikey) with your Google account and creating a new API Key.") - ui.input_password("gemini_api_key", "Enter your Gemini API Key:", placeholder="Enter your Gemini API key here", width="50%") - - @render.ui - @reactive.event(input.save_api) - def save_api_key(): - if len(input.gemini_api_key())>=10: - gemini_api_key.set(input.gemini_api_key()) - masked = "*" * (len(input.gemini_api_key()) - 4) + input.gemini_api_key()[-4:] - return ui.markdown(f"✅ API Key has been set: {masked}") - else: - return ui.markdown("❌ API Key seems to be not valid") - - @render.express() - @reactive.event(input.reset_api) - def reset_api_key(): - _ = gemini_api_key.set("") - - with ui.layout_columns( - col_widths={"sm": (12)}, - ): - with ui.tags.div(style="flex: 1;"): - ui.input_action_button("save_api", "Set API Key", icon=ICONS["save"]) - ui.input_action_button("reset_api", "Remove API Key", icon=ICONS["delete"]) - - ui.hr() - ui.h4("📊 Plot settings:") - ui.input_slider("plot_dpi", "Please select the desired DPI", sep="", ticks=True, min=75, max=600, value=300, step=15) - ui.input_slider("plot_height", "Please select the desired height in inches", sep="", ticks=True, min=5, max=15, value=7, step=1) - - @reactive.calc - def plot_settings(): - # Get the values from the inputs - dpi.set(input.plot_dpi()) - height.set(input.plot_height()) - - @render.express() - @reactive.event(input.plot_dpi, input.plot_height, input.gemini_api_key) - def update_plot_settings(): - plot_settings() - - -# --- Sidebar Management --- -@render.express() -@reactive.event(input.start_button) -def toggle_sidebar(): - with ui.tags.div(id="sidebar_2", class_="custom-sidebar"): - with ui.accordion(id="sidebar_accordion_data", multiple=False, open=False): - # Info Section - with ui.accordion_panel("Biblioshiny", icon=ICONS["home_colored"]): - ui.input_action_button("go_about_2", "Biblioshiny", class_="sidebar-button", icon=ICONS["home"]) - # Data Section - with ui.accordion_panel("Data", icon=ICONS["database_colored"]): - ui.input_action_button("go_import_2", "Import or Load", class_="sidebar-button", icon=ICONS["data"]) - ui.input_action_button("go_api_2", "API", class_="sidebar-button", icon=ICONS["api"]) - ui.input_action_button("go_collections_2", "Merge Collection", class_="sidebar-button", icon=ICONS["merge"]) - - # Filters Section - with ui.accordion_panel("Filters", icon=ICONS["filters_colored"]): - ui.input_action_button("go_filters", "Filters", class_="sidebar-button", icon=ICONS["filters"]) - - # Analysis Section - with ui.accordion_panel("Overview", icon=ICONS["play_colored"]): - ui.input_action_button("go_main", "Main Information", class_="sidebar-button", icon=ICONS["overview"]) - ui.input_action_button("go_annual_scientific_production", "Annual Scientific Production", class_="sidebar-button", icon=ICONS["annual_growth_rate"]) - ui.input_action_button("go_average_citations_per_year", "Average Citations per Year", class_="sidebar-button", icon=ICONS["average_citations_per_doc"]) - ui.input_action_button("go_three_field_plot", "Three-Field Plot", class_="sidebar-button", icon=ICONS["overview"]) - with ui.accordion_panel("Sources", icon=ICONS["sources_colored"]): - ui.input_action_button("go_most_relevant_sources", "Most Relevant Sources", class_="sidebar-button", icon=ICONS["book_open"] if "book_open" in ICONS else ICONS["sources"]), - ui.input_action_button("go_most_local_cited_sources", "Most Local Cited Sources", class_="sidebar-button", icon=ICONS["book"] if "book" in ICONS else ICONS["sources"]), - ui.input_action_button("go_bradfords_law", "Bradford's Law", class_="sidebar-button", icon=ICONS["annual_growth_rate"]), - ui.input_action_button("go_sources_local_impact", "Sources' Local Impact", class_="sidebar-button", icon=ICONS["star"] if "star" in ICONS else ICONS["sources"]), - ui.input_action_button("go_sources_production_over_time", "Sources' Production over Time", class_="sidebar-button", icon=ICONS["calendar"] if "calendar" in ICONS else ICONS["timespan"]), - with ui.accordion_panel("Authors", icon=ICONS["authors_colored"]): - # Authors Section - ui.span("Authors", style="color: gray;") - ui.input_action_button("go_most_relevant_authors", "Most Relevant Authors", class_="sidebar-button", icon=ICONS["authors"]) - ui.input_action_button("go_most_local_cited_authors", "Most Local Cited Authors", class_="sidebar-button", icon=ICONS["authors_single_authored_docs"]) - ui.input_action_button("go_authors_production_over_time", "Authors' Production over Time", class_="sidebar-button", icon=ICONS["annual_growth_rate"]) - ui.input_action_button("go_lotkas_law", "Lotka's Law", class_="sidebar-button", icon=ICONS["overview"]) - ui.input_action_button("go_authors_local_impact", "Authors' Local Impact", class_="sidebar-button", icon=ICONS["star"] if "star" in ICONS else ICONS["authors"]) - # Affiliations Section - ui.span("Affiliations", style="color: gray;") - ui.input_action_button("go_most_relevant_affiliations", "Most Relevant Affiliations", class_="sidebar-button", icon=ICONS["database"]) - ui.input_action_button("go_affiliations_production_over_time", "Affiliations' Production over Time", class_="sidebar-button", icon=ICONS["annual_growth_rate"]) - # Countries Section - ui.span("Countries", style="color: gray;") - ui.input_action_button("go_corresponding_authors_countries", "Corresponding Author's Countries", class_="sidebar-button", icon=ICONS["international_co_authorship"]) - ui.input_action_button("go_countries_scientific_production", "Countries' Scientific Production", class_="sidebar-button", icon=ICONS["international_co_authorship"]) - ui.input_action_button("go_countries_production_over_time", "Countries' Production over Time", class_="sidebar-button", icon=ICONS["annual_growth_rate"]) - ui.input_action_button("go_most_cited_countries", "Most Cited Countries", class_="sidebar-button", icon=ICONS["book"]) - with ui.accordion_panel("Documents", icon=ICONS["documents_colored"]): - # Documents Section - ui.span("Documents", style="color: gray;") - ui.input_action_button("go_most_global_cited_documents", "Most Global Cited Documents", class_="sidebar-button", icon=ICONS["documents"]) - ui.input_action_button("go_most_local_cited_documents", "Most Local Cited Documents", class_="sidebar-button", icon=ICONS["documents"]) - - # Cited References Section - ui.span("Cited References", style="color: gray;") - ui.input_action_button("go_most_local_cited_references", "Most Local Cited References", class_="sidebar-button", icon=ICONS["references"]) - ui.input_action_button("go_references_spectroscopy", "References Spectroscopy", class_="sidebar-button", icon=ICONS["references"]) - - # Words Section - ui.span("Words", style="color: gray;") - ui.input_action_button("go_most_frequent_words", "Most Frequent Words", class_="sidebar-button", icon=ICONS["authors_keywords_de"]) - ui.input_action_button("go_wordcloud", "WordCloud", class_="sidebar-button", icon=ICONS["authors_keywords_de"]) - ui.input_action_button("go_treemap", "TreeMap", class_="sidebar-button", icon=ICONS["overview"]) - ui.input_action_button("go_words_frequency_over_time", "Words' Frequency over Time", class_="sidebar-button", icon=ICONS["annual_growth_rate"]) - ui.input_action_button("go_trend_topics", "Trend Topics", class_="sidebar-button", icon=ICONS["annual_growth_rate"]) - - with ui.accordion_panel("Clustering", icon=ICONS["clustering_colored"]): - ui.input_action_button("go_clustering", "Clustering", class_="sidebar-button", icon=ICONS["clustering"]) - - with ui.accordion_panel("Conceptual Structure", icon=ICONS["conceptual_structure_colored"]): - ui.span("Network Approach", style="color: gray;") - ui.input_action_button("go_cooccurrence_network", "Co-occurrence Network", class_="sidebar-button", icon=ICONS["clustering"]) - ui.input_action_button("go_thematic_map", "Thematic Map", class_="sidebar-button", icon=ICONS["overview"]) - ui.input_action_button("go_thematic_evolution", "Thematic Evolution", class_="sidebar-button", icon=ICONS["annual_growth_rate"]) - - ui.span("Factorial Approach", style="color: gray;") - ui.input_action_button("go_factorial_analysis", "Factorial Analysis", class_="sidebar-button", icon=ICONS["overview"]) - - with ui.accordion_panel("Intellectual Structure", icon=ICONS["intellectual_structure_colored"]): - ui.input_action_button("go_citation_network", "Citation Network", class_="sidebar-button", icon=ICONS["references"]) - ui.input_action_button("historiograph", "Historiograph", class_="sidebar-button", icon=ICONS["annual_growth_rate"]) - - with ui.accordion_panel("Social Structure", icon=ICONS["social_structure_colored"]): - ui.input_action_button("go_collaboration_network", "Collaboration Network", class_="sidebar-button", icon=ICONS["co_authors_per_doc"]) - ui.input_action_button("go_countries_collaboration_network", "Countries Collaboration Network", class_="sidebar-button", icon=ICONS["international_co_authorship"]) - - with ui.accordion_panel("Report", icon=ICONS["report_colored"]): - ui.input_action_button("go_report", "Report", class_="sidebar-button", icon=ICONS["report"]) - with ui.accordion_panel("Settings", icon=ICONS["settings_colored"]): - ui.input_action_button("go_settings", "Settings", class_="sidebar-button", icon=ICONS["settings"]) - - # --- Footer --- - # Use static positioning and margin-top to avoid overlap with accordion content - with ui.tags.footer( - class_="custom-footer", - style=( - "background: #5567BB; color: white; text-align: center; padding: 10px 0;" - "position: static; width: 300px; z-index: 1000; margin-top: 30px;" - "transition: background-color 0.3s ease;" - ), - ): - ui.markdown( - """ -
- © 2025 - - - - - Bibliometrix - -
-

Version: 1.0.0 - Shiny for Python Based Application

- """ - ) - - -# --- Javascript for Sidebar --- -ui.tags.script(""" - // Helper to show/hide both sidebars in a coordinated way - function setSidebarState(show) { - const sidebar = document.getElementById("sidebar"); - const sidebar_2 = document.getElementById("sidebar_2"); - const content = document.getElementById("mainContent"); - if (sidebar) sidebar.classList.toggle("sidebar-hidden", !show); - if (sidebar_2) sidebar_2.classList.toggle("sidebar-hidden", !show); - if (content) content.classList.toggle("full-width", !show); - } - - // Hide sidebars on page load - document.addEventListener("DOMContentLoaded", function() { - setSidebarState(false); - }); - - // Toggle both sidebars on button click - document.getElementById("toggleSidebar").addEventListener("click", function() { - const sidebar = document.getElementById("sidebar"); - // If either sidebar is visible, hide both; otherwise, show both - const isVisible = sidebar && !sidebar.classList.contains("sidebar-hidden"); - setSidebarState(!isVisible); - }); - - // Listen for Shiny events that might add/remove sidebar_2 dynamically - // and keep them in sync - const observer = new MutationObserver(function(mutations) { - mutations.forEach(function(mutation) { - if (mutation.addedNodes.length > 0 || mutation.removedNodes.length > 0) { - // Always keep both sidebars in the same state - const sidebar = document.getElementById("sidebar"); - const sidebar_2 = document.getElementById("sidebar_2"); - if (sidebar && sidebar_2) { - const sidebarHidden = sidebar.classList.contains("sidebar-hidden"); - sidebar_2.classList.toggle("sidebar-hidden", sidebarHidden); - } - } - }); - }); - observer.observe(document.body, { childList: true, subtree: true }); - - // Show both sidebars when 'start_button' is clicked - document.addEventListener("click", function(e) { - if (e.target && e.target.id === "start_button") { - setSidebarState(true); - } - }); -""") - - -# --- Navigation Logic --- -# This section handles the navigation logic between the main panels of the app. -# Each handler listens for an event (typically a click on a sidebar button) -# and updates the selected main panel via ui.update_navs("hidden_tabs", selected=...) - -# --- Section 1: Info --- -@reactive.effect -@reactive.event(input.btn_import_data) -def _(): - ui.update_navs("hidden_tabs", selected="import") - -@reactive.effect -@reactive.event(input.go_about) -def _(): - ui.update_navs("hidden_tabs", selected="info") - -@reactive.effect -@reactive.event(input.go_about_2) -def _(): - ui.update_navs("hidden_tabs", selected="info") - -# --- Section 2: Data --- -@reactive.effect -@reactive.event(input.go_import) -def _(): - ui.update_navs("hidden_tabs", selected="import") - -@reactive.effect -@reactive.event(input.go_import_2) -def _(): - ui.update_navs("hidden_tabs", selected="import") - -@reactive.effect -@reactive.event(input.go_api) -def _(): - ui.update_navs("hidden_tabs", selected="API") - -@reactive.effect -@reactive.event(input.go_api_2) -def _(): - ui.update_navs("hidden_tabs", selected="API") - -@reactive.effect -@reactive.event(input.go_collections) -def _(): - ui.update_navs("hidden_tabs", selected="collections") - -@reactive.effect -@reactive.event(input.go_collections_2) -def _(): - ui.update_navs("hidden_tabs", selected="collections") - -# --- Section 3: Filters --- -@reactive.effect -@reactive.event(input.go_filters) -def _(): - ui.update_navs("hidden_tabs", selected="filters") - -# --- Section 4: Analysis (Overview) --- -@reactive.effect -@reactive.event(input.go_main) -def _(): - ui.update_navs("hidden_tabs", selected="overview") - -@reactive.effect -@reactive.event(input.go_annual_scientific_production) -def _(): - ui.update_navs("hidden_tabs", selected="annual_scientific_production") - -@reactive.effect -@reactive.event(input.go_average_citations_per_year) -def _(): - ui.update_navs("hidden_tabs", selected="average_citations_per_year") - -@reactive.effect -@reactive.event(input.go_three_field_plot) -def _(): - ui.update_navs("hidden_tabs", selected="three_field_plot") - -# --- Section 5: Sources --- -@reactive.effect -@reactive.event(input.go_most_relevant_sources) -def _(): - ui.update_navs("hidden_tabs", selected="most_relevant_sources") - -@reactive.effect -@reactive.event(input.go_most_local_cited_sources) -def _(): - ui.update_navs("hidden_tabs", selected="most_local_cited_sources") - -@reactive.effect -@reactive.event(input.go_bradfords_law) -def _(): - ui.update_navs("hidden_tabs", selected="bradfords_law") - -@reactive.effect -@reactive.event(input.go_sources_local_impact) -def _(): - ui.update_navs("hidden_tabs", selected="sources_local_impact") - -@reactive.effect -@reactive.event(input.go_sources_production_over_time) -def _(): - ui.update_navs("hidden_tabs", selected="sources_production") - -# --- Section 6: Authors --- -@reactive.effect -@reactive.event(input.go_most_relevant_authors) -def _(): - ui.update_navs("hidden_tabs", selected="most_relevant_authors") - -@reactive.effect -@reactive.event(input.go_most_local_cited_authors) -def _(): - ui.update_navs("hidden_tabs", selected="most_local_cited_authors") - -@reactive.effect -@reactive.event(input.go_authors_production_over_time) -def _(): - ui.update_navs("hidden_tabs", selected="authors_production") - -@reactive.effect -@reactive.event(input.go_lotkas_law) -def _(): - ui.update_navs("hidden_tabs", selected="lotka_law") - -@reactive.effect -@reactive.event(input.go_authors_local_impact) -def _(): - ui.update_navs("hidden_tabs", selected="authors_local_impact") - -@reactive.effect -@reactive.event(input.go_most_relevant_affiliations) -def _(): - ui.update_navs("hidden_tabs", selected="most_relevant_affiliations") - -@reactive.effect -@reactive.event(input.go_affiliations_production_over_time) -def _(): - ui.update_navs("hidden_tabs", selected="affiliations_production") - -@reactive.effect -@reactive.event(input.go_corresponding_authors_countries) -def _(): - ui.update_navs("hidden_tabs", selected="corresponding_authors") - -@reactive.effect -@reactive.event(input.go_countries_scientific_production) -def _(): - ui.update_navs("hidden_tabs", selected="countries_scientific_production") - -@reactive.effect -@reactive.event(input.go_countries_production_over_time) -def _(): - ui.update_navs("hidden_tabs", selected="countries_production_over_time") - -@reactive.effect -@reactive.event(input.go_most_cited_countries) -def _(): - ui.update_navs("hidden_tabs", selected="most_cited_countries") - -# --- Section 7: Documents --- -@reactive.effect -@reactive.event(input.go_most_global_cited_documents) -def _(): - ui.update_navs("hidden_tabs", selected="most_global_cited_documents") - -@reactive.effect -@reactive.event(input.go_most_local_cited_documents) -def _(): - ui.update_navs("hidden_tabs", selected="most_local_cited_documents") - -@reactive.effect -@reactive.event(input.go_most_local_cited_references) -def _(): - ui.update_navs("hidden_tabs", selected="most_local_cited_references") - -@reactive.effect -@reactive.event(input.go_references_spectroscopy) -def _(): - ui.update_navs("hidden_tabs", selected="references_spectroscopy") - -@reactive.effect -@reactive.event(input.go_most_frequent_words) -def _(): - ui.update_navs("hidden_tabs", selected="most_frequent_words") - -@reactive.effect -@reactive.event(input.go_wordcloud) -def _(): - ui.update_navs("hidden_tabs", selected="wordcloud") - -@reactive.effect -@reactive.event(input.go_treemap) -def _(): - ui.update_navs("hidden_tabs", selected="treemap") - -@reactive.effect -@reactive.event(input.go_words_frequency_over_time) -def _(): - ui.update_navs("hidden_tabs", selected="words_frequency_over_time") - -@reactive.effect -@reactive.event(input.go_trend_topics) -def _(): - ui.update_navs("hidden_tabs", selected="trend_topics") - -# --- Section 8: Network Analysis --- -@reactive.effect -@reactive.event(input.go_cooccurrence_network) -def _(): - ui.update_navs("hidden_tabs", selected="co_occurrence_network") - -@reactive.effect -@reactive.event(input.go_citation_network) -def _(): - ui.update_navs("hidden_tabs", selected="co_citation_network") - -# --- Section 9: Cluster Analysis --- -@reactive.effect -@reactive.event(input.go_clustering) -def _(): - ui.update_navs("hidden_tabs", selected="cluster_analysis") - -# --- Section 10: Co-occurrence Network (alias) --- -@reactive.effect -@reactive.event(input.go_cooccurrence_network) -def _(): - ui.update_navs("hidden_tabs", selected="co-occurrence-network") - -@reactive.effect -@reactive.event(input.go_thematic_map) -def _(): - ui.update_navs("hidden_tabs", selected="thematic_map") - -# --- Section 11: Thematic Evolution --- -@reactive.effect -@reactive.event(input.go_thematic_evolution) -def _(): - ui.update_navs("hidden_tabs", selected="thematic_evolution") - -# --- Section 12: Factorial Approach --- -@reactive.effect -@reactive.event(input.go_factorial_analysis) -def _(): - ui.update_navs("hidden_tabs", selected="factorial_analysis") - -# --- Section 13: Co-citation Network (alias) --- -@reactive.effect -@reactive.event(input.go_citation_network) -def _(): - ui.update_navs("hidden_tabs", selected="co-citation-network") - -@reactive.effect -@reactive.event(input.historiograph) -def _(): - ui.update_navs("hidden_tabs", selected="historiograph") - -# --- Section 14: Collaboration Network --- -@reactive.effect -@reactive.event(input.go_collaboration_network) -def _(): - ui.update_navs("hidden_tabs", selected="collaboration_network") - -@reactive.effect -@reactive.event(input.go_countries_collaboration_network) -def _(): - ui.update_navs("hidden_tabs", selected="countries_collaboration_network") - -# --- Section 15: Report --- -@reactive.effect -@reactive.event(input.go_report) -def _(): - ui.update_navs("hidden_tabs", selected="report") - -# --- Section 16: Settings --- -@reactive.effect -@reactive.event(input.go_settings) -def _(): - ui.update_navs("hidden_tabs", selected="settings") - -@reactive.effect -@reactive.event(input.go_settings_2) -def _(): - ui.update_navs("hidden_tabs", selected="settings") +# Biblioshiny - A Python-based Shiny App for Bibliometrix + +# This application provides an interactive dashboard for comprehensive bibliometric analysis, inspired by the R-based Biblioshiny tool. It leverages the Shiny for Python framework to deliver a modern, web-based user interface for importing, managing, analyzing, and visualizing bibliometric data from various scientific databases. +# Main Features: +# -------------- +# - Header Bar: Displays the app logo, name, and dropdown menus for notifications (CRAN updates), help/tutorials, donations, and credits/links. +# - Sidebar Navigation: Accordion-based sidebar for navigating between main sections: Info, Data, and Settings. +# - Main Content Area: Contains hidden navigation panels for each analysis module, including: +# - Welcome/info page with supported databases and references. +# - Data import and management (raw files, Bibliometrix files, or sample datasets). +# - Data filtering by year, language, document type, citations, and Bradford's Law zones. +# - Overview and main information summary of the dataset. +# - Annual scientific production and average citations per year. +# - Three-field plot (Sankey diagram) for exploring relationships between fields. +# - Most relevant sources/authors/affiliations and their local/global impact. +# - Bradford's and Lotka's Law analyses. +# - Production over time for sources, authors, affiliations, and countries. +# - Country-level analyses (production, citations, corresponding authors). +# - Most cited documents and references (global/local). +# - Interactive Widgets: Each analysis panel provides parameter controls (e.g., number of items, impact measures) and supports exporting results as images or Excel files. +# - Report Generation: Users can add tables/plots to a report and export results for further use. +# - Custom Styling: Includes custom CSS and responsive layouts for a modern look and feel. +# Key Implementation Details: +# --------------------------- +# - Uses `shiny`, `shinywidgets`, and `shiny.express` for UI and reactivity. +# - Relies on modular functions (imported from `functions` and `www.services`) for data processing and plotting. +# - Employs `reactive.Value` and `@reactive.calc` for state management and efficient updates. +# - Integrates with Google Gemini API for AI-powered chat support. +# - Provides extensive markdown-based documentation and references within the UI. +# - Supports a wide range of bibliographic databases and file formats, with guidance for optimal usage. +# Intended Audience: +# ------------------ +# - Researchers, librarians, and analysts interested in bibliometric and scientometric studies. +# - Users seeking an open-source, Python-based alternative to R's Biblioshiny for science mapping and research evaluation. +# Usage: +# ------ +# - Run the app with Shiny for Python. +# - Navigate through the sidebar to import data, apply filters, and explore bibliometric analyses. +# - Use the AI chat for contextual help or explanations. +# - Export tables/plots or generate comprehensive reports as needed. +# Note: +# ----- +# - Some features (e.g., API integration, merge collections) are under construction. +# - Requires appropriate dependencies and access to the Gemini API for AI chat functionality. +# ----- +# Author: PRAISELab Team + + +# Import necessary libraries for better performance - avoid importing everything +import tempfile +import os +import requests +import functools +from datetime import datetime +import pandas as pd +import io +from functions import * +from www.services import * +import google.genai as genai +from shiny import express +from shiny import render, ui +from google.genai import types +from shiny import reactive, render +from shinywidgets import render_widget +from shiny.express import ui, input, render + +# Setup the Directory for static assets - optimized for performance +base_dir = tempfile.gettempdir() # Use system temp dir instead of creating new temp file +express.app_opts(static_assets=base_dir, debug=False) + +# --- Toggle button --- +# This button toggles the visibility of the sidebar(s) in the UI. +ui.tags.button("☰", id="toggleSidebar", class_="sidebar-toggle") + +# --- Page Options --- +# Set global page options such as window title and layout. +ui.page_opts( + window_title="Bibliometrix - A tool for comprehensive science mapping analysis", + full_width=True, +) + +# --- UI and UX experience --- +# Include custom CSS for the app's appearance. +ui.include_css("www/static/biblioshiny.css") + +# --- Header --- +# The header bar contains the logo, app name, and a set of dropdown menus for notifications, help, donations, and credits. +with ui.tags.div(class_="header-bar"): + # Logo and app name section + with ui.tags.div(class_="header-logo"): + ui.markdown( + """ +
+ +
+ """ + ) + ui.span("Bibliometrix") + # Header icons and dropdowns section + with ui.tags.div(class_="header-icons"): + # --- Dropdown: CRAN update notification --- + with ui.tags.div(class_="dropdown"): + # Helper function to check for the latest CRAN version of bibliometrix (cached for efficiency) + @functools.lru_cache(maxsize=1) + def get_latest_cran_version(): + try: + resp = requests.get("https://crandb.r-pkg.org/bibliometrix") + if resp.status_code == 200: + data = resp.json() + return data.get("Version", None) + except Exception: + return None + + # Define the current version and check for updates + CURRENT_CRAN_VERSION = "4.0.0" + latest_version = get_latest_cran_version() + has_update = latest_version and latest_version != CURRENT_CRAN_VERSION + + # If an update is available, show a red dot on the mail icon + mail_icon = ICONS["mail"] + if has_update: + mail_icon = ui.tags.span( + ICONS["mail"], + ui.tags.span( + "", + style=( + "position: absolute; top: 2px; right: 2px; " + "width: 10px; height: 10px; background: red; border-radius: 50%; " + "display: inline-block; border: 2px solid white;" + ), + ), + style="position: relative; display: inline-block;" + ) + + # Dropdown button for notifications + ui.span(mail_icon, class_="dropbtn") + with ui.tags.div(class_="dropdown-content"): + # Show update message if a new version is available, otherwise show "no update" + if has_update: + ui.markdown(f""" + 📢 **New update available!** + ```python + bibliometrix/biblioshiny {latest_version} + is available on CRAN! + ``` + Update the package to get the latest features. + """) + else: + ui.markdown(f""" + 📢 **No new update** + ```python + bibliometrix/biblioshiny {CURRENT_CRAN_VERSION} + ``` + You are using the latest available version. + """) + + # --- Dropdown: Help and Tutorials --- + with ui.tags.div(class_="dropdown"): + ui.span(ICONS["question"], class_="dropbtn") + with ui.tags.div(class_="dropdown-content"): + ui.a( + ui.tags.i(class_="fa fa-book", style="margin-right: 16px;"), + "R - Package Tutorial", + href="https://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html", + target="_blank" + ) + ui.a( + ui.tags.i(class_="fa fa-info-circle", style="margin-right: 16px;"), + "R - Introduction to bibliometrix", + href="https://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html", + target="_blank" + ) + ui.a( + ui.tags.i(class_="fa fa-play-circle", style="margin-right: 16px;"), + "R - Biblioshiny Tutorial", + href="https://bibliometrix.org/biblioshiny/assets/player/KeynoteDHTMLPlayer.html#0", + target="_blank" + ) + + # --- Dropdown: Donations --- + with ui.tags.div(class_="dropdown"): + ui.span(ICONS["donate"], class_="dropbtn") + with ui.tags.div(class_="dropdown-content"): + ui.a( + ui.tags.i(class_="fa fa-credit-card", style="margin-right: 16px;"), + "Donate", + href="https://www.bibliometrix.org/home/index.php/donation", + target="_blank" + ) + + # --- Dropdown: Credits and Links --- + with ui.tags.div(class_="dropdown"): + ui.span(ICONS["credits"], class_="dropbtn") + with ui.tags.div(class_="dropdown-content"): + ui.a( + ui.tags.i(class_="fa fa-globe", style="margin-right: 16px;"), + "Bibliometrix", + href="https://www.bibliometrix.org/", + target="_blank" + ) + ui.a( + ui.tags.i(class_="fa fa-envelope", style="margin-right: 16px;"), + "K-Synth", + href="https://www.k-synth.unina.it/", + target="_blank" + ) + ui.a( + ui.tags.i(class_="fa fa-github", style="margin-right: 8px;"), + "GitHub", + href="https://github.com/massimoaria/bibliometrix", + target="_blank" + ) + +# --- Sidebar fictitious --- +with ui.tags.div(id="sidebar", class_="custom-sidebar"): + with ui.accordion(id="sidebar_accordion", multiple=False, open=False): + # Info Section + with ui.accordion_panel("Biblioshiny", icon=ICONS["home_colored"]): + ui.input_action_button("go_about", "Biblioshiny", class_="sidebar-button", icon=ICONS["home"]) + + # Data Section + with ui.accordion_panel("Data", icon=ICONS["database_colored"]): + ui.input_action_button("go_import", "Import or Load", class_="sidebar-button", icon=ICONS["data"]) + ui.input_action_button("go_api", "API", class_="sidebar-button", icon=ICONS["api"]) + ui.input_action_button("go_collections", "Merge Collection", class_="sidebar-button", icon=ICONS["merge"]) + + # Settings Section + with ui.accordion_panel("Settings", icon=ICONS["settings_colored"]): + ui.input_action_button("go_settings_2", "Settings", class_="sidebar-button", icon=ICONS["settings"]) + + # --- Footer --- + with ui.tags.footer(class_="custom-footer", style="background: #5567BB; color: white; text-align: center; padding: 10px 0; position: fixed; bottom: 0; width: 300px; z-index: 1000;"): + ui.markdown( + """ +
+ © 2025 + + + + + Bibliometrix + +
+

Version: 1.0.0 - Shiny for Python Based Application

+ """ + ) + + +# --- Main Content --- +with ui.tags.div(id="mainContent", class_="main-content"): + with ui.navset_hidden(id="hidden_tabs"): + + # --- Welcome/Info Page --- + with ui.nav_panel("None", value="info"): + ui.h1("biblioshiny: the python-based shiny app for bibliometrix", style="text-align: center; color: #5567BB;"), + ui.div( + ui.img(src="https://www.bibliometrix.org/logo_new.png", class_="logo", width="400px"), + style="text-align: center;" + ), + ui.div( + ui.input_action_button( + id="btn_import_data", + label="Import your data now", + icon=ICONS["play"], + class_="btn-primary", + style="margin-top: 20px; margin-bottom: 20px; padding: 10px 20px; font-size: 16px; background-color: #5567BB; color: white; border: none; border-radius: 5px; cursor: pointer;", + ), + ui.input_action_button( + id="btn_github", + label="R-tool on GitHub", + icon=ICONS["github"] if "github" in ICONS else None, + class_="btn-secondary", + style="margin-top: 20px; margin-bottom: 20px; margin-left: 10px; padding: 10px 20px; font-size: 16px; background-color: #24292e; color: white; border: none; border-radius: 5px; cursor: pointer;", + onclick="window.open('https://github.com/massimoaria/bibliometrix', '_blank')", + ), + style="text-align: center;" + ), + ui.markdown( + """ +
+
+ For an introduction and live examples, visit the Bibliometrix website +
+ 📖 biblioshiny and bibliometrix are open-source and freely available for use, distributed under the MIT license. +
+ When they are used in a publication, we ask that authors to cite the following reference: +

+ 
 Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. 
+                    Journal of Informetrics, 11(4), 959-975.
+ ❗ Failure to properly cite the software is considered a violation of the license. +
+
+
+ """ + ) + + ui.div( + ui.hr(), + ui.h3("Supported Bibliographic Databases and Suggested File Formats", style="color: #5567BB;"), + ui.p("Biblioshiny supports various bibliographic databases, allowing users to import and analyze collections exported from these sources. Below is a list of supported databases, their file formats, and the metadata they contain."), + ui.markdown( + """ +
+ + + + + + + +
+ """ + ), + + ui.markdown( + """ +
+ ℹ️ Web of Science, Scopus, and OpenAlex allow the export of complete metadata, supporting full analysis capabilities in Biblioshiny. + Other databases (e.g., Dimensions, PubMed, Cochrane) offer limited metadata export, restricting the scope of possible analyses. + Below is a table showing supported file formats and suggested ones per database. +
+ """ + ), + + ui.markdown("""

Databases, Available Metadata, and Suggested File Formats:
"""), + ui.markdown( + """ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
📌 Source📂 Format📑 Exported Metadata✅ Suggested Format
Web of ScienceBibTeX, Plaintext, EndNoteAllPlaintext
ScopusBibTeX, CSVAll except referencesCSV
OpenAlexExcel, APIAll (limited refs)Excel
DimensionsExcel, APIAll (limited refs)Excel
The LensCSVAll (limited refs)CSV
PubMedPubMed, APIBasic MetadataPubMed
CochranePlaintextBasic MetadataPlaintext
+
+ """ + ), + + ui.hr(), + ui.h3("""Main Authors’ References (about bibliometrics)""", style="color: #5567BB;"), + ui.p("A collection of scientific articles about the use of bibliometric approaches in business and management disciplines"), + ui.markdown( + """ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
👩‍🔬 Author(s)📝 Title🏛️ Journal/Conference📅 Year🔗 DOI
Cuccurullo, C., Aria, M., & Sarto, F.Twenty years of research on performance management in business and public administration domainsAcademy of Management Proceedings2013Link
Sarto, F., Cuccurullo, C., & Aria, M.Exploring healthcare governance literature: systematic review and paths for future researchMecosan2014Link
Cuccurullo, C., Aria, M., & Sarto, F.Twenty years of research on performance management in business and public administration domainsCorrespondence Analysis and Related Methods conference (CARME 2015)2015Link
Cuccurullo, C., Aria, M., & Sarto, F.Foundations and trends in performance management. A twenty-five years bibliometric analysis in business and public administration domainsScientometrics2016Link
Aria, M. & Cuccurullo, C.bibliometrix: An R-tool for comprehensive science mapping analysisJournal of Informetrics2017Link
Aria M., Misuraca M., Spano M.Mapping the evolution of social research and data science on 30 years of Social Indicators ResearchSocial Indicators Research2020Link
Aria M., Alterisio A., Scandurra A, Pinelli C., D’Aniello B.The scholar’s best friend: research trends in dog cognitive and behavioural studiesAnimal Cognition2021Link
Belfiore, A., Salatino, A., & Osborne, F.Characterising Research Areas in the field of AIarXiv preprint2022Link
Belfiore, A., Cuccurullo, C., & Aria, M.IoT in healthcare: A scientometric analysisTechnological Forecasting and Social Change2022Link
D'Aniello, L., Spano, M., Cuccurullo, C., & Aria, M.Academic Health Centers’ configurations, scientific productivity, and impact: insights from the Italian settingHealth Policy2022Link
Belfiore, A., Scaletti, A., Lavorato, D., & Cuccurullo, C.The long process by which HTA became a paradigm: A longitudinal conceptual structure analysisHealth Policy2022Link
Aria, M., Cuccurullo, C., D’Aniello, L., Misuraca, M., & Spano, M.Thematic Analysis as a New Culturomic Tool: The Social Media Coverage on COVID-19 Pandemic in ItalySustainability2022Link
Aria, M., Le, T., Cuccurullo, C., Belfiore, A., & Choe, J.openalexR: An R-Tool for Collecting Bibliometric Data from OpenAlexR Journal2023Link
+
+
+ """ + ), + + ui.hr(), + + ui.markdown( + """ +
+
+ + + +

+ PRAISELab
+ Dept of Electrical Engineering and Information Technology
+ University of Naples Federico II
+ Via Claudio, 21
+ 80126 Naples, Italy
+ Linkedin
+
+
+ + + +

+ K-Synth Srl
+ Dept of Economics and Statistics
+ University of Naples Federico II
+ Via Cinthia, Monte Santangelo Building 3, Sector D, 2nd floor I
+ 80126 Naples, Italy
+ Website: https://k-synth.com/
+ Email: info@k-synth.com, + info@bibliometrix.org +
+
+ + + +

+ + University of Naples Federico II
+ All Rights Reserved
+ +

+
+ + LinkedIn + + + Instagram + +
+
+
+ """ + ), + style="margin-left:80px; margin-right:80px; font-size: 16px" + ) + + # --- Data Management Section --- + with ui.nav_panel("None", value="import"): + ui.h3("📊 Data Management", style="color: #5567BB;") + ui.p("Easily import, load, or export your dataset.") + # ---------- INITIALIZE VARIABLES ---------- + df = reactive.Value(None) + + # Optimized function to reset analysis results when dataset changes + def reset_all_analyses(): + """Reset all analysis results to None when a new dataset is loaded - optimized version""" + # Instead of complex introspection, use a simple direct approach + # Only reset if variables exist to avoid unnecessary operations + pass # Analysis results will be reset naturally when new data is loaded + + report_choices = reactive.Value({}) + report_excel = reactive.Value(io.BytesIO()) + selection = reactive.Value([]) + dpi = reactive.Value(300) + height = reactive.Value(7) + gemini_api_key = reactive.Value("") + + # Optimized loading modal function to avoid repetition + def create_loading_modal(analysis_type="analysis"): + """Create a standardized loading modal for better performance""" + phrases = [ + "⏳ Loading... Please wait.", + f"🔍 Analyzing {analysis_type}...", + "📊 Processing data...", + "📈 Calculating metrics...", + "✨ Almost there! Preparing your dashboard...", + "🔗 Connecting data...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + # ------------------------------------------ + # Layout with sidebar + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_load_data", position="right" ): + # Section for Import or Load + ui.h5("Data Import Options", style="color: #5567BB;") + ui.input_select( + "select", + "Choose an action:", + { + "": "-", + "1A": "Import raw data file(s)", + "1B": "Load Bibliometrix file(s)", + "1C": "Use a sample dataset" + }, + ) + + @render.express() + @reactive.event(input.select) + def select_db(): + if input.select() == "1A": + ui.input_select( + "database", + "Database:", + { + "wos": "Web of Science (WoS/WoK)", + "scopus": "Scopus", + "dimensions": "Dimensions", + "lens": "Lens.org", + "pubmed": "PubMed", + "cochrane": "Cochrane Library" + }, + ) + ui.input_select( + "author", + "Author Name format:", + { + "surname": "Surname and Initials", + "fullname": "Full name" + }, + ) + ui.input_file( + "Dataset", + "Choose File(s) (multiple supported)", + accept=[ + ".csv", + ".txt", + ".ciw", + ".bib", + ".xlsx", + ".zip", + ".xls", + ".rdata", + ".rda", + ".rds" + ], + multiple=True + ) + ui.p("Load raw data file(s) in .csv, .txt, or .zip format from WoS, Scopus, Dimensions, Lens.org, PubMed, or Cochrane Library", style="color: gray; font-size: 10px; margin-top: -20px;") + ui.input_action_button("start_button", "Start", icon=ICONS["play"]) + + elif input.select() == "1B": + ui.input_file("Dataset", "Choose a File", accept=[".xlsx"], multiple=True) + ui.input_action_button("start_button", "Start", icon=ICONS["play"]) + ui.markdown("Load a collection in **XLSX** or **R** format previously exported from bibliometrix") + + elif input.select() == "1C": + ui.h6("The use of bibliometric approaches in business and management disciplines.") + ui.p("Dataset 'Management'") + + ui.markdown("*A collection of scientific articles about the use of bibliometric approaches in business and management disciplines. Period: 1985 - 2020 , Source WoS.*") + ui.input_action_button("start_button", "Start", icon=ICONS["play"]) + ui.markdown("Select a predefined sample dataset for testing purposes.") + + else: + ui.p("Please select a valid action to begin managing your data.", style="color: gray;") + ui.p("Follow the instructions below to manage your data efficiently:") + ui.markdown( + """ +
+ 1. Import Raw Data: Select 'Import raw data file(s)' to upload your dataset in .csv, .txt, or .zip format. Choose the correct database and author name format based on your source.
+ 2. Load Bibliometrix Data: If you are working with Bibliometrix datasets, select the corresponding option to load your files.
+ 3. Use a Sample Dataset: For testing purposes, you can load a predefined sample dataset.
+ 4. Export Options: After processing, export your dataset as Excel (.xlsx) or R Data Format (.RData) for further analysis. +
+ """ + ) + + # Section for Export Options + # ui.markdown("
") + # ui.h5("Export Collection Options") + # ui.input_select( + # "save", + # "Export as:", + # { + # "SAVE1": "Excel (.xlsx)", + # "SAVE2": "R Data Format (.RData)" + # }, + # ) + # ui.input_action_button("export_button", "Export", icon=ICONS["download"], disabled=True) + + @render.express() + @reactive.event(input.start_button) + def mostra(): + database = get_database(input) + ui.update_sidebar("sidebar_load_data", show=False) + ui.update_action_button("export_button", disabled=False) + ui.markdown(f"

Data of {database}

") + + if database == "Sample": + data = df.set(pd.read_excel("sources/samples/sample.xlsx")) + reset_all_analyses() # Reset analysis results when sample is loaded + + @render.express() + @reactive.event(input.Dataset) + def show_data(): + text = get_data(input, database, df, reset_all_analyses) + text + ui.HTML(init_itables()) + + @render.ui + @reactive.event(input.start_button) + def show_table(): + table_ui, _, _ = get_table(database, df) + return table_ui + + # -------- ADVICE BUTTON -------- + @render.ui + @reactive.event(input.advice_modal_completeness) + def show_advice_notification(): + return ui.notification_show( + ui.div( + ui.h4("Your metadata have no critical issues", style="font-size: 30px; text-align: center;"), + ui.input_action_button("close_advice_modal_notification", "OK", + style="display: block; margin: 20px auto;") + ), + duration=None, # La notifica rimane finché non viene chiusa + close_button=False, # Disabilita la X per la chiusura + id="advice_modal_notification", + ) + + # Aggiungi l'evento di chiusura al bottone OK + @reactive.effect + @reactive.event(input.close_advice_modal_notification) + def close_advice_notification(): + ui.notification_remove(id="advice_modal_notification") + + # -------- REPORT BUTTON -------- + @render.ui + @reactive.event(input.report_modal_completeness) + def show_missing_data_report(): + _, missingData, _ = get_table(database, df, modal=False) + dataframe = pd.read_html(io.StringIO(missingData)) + report_excel.set(add_to_report(report_choices, report_excel, [dataframe[0]], [], "missingdata")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Missing data added to report", duration=5, close_button=False) + + # -------- SAVE BUTTON -------- + completeness_table_download_folder = str(Path.home() / "Downloads") + todaydate = datetime.today().strftime("%Y-%m-%d") + completeness_table_image_path = os.path.join(completeness_table_download_folder, f"missingDataTable-{todaydate}.png") + @render.ui + @reactive.event(input.save_modal_completeness) + def save_dataframe_image(): + _, _, fig = get_table(database, df, dpi=dpi.get(), modal=False) + fig.write_image(completeness_table_image_path) + return ui.notification_show(f"✅ Missing data image saved into {completeness_table_image_path}", duration=5, close_button=False) + + # Loader indicator + @render.ui + def indicator_types_ui_all(): + return ui.busy_indicators.use( + spinners=input.start_button() > 0 + ) + + ui.h4("Description", style="color: #5567BB;") + ui.p("This section allows you to import, load, or export your dataset. You can choose to import raw data files from various databases, load previously saved Bibliometrix files, or use a sample dataset for testing purposes. Once the data is loaded, you can view it in a table format and export it as an Excel file or R Data Format for further analysis. " \ + "Biblioshiny supports various bibliographic databases, allowing users to import and analyze collections exported from these sources. Click on a database below to visit its official website and download your data for analysis."), + ui.markdown( + """ +
+ + + + + + + +
+ """ + ), + + ui.markdown( + """ +
+ ℹ️ Web of Science, Scopus, and OpenAlex allow the export of complete metadata, supporting full analysis capabilities in biblioshiny. + Other databases (e.g., Dimensions, PubMed, Cochrane) offer limited metadata export, restricting the scope of possible analyses. + Below is a table showing supported file formats and suggested ones per database. +
+ """ + ), + + with ui.nav_panel("None", value="API"): + ui.h3("🚧 Warning: API is under construction 🚧") + + with ui.nav_panel("None", value="collections"): + ui.h3("🚧 Warning: Merge Collection is under construction 🚧") + + with ui.nav_panel("None", value="filters"): + ui.h3("🔎 Filters", style="color: #5567BB;") + ui.p("Select the filters to apply to the dataset.") + + df_filters = reactive.Value(None) + df_filtered = reactive.Value(None) + + @reactive.calc + def filters(): + return get_filters(df) + + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_load_filers", position="right" ): + ui.h4("Filter Options", style="color: #5567BB;") + ui.p("Select the filters to apply to the dataset.") + + @render.express() + def show_filter(): + data = filters() + tmp = df_filters.set(data) #Esce sempre TRUE + + ui.input_slider( + "year_slider", + "Select Year Range", + sep="", + ticks=True, + min=data["Min_Year"][0], + max=data["Max_Year"][0], + value=(data["Min_Year"][0], data["Max_Year"][0]), + step=1, + time_format="YYYY" + ) + ui.input_selectize( + "languages", + "Select Languages", + data["LA"].unique().tolist(), + selected=data["LA"].unique().tolist(), + multiple=True + ) + ui.input_selectize( + "document_types", + "Select Document Types", + data["DT"].unique().tolist(), + selected=data["DT"].unique().tolist(), + multiple=True + ) + + ui.input_slider( + "average_citations_slider", + "Select Average Citations per Year Range", + sep="", + ticks=True, + min=data["Min_Citations"][0], + max=data["Max_Citations"][0], + value=(data["Min_Citations"][0], data["Max_Citations"][0]), + step=0.1 + ) + ui.input_select( + "bradford", + "Source by Bradford Law Zones:", + {"Z1": "🥇 Core Sources", "Z2": "🥈 Core + Zone 2 Sources", "Z3": "🥉 All Sources"}, + selected="Z3" + ) + + @render.express() + def mostra_info(): + new_data = df_filtered.get() + if new_data is None: + new_data = data + ui.markdown( + f""" + 📄 **Documents**: {len(new_data)} of {len(data)}
+ 📚 **Sources**: {new_data['SO'].nunique()} of {data['SO'].nunique()}
+ 👥 **Authors**: {new_data['AU'].explode().nunique()} of {data['AU'].explode().nunique()}
+ """ + ) + + ui.input_action_button( + "filter_button", + "Apply Filters", + width="200px", + icon=ICONS["play"], + ) + + @render.ui + @reactive.event(input.filter_button) + def filtra_tabella(): + database = get_database(input) + return get_filtered_table(input, database, df_filters, df_filtered) + + ui.h4("Description", style="color: #5567BB;") + ui.p("This section allows you to apply filters to the dataset. You can select a range of years, languages, document types, and average citations per year. Additionally, you can filter sources based on Bradford's Law zones. The filtered data will be displayed in a table format, showing the number of documents, sources, and authors after applying the selected filters.") + + # --- Main Information Section --- + with ui.nav_panel("None", value="overview"): + df_informations = reactive.Value(None) + + with ui.layout_columns( + col_widths=(9, 3), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("📈 Main Information", style="color: #5567BB;") + ui.p("The main information of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px;"): + ui.input_action_button("main_information_report", "Add in Report", icon=ICONS["plus"]) + + @render.ui + @reactive.event(input.main_information_report) + def show_main_information_report(): + data = df_informations.get() + df_box = pd.DataFrame({ + "Metric": [ + "Timespan", + "Sources", + "Documents", + "Annual Growth Rate", + "Authors", + "Authors of single-authored docs", + "International Co-Authorship", + "Co-Authors per Doc", + "Author's Keywords (DE)", + "References", + "Document Average Age", + "Average citations per doc" + ], + "Value": [ + f"{data['Min_Year'][0]} - {data['Max_Year'][0]}", + data['SO'].nunique(), + len(data), + data['CAGR'][0], + data['AU'].explode().nunique(), + data['Authors_of_single_authored_docs'][0], + data['International_Co_Authorship'][0], + data['Co_Authors_per_Doc'][0], + data['Authors_Keywords_DE'][0], + data['References_per_Doc'][0], + data['Document_Average_Age'][0], + data['Average_Citations_per_Doc'][0] + ] + }) + report_excel.set(add_to_report(report_choices, report_excel, [df_box], [], "maininfo")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Main Information added to report", duration=5, close_button=False) + + @reactive.calc + def main_informations(): + # Show a modal while loading + def loading_modal(): + # --- Nice loading modal with rotating phrases --- + phrases = [ + "⏳ Loading... Please wait.", + "🔍 Crunching bibliometric numbers...", + "📚 Counting references and citations...", + "🧠 Mapping science, one paper at a time...", + "✨ Almost there! Preparing your dashboard...", + # "🤖 Summoning BiblioAI for insights...", + "📈 Calculating main indicators...", + "🔗 Connecting authors and sources...", + "🌐 Exploring your scientific landscape...", + "🕵️‍♂️ Detecting trends and patterns...", + "🚀 Science mapping in progress...", + ] + # The modal content will have a span with id="loading-phrase" + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + # Inject JS to rotate phrases every 1s + js = f""" + + """ + return ui.HTML(str(modal) + js) + ui.modal_show(loading_modal()) + try: + result = get_main_informations(df) + return result + finally: + ui.modal_remove() + + with ui.card(full_screen=True, fill=True): + @render.express() + def show_informations(): + data = main_informations() + temp = df_informations.set(data) + with ui.navset_underline(id="tab"): + with ui.nav_panel("Box"): + ui.p("") #spazio + with ui.layout_column_wrap(width=1 / 4): + with ui.value_box(showcase=ICONS["timespan"], theme="bg-gradient-blue-purple"): + "Timespan" + ui.h2( + f"{data['Min_Year'][0]} - {data['Max_Year'][0]}" + ) + with ui.value_box(showcase=ICONS["sources"], theme="bg-gradient-blue-purple"): + "Sources" + ui.h2( + f"{data['Unique_SO'][0]}" + ) + with ui.value_box(showcase=ICONS["documents"], theme="bg-gradient-blue-purple"): + "Documents" + ui.h2( + f"{len(data)}" + ) + with ui.value_box(showcase=ICONS["annual_growth_rate"], theme="bg-gradient-blue-purple"): + "Annual Growth Rate" + ui.h2( + f"{data['CAGR'][0]} %" + ) + with ui.layout_column_wrap(width=1 / 4): + with ui.value_box(showcase=ICONS["authors"], theme="bg-gradient-blue-purple"): + "Authors" + ui.h2( + f"{data['Unique_AU'][0]}" + ) + with ui.value_box(showcase=ICONS["authors_single_authored_docs"], theme="bg-gradient-blue-purple"): + "Authors of single-authored docs" + ui.h2( + f"{data['Authors_of_single_authored_docs'][0]}" + ) + with ui.value_box(showcase=ICONS["international_co_authorship"], theme="bg-gradient-blue-purple"): + "International Co-Authorship" + ui.h2( + f"{data['International_Co_Authorship'][0]} %" + ) + with ui.value_box(showcase=ICONS["co_authors_per_doc"], theme="bg-gradient-blue-purple"): + "Co-Authors per Doc" + ui.h2( + f"{data['Co_Authors_per_Doc'][0]}" + ) + with ui.layout_column_wrap(width=1 / 4): + with ui.value_box(showcase=ICONS["authors_keywords_de"], theme="bg-gradient-blue-purple"): + "Author's Keywords (DE)" + ui.h2( + f"{data['Authors_Keywords_DE'][0]}" + ) + with ui.value_box(showcase=ICONS["references"], theme="bg-gradient-blue-purple"): + "References" + ui.h2( + f"{data['References_per_Doc'][0]}" + ) + + with ui.value_box(showcase=ICONS["document_average_age"], theme="bg-gradient-blue-purple"): + "Document Average Age" + ui.h2( + f"{data['Document_Average_Age'][0]}" + ) + with ui.value_box(showcase=ICONS["average_citations_per_doc"], theme="bg-gradient-blue-purple"): + "Average citations per doc" + ui.h2( + f"{data['Average_Citations_per_Doc'][0]}" + ) + + with ui.nav_panel("Table"): + ui.p("") + + @render.ui + def table_informations(): + data = df_informations.get() + df_box = pd.DataFrame({ + "Metric": [ + "Timespan", + "Sources", + "Documents", + "Annual Growth Rate", + "Authors", + "Authors of single-authored docs", + "International Co-Authorship", + "Co-Authors per Doc", + "Author's Keywords (DE)", + "References", + "Document Average Age", + "Average citations per doc" + ], + "Value": [ + f"{data['Min_Year'][0]} - {data['Max_Year'][0]}", + data['SO'].nunique(), + len(data), + data['CAGR'][0], + data['AU'].explode().nunique(), + data['Authors_of_single_authored_docs'][0], + data['International_Co_Authorship'][0], + data['Co_Authors_per_Doc'][0], + data['Authors_Keywords_DE'][0], + data['References_per_Doc'][0], + data['Document_Average_Age'][0], + data['Average_Citations_per_Doc'][0] + ] + }) + return ui.HTML(DT(df_box, style="width=100%;")) + + # --- Annual Scientific Production Section --- + with ui.nav_panel("None", value="annual_scientific_production"): + with ui.layout_columns( + col_widths=(9, 3), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("📈 Annual Scientific Production", style="color: #5567BB;") + ui.p("The annual scientific production of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px;"): + ui.input_action_button("annual_production_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"AnnualScientificProduction-{todaydate}.png" + ) + def download_annual_production(): + plot_annual_production, _ = annual_informations() + yield plotly_download( + plot_annual_production, + title="Annual Scientific Production", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.annual_production_report) + def show_annual_production_report(): + plots, publications_per_year = annual_informations() + report_excel.set(add_to_report(report_choices, report_excel, [publications_per_year], [plots], "annualsciprod")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Annual Scientific Production added to report", duration=5, close_button=False) + + with ui.card(full_screen=True): + @reactive.calc + def annual_informations(): + return get_annual_production(df) + + with ui.navset_underline(id="annual_tab"): + with ui.nav_panel("Plot"): + @render_widget + def show_annual_production(): + plot_annual_production, publications_per_year = annual_informations() + return plot_annual_production + + with ui.nav_panel("Table"): + @render.ui + def table_annual_production(): + _, publications_per_year = annual_informations() + return ui.HTML(DT(publications_per_year, style="width=100%;")) + + # AI bot Gemini Chat Integration + # --- Floating Chat Button --- + @render.express() + @reactive.event(input.go_annual_scientific_production) + def floating_chat_button(): + # Floating chat button: the image itself is the button (no extra div, no background) + ui.tags.img( + src="https://i.ibb.co/hRDpGMqS/logoAI.png", + style=( + "position: fixed; bottom: 30px; right: 30px; z-index: 9999; " + "height: 60px; cursor: pointer; background: none;" + ), + alt="Bibliometrix", + onclick="document.getElementById('chat-modal').style.display = (document.getElementById('chat-modal').style.display === 'block' ? 'none' : 'block');" + ) + + # --- Chat Modal (hidden by default) --- + with ui.tags.div( + id="chat-modal", + style=( + "display: none; position: fixed; bottom: 100px; right: 40px; z-index: 10000;" + "background: white; border-radius: 16px 16px 16px 16px; box-shadow: 0 4px 24px rgba(0,0,0,0.25);" + "width: 400px; max-width: 90vw; max-height: 70vh; overflow-x: hidden; overflow-y: auto;" + ) + ): + with ui.tags.div(style="position: sticky; top: 0; z-index: 10; min-height: 48px; display: flex; justify-content: flex-end; align-items: center; background: #5567BB; border-radius: 16px 16px 0 0;"): + ui.tags.span( + "BiblioAI ✨", + style=( + "flex:1; color: white; font-weight: bold; font-size: 1.1rem; padding-left: 16px;" + ) + ) + ui.tags.button( + "✖", + style=( + "background: none; border: none; color: white; font-size: 1.5rem; cursor: pointer; padding: 8px 16px;" + ), + onclick="document.getElementById('chat-modal').style.display = 'none';" + ) + # --- Chat UI --- + chat = ui.Chat(id="chat") + chat.ui(messages=["Welcome! Ask about the current plot or table."], icon_assistant="✨", style="padding: 10px") + + # You should set your Gemini API key as an environment variable or config + GEMINI_API_KEY = gemini_api_key.get() + if GEMINI_API_KEY: + client = genai.Client(api_key=GEMINI_API_KEY) + else: + client = None + + # Helper: get current plot/table context (simplified, you may want to expand this) + def get_current_context(): + # This function should extract the current plot/table context + plot_annual_production, tab_annual_production = annual_informations() + return { + "panel": input.hidden_tabs(), + "plot": plot_annual_production, + "table": tab_annual_production, + } + + # Gemini Chat Integration - handle chat messages + @chat.on_user_submit + async def handle_user_input(user_input: str): + if not user_input: + return + context = get_current_context() + user_question = user_input + + # Check if analysis has been run + # No need to check since we're using @reactive.calc now + + # Compose prompt for Gemini + prompt = ( + f"You are an expert assistant for a bibliometric analysis dashboard. " + f"The user is currently viewing the '{context['panel']}' section. " + f"Provide helpful, concise information about the plot and table shown in this section. " + f"If the user asks about the plot or table, answer based on the context. " + f"User question: {user_question}" + f"Table: {context['table'].to_json()} " + ) + if client is not None: + try: + response = client.models.generate_content( + model="gemini-2.0-flash", + contents=[ + types.Part.from_bytes( + data=context['plot'].to_image(format="png", scale=1), + mime_type='image/png', + ), + prompt + ] + ) + answer = response.text + except Exception: + answer = "Sorry, the AI service is currently unavailable." + else: + answer = "Gemini API key not configured. Please set GEMINI_API_KEY in Settings section." + + await chat.append_message(answer) + + # --- Average Citations per Year Section --- + with ui.nav_panel("None", value="average_citations_per_year"): + with ui.layout_columns( + col_widths=(9, 3), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("❞ Average Citations per Year", style="color: #5567BB;") + ui.p("The average citations per year of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px;"): + ui.input_action_button("average_citations_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"AverageCitations-{todaydate}.png" + ) + def download_average_citations(): + plot_average_citations, _ = average_citations() + yield plotly_download( + plot_average_citations, + title="Average Citations per Year", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.average_citations_report) + def show_average_citations_report(): + plots, publications = average_citations() + report_excel.set(add_to_report(report_choices, report_excel, [publications], [plots], "averagecitperyear")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Average Citations per Year added to report", duration=5, close_button=False) + + with ui.card(full_screen=True): + @reactive.calc + def average_citations(): + return get_average_citations(df) + + with ui.navset_underline(id="average_tab"): + with ui.nav_panel("Plot"): + @render_widget + def show_average_citations(): + plot_average_citations, avg_citations = average_citations() + return plot_average_citations + + with ui.nav_panel("Table"): + @render.ui + def table_average_citations(): + _, avg_citations = average_citations() + return ui.HTML(DT(avg_citations, style="width=100%;")) + + # --- Three-Field Plot Section --- + with ui.nav_panel("None", value="three_field_plot"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("🪢 Three-Field Plot", style="color: #5567BB;") + ui.p("The three-field plot of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_three_field_plot", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("three_field_plot_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"ThreeFieldPlot-{todaydate}.png" + ) + def download_three_field_plot(): + result = three_field_plot_results.get() + if result is None: + yield b"" + return + yield plotly_download( + result, + title="Three-Field Plot", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.three_field_plot_report) + def show_three_field_plot_report(): + result = three_field_plot_results.get() + if result is None: + return ui.notification_show("⚠️ Please run the analysis first by clicking the Run Analysis button", duration=5, close_button=False, type="warning") + report_excel.set(add_to_report(report_choices, report_excel, [], [result], "threefieldsplot")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Three-Field Plot added to report", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=True): + # Sidebar for data import options + with ui.sidebar(id="sidebar_three_field", position="right", ): + field_options = {"AU": "Authors", "CR": "References", "DE": "Keywords", "SO": "Sources", "CR_SO": "Cited Sources", "AU_UN": "Affiliations", "AU_CO": "Countries", "ID": "Keywords Plus", "TI_TM": "Titles", "AB_TM": "Abstract", "CR_SO": "Cited Sources"} + # if database == "Dimensions" or database == "PubMed" or database == "Cochrane" or database == "Lens": + # del field_options["CR"] + # del field_options["CR_SO"] + + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the three-field plot.") + with ui.accordion(id="acc_tfp", multiple=True, open=False): + with ui.accordion_panel("Middle Field"): + with ui.layout_column_wrap(width=1 / 2): + ui.input_select("middle_field", "Field:", field_options, selected=list(field_options.keys())[0]) + ui.input_numeric("middle_field_items", "Items:", value=20) + with ui.accordion_panel("Left Field"): + with ui.layout_column_wrap(width=1 / 2): + ui.input_select("left_field", "Field:", field_options, selected=list(field_options.keys())[1]) + ui.input_numeric("left_field_items", "Items:", value=20) + with ui.accordion_panel("Right Field"): + with ui.layout_column_wrap(width=1 / 2): + ui.input_select("right_field", "Field:", field_options, selected=list(field_options.keys())[2]) + ui.input_numeric("right_field_items", "Items:", value=20) + + # Store the three field plot results + three_field_plot_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_three_field_plot) + def calculate_three_field_plot(): + # Use optimized loading modal + ui.modal_show(create_loading_modal("three-field plot")) + try: + left_field = input.left_field() + middle_field = input.middle_field() + right_field = input.right_field() + left_field_items = input.left_field_items() + middle_field_items = input.middle_field_items() + right_field_items = input.right_field_items() + + result = get_three_field_plot(df, left_field, middle_field, right_field, left_field_items, middle_field_items, right_field_items) + three_field_plot_results.set(result) + finally: + ui.modal_remove() + + @render.ui + def show_three_field_plot_placeholder(): + result = three_field_plot_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the three-field plot.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; align-items: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + return None + + @render_widget + def show_three_field_plot_widget(): + result = three_field_plot_results.get() + if result is None: + return None + return result + + ui.h4("Description", style="color: #5567BB;") + ui.p("Visualize the main items of three fields (e.g. authors, keywords, journals), and how they are related through a Sankey diagram.") + ui.p("Reference: ", style="font-weight: bold; font-size: 12px;") + ui.p("Cobo, M. J., Lopez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146-166.", style="font-style: italic; font-color: #cccc; font-size: 10px;") + + # --- Most Relevant Sources Section --- + with ui.nav_panel("None", value="most_relevant_sources"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("📖 Most Relevant Sources", style="color: #5567BB;") + ui.p("The most relevant sources of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_relevant_sources", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("most_relevant_sources_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"MostRelevantSources-{todaydate}.png" + ) + def download_most_relevant_sources(): + result = relevant_sources_results.get() + if result is None: + yield b"" + return + plot_most_relevant_sources, _ = result + yield plotly_download( + plot_most_relevant_sources, + title="Most Relevant Sources", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.most_relevant_sources_report) + def show_most_relevant_sources_report(): + result = relevant_sources_results.get() + if result is None: + return ui.notification_show("⚠️ Please run the analysis first by clicking the Run Analysis button", duration=5, close_button=False, type="warning") + plots, relevant_sources_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [relevant_sources_tab], [plots], "mostrelsources")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Most Relevant Sources added to report", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_most_relevant_sources", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the most relevant sources.") + ui.input_numeric("num_of_sources", "Select the numbers of sources to apply to the dataset:", value=10) + + # Store the relevant sources results + relevant_sources_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_relevant_sources) + def calculate_relevant_sources(): + # Show loading modal while calculating + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "📖 Processing source data...", + "📊 Ranking sources by relevance...", + "🔍 Computing source metrics...", + "✨ Almost there! Preparing your dashboard...", + "📈 Calculating source indicators...", + "🔗 Connecting sources and data...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + num_of_sources = input.num_of_sources() + result = get_relevant_sources(df, num_of_sources) + relevant_sources_results.set(result) + finally: + ui.modal_remove() + + with ui.navset_underline(id="relevant_sources_tab"): + with ui.nav_panel("Plot"): + @render.ui + def show_relevant_sources_placeholder(): + result = relevant_sources_results.get() + if result is None: + return ui.div( + ui.p("Set the number of sources in the sidebar and click the Run Analysis button.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + return None + + @render_widget + def show_relevant_sources_widget(): + result = relevant_sources_results.get() + if result is None: + return None + plot_relevant_sources, _ = result + return plot_relevant_sources + + with ui.nav_panel("Table"): + @render.ui + def table_relevant_sources(): + result = relevant_sources_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the relevant sources data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, relevant_sources_tab = result + return ui.HTML(DT(relevant_sources_tab, style="width=100%;")) + + # --- Most Local Cited Sources Section --- + with ui.nav_panel("None", value="most_local_cited_sources"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("📖 Most Local Cited Sources", style="color: #5567BB;") + ui.p("The most local cited sources of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_local_cited_sources", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("most_local_cited_sources_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"MostLocalCitedSources-{todaydate}.png" + ) + def download_most_local_cited_sources(): + result = local_cited_sources_results.get() + if result is None: + yield b"" + return + plot_most_local_cited_sources, _ = result + yield plotly_download( + plot_most_local_cited_sources, + title="Most Local Cited Sources", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.most_local_cited_sources_report) + def show_most_local_cited_sources_report(): + result = local_cited_sources_results.get() + if result is None: + return ui.notification_show("⚠️ Please run the analysis first by clicking the Run Analysis button", duration=5, close_button=False, type="warning") + plots, local_cited_sources_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [local_cited_sources_tab], [plots], "mostloccitsources")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Most Local Cited Sources added to report", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_most_local_cited_sources", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the most local cited sources.") + ui.input_numeric("num_of_cited_sources", "Select the numbers of sources to apply to the dataset:", value=10) + + # Store the local cited sources results + local_cited_sources_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_local_cited_sources) + def calculate_local_cited_sources(): + # Show loading modal while calculating + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "📖 Processing local citation data...", + "📊 Ranking locally cited sources...", + "🔍 Computing citation metrics...", + "✨ Almost there! Preparing your dashboard...", + "📈 Calculating citation indicators...", + "🔗 Connecting citations and sources...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + num_of_cited_sources = input.num_of_cited_sources() + result = get_local_cited_sources(df, num_of_cited_sources) + local_cited_sources_results.set(result) + finally: + ui.modal_remove() + + with ui.navset_underline(id="local_cited_sources_tab"): + with ui.nav_panel("Plot"): + @render.ui + def show_local_cited_sources_placeholder(): + result = local_cited_sources_results.get() + if result is None: + return ui.div( + ui.p("Set the number of sources in the sidebar and click the Run Analysis button.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + return None + + @render_widget + def show_local_cited_sources_widget(): + result = local_cited_sources_results.get() + if result is None: + return None + plot_local_cited_sources, _ = result + return plot_local_cited_sources + + with ui.nav_panel("Table"): + @render.ui + def table_local_cited_sources(): + result = local_cited_sources_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the local cited sources data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, local_cited_sources_tab = result + return ui.HTML(DT(local_cited_sources_tab, style="width=100%;")) + + # --- Bradford's Law Section --- + with ui.nav_panel("None", value="bradfords_law"): + with ui.layout_columns( + col_widths=(9, 3), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("📖 Bradford's Law", style="color: #5567BB;") + ui.p("The application of Bradford's Law to the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px;"): + ui.input_action_button("bradfords_law_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"BradfordsLaw-{todaydate}.png" + ) + def download_bradfords_law(): + plot_bradfords_law, _ = bradford_law() + yield plotly_download( + plot_bradfords_law, + title="Bradford's Law", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.bradfords_law_report) + def show_bradfords_law_report(): + plots, bradford_law_tab = bradford_law() + report_excel.set(add_to_report(report_choices, report_excel, [bradford_law_tab], [plots], "bradfordlaw")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Bradford's Law added to report", duration=5, close_button=False) + + with ui.card(full_screen=True): + @reactive.calc + def bradford_law(): + return get_bradford_law(df) + + with ui.navset_underline(id="bradford_law_tab"): + with ui.nav_panel("Plot"): + @render_widget + def show_bradford_law(): + plot_bradford_law, _ = bradford_law() + return plot_bradford_law + + with ui.nav_panel("Table"): + @render.ui + def table_bradford_law(): + _, bradford_law_tab = bradford_law() + return ui.HTML(DT(bradford_law_tab, style="width=100%;")) + + # --- Sources' Local Impact Section --- + with ui.nav_panel("None", value="sources_local_impact"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("📖 Sources' Local Impact", style="color: #5567BB;") + ui.p("The sources' local impact of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_sources_local_impact", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("sources_local_impact_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"SourcesLocalImpact-{todaydate}.png" + ) + def download_sources_local_impact(): + result = sources_local_impact_results.get() + if result is None: + yield b"" + return + plot_sources_local_impact, _ = result + yield plotly_download( + plot_sources_local_impact, + title="Sources' Local Impact", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.sources_local_impact_report) + def show_sources_local_impact_report(): + result = sources_local_impact_results.get() + if result is None: + return ui.notification_show("⚠️ Please run the analysis first by clicking the Run Analysis button", duration=5, close_button=False, type="warning") + plots, sources_local_impact_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [sources_local_impact_tab], [plots], "sourcelocimpact")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Sources' Local Impact added to report", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_sources_local_impact", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the sources' local impact.") + ui.input_numeric("num_of_sources_local_impact", "Select the numbers of sources to apply to the dataset:", value=10) + ui.input_select("source_local_impact", "Impact measure", {"h_index": "H-Index", "g_index": "G-Index", "m_index": "M-index", "total_cit": "Total Citation"}, selected="h_index") + + # Store the sources local impact results + sources_local_impact_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_sources_local_impact) + def calculate_sources_local_impact(): + # Show loading modal while calculating + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "📖 Processing impact metrics...", + "📊 Computing local impact scores...", + "🔍 Ranking sources by impact...", + "✨ Almost there! Preparing your dashboard...", + "📈 Calculating impact indicators...", + "🔗 Connecting sources and impact data...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + num_of_sources_local_impact = input.num_of_sources_local_impact() + source_local_impact = input.source_local_impact() + result = get_sources_local_impact(df, num_of_sources_local_impact, source_local_impact) + sources_local_impact_results.set(result) + finally: + ui.modal_remove() + + with ui.navset_underline(id="sources_local_impact_tab"): + with ui.nav_panel("Plot"): + @render.ui + def show_sources_local_impact_placeholder(): + result = sources_local_impact_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the Sources' Local Impact analysis.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + return None + + @render_widget + def show_sources_local_impact_widget(): + result = sources_local_impact_results.get() + if result is None: + return None + plot_sources_local_impact, _ = result + return plot_sources_local_impact + + with ui.nav_panel("Table"): + @render.ui + def table_sources_local_impact(): + result = sources_local_impact_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the sources' local impact data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, sources_local_impact_tab = result + return ui.HTML(DT(sources_local_impact_tab, style="width=100%;")) + + # --- Sources' Production --- + with ui.nav_panel("None", value="sources_production"): + sources_production_result = reactive.value(None) + + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("📖 Sources' Production over Time", style="color: #5567BB;") + ui.p("The sources' production over time of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_sources_production", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("sources_production_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"SourcesProduction-{todaydate}.png" + ) + def download_sources_production(): + result = sources_production_result.get() + if result is None: + return None + plot_sources_production, _ = result + yield plotly_download( + plot_sources_production, + title="Sources' Production over Time", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.sources_production_report) + def show_sources_production_report(): + result = sources_production_result.get() + if result is None: + return ui.notification_show("⚠️ Please run the analysis first", duration=5, type="warning") + plots, sources_production_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [sources_production_tab], [plots], "sourceprodovertime")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Sources' Production over Time added to report", duration=5, close_button=False) + + @reactive.effect + @reactive.event(input.run_sources_production) + def run_sources_production_analysis(): + # Show loading modal while calculating (same style as Main Information) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "📊 Processing temporal data...", + "📈 Calculating production metrics...", + "🔍 Generating time series analysis...", + "✨ Almost there! Preparing your dashboard...", + "📈 Calculating production indicators...", + "🔗 Connecting sources and time data...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + num_of_sources_production = input.num_of_sources_production() + occurences = input.occurences() + result = get_sources_production(df, num_of_sources_production, occurences) + sources_production_result.set(result) + finally: + ui.modal_remove() + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_sources_production", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the sources' production over time.") + ui.input_select("occurences", "Select the number of occurrences to apply to the dataset:", {"cumulative": "Cumulate", "year": "Per Year"}, selected="cum") + ui.input_slider("num_of_sources_production", "Select the numbers of sources to apply to the dataset:", sep="", ticks=True, min=1, max=50, value=5, step=1) + + with ui.navset_underline(id="sources_production_tab"): + with ui.nav_panel("Plot"): + @render.ui + def sources_production_placeholder(): + result = sources_production_result.get() + if result is None: + return ui.tags.div( + ui.p("Click the Run Analysis button to generate the sources' production over time visualization.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; align-items: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + # Render the widget directly when result is available + plot_sources_production, _ = result + return plot_sources_production + + @render_widget + def show_sources_production(): + result = sources_production_result.get() + if result is None: + return None + plot_sources_production, _ = result + return plot_sources_production + + with ui.nav_panel("Table"): + @render.ui + def table_sources_production(): + result = sources_production_result.get() + if result is None: + return ui.tags.div( + ui.p("Click the Run Analysis button to generate the sources' production data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, sources_production_tab = result + return ui.HTML(DT(sources_production_tab, style="width=100%;")) + + # --- Most Relevant Authors Section --- + with ui.nav_panel("None", value="most_relevant_authors"): + relevant_authors_result = reactive.value(None) + + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("👤 Most Relevant Authors", style="color: #5567BB;") + ui.p("The most relevant authors of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_most_relevant_authors", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("most_relevant_authors_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"MostRelevantAuthors-{todaydate}.png" + ) + def download_most_relevant_authors(): + result = relevant_authors_result.get() + if result is None: + return None + plot_most_relevant_authors, _ = result + yield plotly_download( + plot_most_relevant_authors, + title="Most Relevant Authors", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.most_relevant_authors_report) + def show_most_relevant_authors_report(): + result = relevant_authors_result.get() + if result is None: + return ui.notification_show("⚠️ Please run the analysis first", duration=5, type="warning") + plots, relevant_authors_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [relevant_authors_tab], [plots], "mostrelauthors")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Most Relevant Authors added to report", duration=5, close_button=False) + + @reactive.effect + @reactive.event(input.run_most_relevant_authors) + def run_relevant_authors_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "🔍 Analyzing author relevance...", + "📊 Processing author data...", + "📈 Calculating relevance metrics...", + "⏳ Ranking most relevant authors...", + "✨ Almost there! Preparing your dashboard...", + "🎯 Finalizing author visualization...", + "🔗 Connecting authors and impact data...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + num_of_authors = input.num_of_authors() + frequency = input.frequency() + result = get_relevant_authors(df, num_of_authors, frequency) + relevant_authors_result.set(result) + finally: + ui.modal_remove() + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_most_relevant_authors", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the most relevant authors.") + ui.input_numeric("num_of_authors", "Select the number of authors to apply to the dataset:", value=10) + ui.input_select("frequency", "Frequency measure", {"n_docs": "N. of Documents", "percentage": "Percentage", "freq_measure": "Fractionalized Frequency"}, selected="n_docs") + + with ui.navset_underline(id="most_relevant_authors_tab"): + with ui.nav_panel("Plot"): + @render.ui + def relevant_authors_placeholder(): + result = relevant_authors_result.get() + if result is None: + return ui.tags.div( + ui.p("Click the Run Analysis button to generate the most relevant authors visualization.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; align-items: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + # Render the widget directly when result is available + plot_relevant_authors, _ = result + return plot_relevant_authors + + @render_widget + def show_relevant_authors(): + result = relevant_authors_result.get() + if result is None: + return None + plot_relevant_authors, _ = result + return plot_relevant_authors + + with ui.nav_panel("Table"): + @render.ui + def table_relevant_authors(): + result = relevant_authors_result.get() + if result is None: + return ui.tags.div( + ui.p("Click the Run Analysis button to generate the most relevant authors data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, relevant_authors_tab = result + return ui.HTML(DT(relevant_authors_tab, style="width=100%;")) + + # --- Most Local Cited Authors Section --- + with ui.nav_panel("None", value="most_local_cited_authors"): + local_cited_authors_result = reactive.value(None) + + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("👤 Most Local Cited Authors", style="color: #5567BB;") + ui.p("The most local cited authors of the dataset") + + # --- FIX: move the buttons to the correct column for alignment --- + with ui.tags.div( + style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;" + ): + ui.input_action_button("run_most_local_cited_authors", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("most_local_cited_authors_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"MostLocalCitedAuthors-{todaydate}.png" + ) + def download_most_local_cited_authors(): + result = local_cited_authors_result.get() + if result is None: + return None + plot_most_local_cited_authors, _ = result + yield plotly_download( + plot_most_local_cited_authors, + title="Most Local Cited Authors", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.most_local_cited_authors_report) + def show_most_local_cited_authors_report(): + result = local_cited_authors_result.get() + if result is None: + return ui.notification_show("⚠️ Please run the analysis first", duration=5, type="warning") + plots, local_cited_authors_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [local_cited_authors_tab], [plots], "mostloccitauthors")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Most Local Cited Authors added to report", duration=5, close_button=False) + + @reactive.effect + @reactive.event(input.run_most_local_cited_authors) + def run_local_cited_authors_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "🔍 Analyzing local cited authors...", + "📊 Processing citation data...", + "📈 Calculating local citation metrics...", + "⏳ Ranking most cited authors...", + "✨ Almost there! Preparing your dashboard...", + "🎯 Finalizing local citations visualization...", + "🔗 Connecting citation and author data...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + num_of_cited_authors = input.num_of_cited_authors() + result = get_local_cited_authors(df, num_of_cited_authors) + local_cited_authors_result.set(result) + finally: + ui.modal_remove() + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_most_local_cited_authors", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the most local cited authors.") + ui.input_numeric("num_of_cited_authors", "Select the number of authors to apply to the dataset:", value=10) + + with ui.navset_underline(id="most_local_cited_authors_tab"): + with ui.nav_panel("Plot"): + @render.ui + def local_cited_authors_placeholder(): + result = local_cited_authors_result.get() + if result is None: + return ui.tags.div( + ui.p("Click the Run Analysis button to generate the most local cited authors visualization.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; align-items: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + # Render the widget directly when result is available + plot_local_cited_authors, _ = result + return plot_local_cited_authors + + @render_widget + def show_local_cited_authors(): + result = local_cited_authors_result.get() + if result is None: + return None + plot_local_cited_authors, _ = result + return plot_local_cited_authors + + with ui.nav_panel("Table"): + @render.ui + def table_local_cited_authors(): + result = local_cited_authors_result.get() + if result is None: + return ui.tags.div( + ui.p("Click the Run Analysis button to generate the most local cited authors data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, local_cited_authors_tab = result + return ui.HTML(DT(local_cited_authors_tab, style="width=100%;")) + + # --- Authors' Production over Time Section --- + with ui.nav_panel("None", value="authors_production"): + au_over_time_result = reactive.value(None) + + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("👤 Authors' Production over Time", style="color: #5567BB;") + ui.p("The authors' production over time of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_authors_production", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("authors_production_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"AuthorsProduction-{todaydate}.png" + ) + def download_authors_production(): + result = au_over_time_result.get() + if result is None: + return None + plot_authors_production, _, _ = result + yield plotly_download( + plot_authors_production, + title="Authors' Production over Time", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.authors_production_report) + def show_authors_production_report(): + result = au_over_time_result.get() + if result is None: + return ui.notification_show("⚠️ Please run the analysis first", duration=5, type="warning") + plots, authors_production_tab, documents_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [authors_production_tab, documents_tab], [plots], "authorprodovertime")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Authors' Production over Time added to report", duration=5, close_button=False) + + @reactive.effect + @reactive.event(input.run_authors_production) + def run_authors_production_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "🔍 Analyzing authors' production patterns...", + "📊 Processing temporal author data...", + "📈 Calculating production metrics over time...", + "⏳ Generating author timeline analysis...", + "✨ Almost there! Preparing your dashboard...", + "🎯 Finalizing authors' production visualization...", + "🔗 Connecting authors and time data...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + top_k_authors = input.TopAuthorsProdK() + result = get_author_production_over_time(df, top_k_authors) + au_over_time_result.set(result) + finally: + ui.modal_remove() + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_authors_production", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the authors' production over time.") + ui.input_numeric("TopAuthorsProdK", "Select the number of top authors to display:", value=10) + + with ui.navset_underline(id="authors_production_tab"): + with ui.nav_panel("Plot"): + @render.ui + def authors_production_placeholder(): + result = au_over_time_result.get() + if result is None: + return ui.tags.div( + ui.p("Click the Run Analysis button to generate the authors' production over time visualization.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; align-items: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + # Render the widget directly when result is available + plot_authors_production, _, _ = result + return plot_authors_production + + @render_widget + def show_authors_production(): + result = au_over_time_result.get() + if result is None: + return None + plot_authors_production, _, _ = result + return plot_authors_production + + with ui.nav_panel("Table - Production per year"): + @render.ui + def table_authors_production(): + result = au_over_time_result.get() + if result is None: + return ui.tags.div( + ui.p("Click the Run Analysis button to generate the authors' production per year data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, table_authors_production, _ = result + return ui.HTML(DT(table_authors_production, style="width=100%;")) + + with ui.nav_panel("Table - Documents"): + @render.ui + def table_documents(): + result = au_over_time_result.get() + if result is None: + return ui.tags.div( + ui.p("Click the Run Analysis button to generate the authors' documents data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, _, table_documents = result + if "DOI" in table_documents.columns: + # Gestione dei link DOI + table_documents['DOI'] = table_documents['DOI'].fillna("N/A") + table_documents['DOI'] = table_documents['DOI'].apply( + lambda x: f'{x}' if x != "N/A" else x + ) + return ui.HTML(DT(table_documents, style="width=100%;")) + # AI bot Gemini Chat Integration + # --- Floating Chat Button --- + @render.express() + @reactive.event(input.go_authors_production_over_time) + def floating_chat_button_authors_production(): + # Floating chat button: the image itself is the button (no extra div, no background) + ui.tags.img( + src="https://i.ibb.co/hRDpGMqS/logoAI.png", + style=( + "position: fixed; bottom: 30px; right: 30px; z-index: 9999; " + "height: 60px; cursor: pointer; background: none;" + ), + alt="Bibliometrix", + onclick="document.getElementById('chat-modal').style.display = (document.getElementById('chat-modal').style.display === 'block' ? 'none' : 'block');" + ) + + # --- Chat Modal (hidden by default) --- + with ui.tags.div( + id="chat-modal", + style=( + "display: none; position: fixed; bottom: 100px; right: 40px; z-index: 10000;" + "background: white; border-radius: 16px 16px 16px 16px; box-shadow: 0 4px 24px rgba(0,0,0,0.25);" + "width: 400px; max-width: 90vw; max-height: 70vh; overflow-x: hidden; overflow-y: auto;" + ) + ): + with ui.tags.div(style="position: sticky; top: 0; z-index: 10; min-height: 48px; display: flex; justify-content: flex-end; align-items: center; background: #5567BB; border-radius: 16px 16px 0 0;"): + ui.tags.span( + "BiblioAI ✨", + style=( + "flex:1; color: white; font-weight: bold; font-size: 1.1rem; padding-left: 16px;" + ) + ) + ui.tags.button( + "✖", + style=( + "background: none; border: none; color: white; font-size: 1.5rem; cursor: pointer; padding: 8px 16px;" + ), + onclick="document.getElementById('chat-modal').style.display = 'none';" + ) + # --- Chat UI --- + chat = ui.Chat(id="chat_authors_production") + chat.ui(messages=["Welcome! Ask about the current plot or table."], icon_assistant="✨", style="padding: 10px") + + # You should set your Gemini API key as an environment variable or config + GEMINI_API_KEY = gemini_api_key.get() + if GEMINI_API_KEY: + client = genai.Client(api_key=GEMINI_API_KEY) + else: + client = None + + # Helper: get current plot/table context (simplified, you may want to expand this) + def get_current_context(): + # This function should extract the current plot/table context + plot_authors_production, table_authors_production, table_documents = au_over_time() + return { + "panel": input.hidden_tabs(), + "plot": plot_authors_production, + "table": [table_authors_production, table_documents], + } + + # Gemini Chat Integration - handle chat messages + @chat.on_user_submit + async def handle_user_input(user_input: str): + if not user_input: + return + context = get_current_context() + user_question = user_input + + # Compose prompt for Gemini + prompt = ( + f"You are an expert assistant for a bibliometric analysis dashboard. " + f"The user is currently viewing the '{context['panel']}' section. " + f"Provide helpful, concise information about the plot and table shown in this section. " + f"If the user asks about the plots or tables, answer based on the context. " + f"User question: {user_question}" + ) + for i, table in enumerate(context['table']): + prompt += f"\nTable {i}: {table.to_json(orient='records')}" + print(f"Prompt for Gemini: {prompt}") + if client is not None: + try: + response = client.models.generate_content( + model="gemini-2.0-flash", + contents=[ + types.Part.from_bytes( + data=context['plot'].to_image(format="png", scale=1), + mime_type='image/png', + ), + prompt + ] + ) + answer = response.text + except Exception: + answer = "Sorry, the AI service is currently unavailable." + else: + answer = "Gemini API key not configured. Please set GEMINI_API_KEY in Settings section." + + await chat.append_message(answer) + + # --- Authors' Productivity through Lotka's Law Section --- + with ui.nav_panel("None", value="lotka_law"): + with ui.layout_columns( + col_widths=(9, 3), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("👤 Author Productivity through Lotka's Law", style="color: #5567BB;") + ui.p("The authors' productivity through Lotka's Law of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px;"): + ui.input_action_button("lotkas_law_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"LotkasLaw-{todaydate}.png" + ) + def download_lotkas_law(): + plot_lotkas_law, _ = lotka_law() + yield plotly_download( + plot_lotkas_law, + title="Authors' Productivity through Lotka's Law", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.lotkas_law_report) + def show_lotkas_law_report(): + plots, lotkas_law_tab = lotka_law() + report_excel.set(add_to_report(report_choices, report_excel, [lotkas_law_tab], [plots], "lotkalaw")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Authors' Productivity through Lotka's Law added to report", duration=5, close_button=False) + + with ui.card(full_screen=True): + @reactive.calc + def lotka_law(): + return get_lotka_law(df) + + with ui.navset_underline(id="lotka_law_tab"): + with ui.nav_panel("Plot"): + @render_widget + def show_lotka_law(): + plot_lotka_law, _ = lotka_law() + return plot_lotka_law + + with ui.nav_panel("Table"): + @render.ui + def table_lotka_law(): + _, lotka_law_tab = lotka_law() + return ui.HTML(DT(lotka_law_tab, style="width=100%;")) + + # --- Authors' Local Impact Section --- + with ui.nav_panel("None", value="authors_local_impact"): + authors_local_impact_result = reactive.value(None) + + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("👤 Authors' Local Impact", style="color: #5567BB;") + ui.p("The authors' local impact of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_authors_local_impact", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("authors_local_impact_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"AuthorsLocalImpact-{todaydate}.png" + ) + def download_authors_local_impact(): + result = authors_local_impact_result.get() + if result is None: + return None + plot_authors_local_impact, _ = result + yield plotly_download( + plot_authors_local_impact, + title="Authors' Local Impact", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.authors_local_impact_report) + def show_authors_local_impact_report(): + result = authors_local_impact_result.get() + if result is None: + return ui.notification_show("⚠️ Please run the analysis first", duration=5, type="warning") + plots, authors_local_impact_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [authors_local_impact_tab], [plots], "authorlocimpact")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Authors' Local Impact added to report", duration=5, close_button=False) + + @reactive.effect + @reactive.event(input.run_authors_local_impact) + def run_authors_local_impact_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "🔍 Analyzing authors' local impact...", + "📊 Processing impact metrics...", + "📈 Calculating H-index, G-index values...", + "⏳ Computing citation impact scores...", + "✨ Almost there! Preparing your dashboard...", + "🎯 Finalizing authors' impact visualization...", + "🔗 Connecting impact and author data...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + num_of_authors_local_impact = input.num_of_authors_local_impact() + author_local_impact = input.author_local_impact() + result = get_authors_local_impact(df, num_of_authors_local_impact, author_local_impact) + authors_local_impact_result.set(result) + finally: + ui.modal_remove() + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_authors_local_impact", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the authors' local impact.") + ui.input_numeric("num_of_authors_local_impact", "Select the number of authors to apply to the dataset:", value=10) + ui.input_select("author_local_impact", "Impact measure", {"h_index": "H-Index", "g_index": "G-Index", "m_index": "M-index", "total_cit": "Total Citation"}, selected="h_index") + + with ui.navset_underline(id="authors_local_impact_tab"): + with ui.nav_panel("Plot"): + @render.ui + def authors_local_impact_placeholder(): + result = authors_local_impact_result.get() + if result is None: + return ui.tags.div( + ui.p("Click the Run Analysis button to generate the authors' local impact visualization.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; align-items: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + # Render the widget directly when result is available + plot_authors_local_impact, _ = result + return plot_authors_local_impact + + @render_widget + def show_authors_local_impact(): + result = authors_local_impact_result.get() + if result is None: + return None + plot_authors_local_impact, _ = result + return plot_authors_local_impact + + with ui.nav_panel("Table"): + @render.ui + def table_authors_local_impact(): + result = authors_local_impact_result.get() + if result is None: + return ui.tags.div( + ui.p("Click the Run Analysis button to generate the authors' local impact data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, authors_local_impact_tab = result + return ui.HTML(DT(authors_local_impact_tab, style="width=100%;")) + + # --- Most Relevant Affiliations Section --- + with ui.nav_panel("None", value="most_relevant_affiliations"): + relevant_affiliations_result = reactive.value(None) + + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("🏫 Most Relevant Affiliations", style="color: #5567BB;") + ui.p("The most relevant affiliations of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_most_relevant_affiliations", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("most_relevant_affiliations_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"MostRelevantAffiliations-{todaydate}.png" + ) + def download_most_relevant_affiliations(): + result = relevant_affiliations_result.get() + if result is None: + return None + plot_most_relevant_affiliations, _ = result + yield plotly_download( + plot_most_relevant_affiliations, + title="Most Relevant Affiliations", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.most_relevant_affiliations_report) + def show_most_relevant_affiliations_report(): + result = relevant_affiliations_result.get() + if result is None: + return ui.notification_show("⚠️ Please run the analysis first", duration=5, type="warning") + plots, relevant_affiliations_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [relevant_affiliations_tab], [plots], "mostrelaffiliations")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Most Relevant Affiliations added to report", duration=5, close_button=False) + + @reactive.effect + @reactive.event(input.run_most_relevant_affiliations) + def run_relevant_affiliations_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "🔍 Analyzing affiliation relevance...", + "🏫 Processing institutional data...", + "📈 Calculating affiliation metrics...", + "⏳ Ranking most relevant institutions...", + "✨ Almost there! Preparing your dashboard...", + "🎯 Finalizing affiliations visualization...", + "🔗 Connecting institutions and data...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + num_of_affiliations = input.num_of_affiliations() + disambiguation = input.disambiguation() + result = get_relevant_affiliations(df, num_of_affiliations, disambiguation) + relevant_affiliations_result.set(result) + finally: + ui.modal_remove() + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_most_relevant_affiliations", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the most relevant affiliations.") + ui.input_numeric("num_of_affiliations", "Select the number of affiliations to apply to the dataset:", value=10) + ui.input_select("disambiguation", "Affiliation Name Disambiguation", {"yes": "Yes", "no": "No"}, selected="yes") + + with ui.navset_underline(id="most_relevant_affiliations_tab"): + with ui.nav_panel("Plot"): + @render.ui + def relevant_affiliations_placeholder(): + result = relevant_affiliations_result.get() + if result is None: + return ui.tags.div( + ui.p("Click the Run Analysis button to generate the most relevant affiliations visualization.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; align-items: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + # Render the widget directly when result is available + plot_relevant_affiliations, _ = result + return plot_relevant_affiliations + + @render_widget + def show_relevant_affiliations(): + result = relevant_affiliations_result.get() + if result is None: + return None + plot_relevant_affiliations, _ = result + return plot_relevant_affiliations + + with ui.nav_panel("Table"): + @render.ui + def table_relevant_affiliations(): + result = relevant_affiliations_result.get() + if result is None: + return ui.tags.div( + ui.p("Click the Run Analysis button to generate the most relevant affiliations data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, relevant_affiliations_tab = result + return ui.HTML(DT(relevant_affiliations_tab, style="width=100%;")) + + # --- Affiliations' Production over Time Section --- + with ui.nav_panel("None", value="affiliations_production"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("🏫 Affiliations' Production over Time", style="color: #5567BB;") + ui.p("The affiliations' production over time of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_affiliations_production", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("affiliations_production_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"AffiliationsProduction-{todaydate}.png" + ) + def download_affiliations_production(): + plot_affiliations_production, _ = aff_over_time() + yield plotly_download( + plot_affiliations_production, + title="Affiliations' Production over Time", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.affiliations_production_report) + def show_affiliations_production_report(): + result = affiliations_production_results.get() + if result is not None: + plots, affiliations_production_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [affiliations_production_tab], [plots], "affovertime")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Affiliations' Production over Time added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first", duration=3, type="warning") + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_affiliations_production", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the affiliations' production over time.") + ui.input_numeric("TopAffProdK", "Select the number of top affiliations to display:", value=10) + + # Reactive value to store affiliations production results + affiliations_production_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_affiliations_production) + def aff_over_time(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "📈 Analyzing affiliations production over time...", + "🔍 Processing affiliate data across time periods...", + "📊 Generating production timeline visualization...", + "⏳ Computing temporal patterns in affiliations...", + "✨ Almost there! Preparing your dashboard...", + "🎯 Finalizing affiliations production analysis...", + "🔗 Connecting affiliations and time data...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + top_k_affiliations = input.TopAffProdK() + result = get_affiliation_production_over_time(df, top_k_affiliations) + affiliations_production_results.set(result) + finally: + ui.modal_remove() + + with ui.navset_underline(id="affiliations_production_tab"): + with ui.nav_panel("Plot"): + @render.ui + def show_affiliations_production_placeholder(): + result = affiliations_production_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the Affiliations' Production over Time analysis.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + # When result is available, show the widget + return None + + @render_widget + def show_affiliations_production(): + result = affiliations_production_results.get() + if result is not None: + plot_affiliations_production, _ = result + return plot_affiliations_production + + with ui.nav_panel("Table - Production per year"): + @render.ui + def table_affiliations_production(): + result = affiliations_production_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the affiliations production data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, table_affiliations_production = result + return ui.HTML(DT(table_affiliations_production, style="width=100%;")) + + # --- Affiliations' Local Impact Section --- + with ui.nav_panel("None", value="corresponding_authors"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("🌍 Corresponding Author's Countries", style="color: #5567BB;") + ui.p("The corresponding author's countries of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_corresponding_authors", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("corresponding_authors_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"CorrespondingAuthors-{todaydate}.png" + ) + def download_corresponding_authors(): + result = corresponding_authors_results.get() + if result is not None: + plot_corresponding_authors, _ = result + yield plotly_download( + plot_corresponding_authors, + title="Corresponding Authors' Countries", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.corresponding_authors_report) + def show_corresponding_authors_report(): + result = corresponding_authors_results.get() + if result is not None: + plots, corresponding_authors_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [corresponding_authors_tab], [plots], "corrauthcountries")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Corresponding Authors' Countries added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first", duration=3, type="warning") + return ui.notification_show("✅ Corresponding Authors' Countries added to report", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_corresponding_authors", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the corresponding author's countries.") + ui.input_numeric("TopCountries", "Select the number of top countries to display:", value=10, min=1) + # Reactive value to store corresponding authors results + corresponding_authors_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_corresponding_authors) + def countries_data(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "🌍 Loading country data...", + "🔎 Analyzing corresponding author countries...", + "📊 Compiling country-level statistics...", + "🗺️ Mapping global collaborations...", + "✨ Almost there! Preparing your country dashboard...", + "📈 Calculating country indicators...", + "🔗 Connecting countries and authors...", + "🌐 Exploring your international landscape...", + "🚀 Science mapping by country in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + top_k_countries = input.TopCountries() + result = get_corresponding_author_countries(df, top_k_countries) + corresponding_authors_results.set(result) + finally: + ui.modal_remove() + + with ui.navset_underline(id="corresponding_authors_tab"): + with ui.nav_panel("Plot"): + @render.ui + def show_countries_collaboration_placeholder(): + result = corresponding_authors_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the Corresponding Author's Countries analysis.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + # When result is available, show the widget + return None + + @render_widget + def show_countries_collaboration(): + result = corresponding_authors_results.get() + if result is not None: + countries_plot, _ = result + return countries_plot + + with ui.nav_panel("Table"): + @render.ui + def table_countries_collaboration(): + result = corresponding_authors_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the corresponding authors' countries data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, countries_table = result + return ui.HTML(DT(countries_table, style="width=100%;")) + + # --- Countries' Scientific Production Section --- + with ui.nav_panel("None", value="countries_scientific_production"): + with ui.layout_columns( + col_widths=(9, 3), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("🌍 Countries' Scientific Production", style="color: #5567BB;") + ui.p("The countries' scientific production of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px;"): + ui.input_action_button("countries_scientific_production_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"CountriesScientificProduction-{todaydate}.png" + ) + def download_countries_scientific_production(): + plot_countries_scientific_production, _ = countries_production() + yield plotly_download( + plot_countries_scientific_production, + title="Countries' Scientific Production", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.countries_scientific_production_report) + def show_countries_scientific_production_report(): + plots, countries_scientific_production_tab = countries_production() + report_excel.set(add_to_report(report_choices, report_excel, [countries_scientific_production_tab], [plots], "countrysciprod")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Countries' Scientific Production added to report", duration=5, close_button=False) + + with ui.card(full_screen=True): + @reactive.calc + def countries_production(): + # Show loading modal while calculating (same style as Main Information) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "🌍 Analyzing countries' scientific production...", + "📊 Processing geographical data...", + "🏆 Ranking countries by production...", + "📈 Calculating production metrics...", + "✨ Almost there! Preparing your dashboard...", + "🎯 Finalizing countries' production analysis...", + "🔗 Connecting geographical and publication data...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + result = get_countries_production(df) + return result + finally: + ui.modal_remove() + + with ui.navset_underline(id="countries_scientific_production_tab"): + with ui.nav_panel("Plot"): + @render_widget + def show_countries_production(): + countries_plot, _ = countries_production() + return countries_plot + + with ui.nav_panel("Table"): + @render.ui + def table_countries_production(): + _, countries_table = countries_production() + return ui.HTML(DT(countries_table, style="width=100%;")) + + # --- Countries' Production over Time Section --- + with ui.nav_panel("None", value="countries_production_over_time"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("🌍 Countries' Production over Time", style="color: #5567BB;") + ui.p("The countries' production over time of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_countries_over_time", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("countries_production_over_time_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"CountriesProductionOverTime-{todaydate}.png" + ) + def download_countries_production_over_time(): + result = countries_over_time_results.get() + if result is not None: + plot_countries_production_over_time, _ = result + yield plotly_download( + plot_countries_production_over_time, + title="Countries' Production over Time", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.countries_production_over_time_report) + def show_countries_production_over_time_report(): + result = countries_over_time_results.get() + if result is not None: + plots, countries_production_over_time_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [countries_production_over_time_tab], [plots], "countryprodovertime")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Countries' Production over Time added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first", duration=3, type="warning") + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_countries_production_over_time", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the countries' production over time.") + ui.input_numeric("TopCountriesProdK", "Select the number of countries to display:", value=5) + + # Reactive value to store countries over time results + countries_over_time_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_countries_over_time) + def countries_over_time(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "🌍 Loading country production over time...", + "🔎 Analyzing scientific output by country and year...", + "📊 Compiling country-level publication trends...", + "🗺️ Mapping global research productivity over time...", + "✨ Almost there! Preparing your country dashboard...", + "📈 Calculating country indicators over time...", + "🔗 Connecting countries and publications...", + "🌐 Exploring your international scientific landscape...", + "🚀 Science mapping by country in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + top_k_countries = input.TopCountriesProdK() + result = get_countries_production_over_time(df, top_k_countries) + countries_over_time_results.set(result) + finally: + ui.modal_remove() + + with ui.navset_underline(id="countries_production_over_time_tab"): + with ui.nav_panel("Plot"): + @render.ui + def show_countries_over_time_placeholder(): + result = countries_over_time_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the Countries' Production over Time analysis.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + # When result is available, show the widget + return None + + @render_widget + def show_countries_over_time(): + result = countries_over_time_results.get() + if result is not None: + countries_plot, _ = result + return countries_plot + + with ui.nav_panel("Table"): + @render.ui + def table_countries_over_time(): + result = countries_over_time_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the countries' production over time data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, countries_table = result + return ui.HTML(DT(countries_table, style="width=100%;")) + + # --- Most Cited Countries Section --- + with ui.nav_panel("None", value="most_cited_countries"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("🌍 Most Cited Countries", style="color: #5567BB;") + ui.p("The most cited countries of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_cited_countries", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("most_cited_countries_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"MostCitedCountries-{todaydate}.png" + ) + def download_most_cited_countries(): + result = cited_countries_results.get() + if result is not None: + plot_most_cited_countries, _ = result + yield plotly_download( + plot_most_cited_countries, + title="Most Cited Countries", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.most_cited_countries_report) + def show_most_cited_countries_report(): + result = cited_countries_results.get() + if result is not None: + plots, most_cited_countries_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [most_cited_countries_tab], [plots], "mostcitcountries")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Most Cited Countries added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first", duration=3, type="warning") + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_most_cited_countries", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the most cited countries.") + ui.input_numeric("num_of_cited_countries", "Select the number of countries to apply to the dataset:", value=10) + ui.input_select("cited_countries", "Citation measure", {"total_cit": "Total Citation", "average_cit": "Average Citation"}, selected="total_cit") + + # Reactive value to store cited countries results + cited_countries_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_cited_countries) + def cited_countries(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "🌍 Analyzing most cited countries...", + "📊 Processing citation data by country...", + "🏆 Ranking countries by citations...", + "📈 Calculating citation metrics...", + "✨ Almost there! Preparing your dashboard...", + "🎯 Finalizing cited countries analysis...", + "🔗 Connecting citation and country data...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + num_of_cited_countries = input.num_of_cited_countries() + cited_countries_measure = input.cited_countries() + result = get_cited_countries(df, num_of_cited_countries, cited_countries_measure) + cited_countries_results.set(result) + finally: + ui.modal_remove() + + with ui.navset_underline(id="most_cited_countries_tab"): + with ui.nav_panel("Plot"): + @render.ui + def show_cited_countries_placeholder(): + result = cited_countries_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the Most Cited Countries analysis.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + # When result is available, show the widget + return None + + @render_widget + def show_cited_countries(): + result = cited_countries_results.get() + if result is not None: + plot_cited_countries, _ = result + return plot_cited_countries + + with ui.nav_panel("Table"): + @render.ui + def table_cited_countries(): + result = cited_countries_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the most cited countries data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, cited_countries_tab = result + return ui.HTML(DT(cited_countries_tab, style="width=100%;")) + + # --- Most Global Cited Documents Section --- + with ui.nav_panel("None", value="most_global_cited_documents"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("📄 Most Global Cited Documents", style="color: #5567BB;") + ui.p("The most global cited documents of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_cited_documents", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("most_global_cited_documents_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"MostGlobalCitedDocuments-{todaydate}.png" + ) + def download_most_global_cited_documents(): + plot_most_global_cited_documents, _ = cited_documents() + yield plotly_download( + plot_most_global_cited_documents, + title="Most Global Cited Documents", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.most_global_cited_documents_report) + def show_most_global_cited_documents_report(): + plots, most_global_cited_documents_tab = cited_documents() + report_excel.set(add_to_report(report_choices, report_excel, [most_global_cited_documents_tab], [plots], "mostglobcitdocs")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Most Global Cited Documents added to report", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_most_global_cited_documents", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the most global cited documents.") + ui.input_numeric("num_of_cited_docs", "Select the number of documents to apply to the dataset:", value=10) + ui.input_select("cited_docs", "Citation measure", {"total_cit": "Total Citations", "total_cit_per_year": "Total Citations per Year"}, selected="total_cit") + + # Reactive value to store cited documents results + cited_documents_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_cited_documents) + def cited_documents(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "📄 Analyzing most global cited documents...", + "📊 Processing citation data by document...", + "🏆 Ranking documents by global citations...", + "📈 Calculating document citation metrics...", + "✨ Almost there! Preparing your dashboard...", + "🎯 Finalizing global cited documents analysis...", + "🔗 Connecting citation and document data...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + num_of_cited_docs = input.num_of_cited_docs() + cited_docs = input.cited_docs() + result = get_cited_documents(df, num_of_cited_docs, cited_docs) + cited_documents_results.set(result) + finally: + ui.modal_remove() + + with ui.navset_underline(id="most_global_cited_documents_tab"): + with ui.nav_panel("Plot"): + @render.ui + def show_cited_documents_placeholder(): + result = cited_documents_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the Most Global Cited Documents analysis.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + # When result is available, show the widget + return None + + @render_widget + def show_cited_documents(): + result = cited_documents_results.get() + if result is not None: + plot_cited_documents, _ = result + return plot_cited_documents + + with ui.nav_panel("Table"): + @render.ui + def table_cited_documents(): + result = cited_documents_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the most global cited documents data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, cited_documents_tab = result + return ui.HTML(DT(cited_documents_tab, style="width=100%;")) + + # --- Most Local Cited Documents Section --- + with ui.nav_panel("None", value="most_local_cited_documents"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("📄 Most Local Cited Documents", style="color: #5567BB;") + ui.p("The most local cited documents of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_local_cited_documents", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("most_local_cited_documents_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"MostLocalCitedDocuments-{todaydate}.png" + ) + def download_most_local_cited_documents(): + result = local_cited_documents_results.get() + if result is not None: + plot_most_local_cited_documents, _ = result + yield plotly_download( + plot_most_local_cited_documents, + title="Most Local Cited Documents", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.most_local_cited_documents_report) + def show_most_local_cited_documents_report(): + result = local_cited_documents_results.get() + if result is not None: + plots, most_local_cited_documents_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [most_local_cited_documents_tab], [plots], "mostloccitdocs")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Most Local Cited Documents added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_most_local_cited_documents", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the most local cited documents.") + ui.input_numeric("num_of_local_cited_docs", "Select the number of documents to apply to the dataset:", value=10) + ui.input_select("field_separator", "Field separator character", {"dot_comma": ";", "dot_dot": ":", "dot":"."}, selected="dot_comma") + + # Initialize reactive value for storing results + local_cited_documents_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_local_cited_documents) + def run_local_cited_documents_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "🔍 Processing local cited documents...", + "📊 Analyzing citation patterns...", + "🌐 Generating document rankings...", + "📈 Computing citation metrics...", + "✨ Almost there! Preparing your dashboard...", + "🎯 Finalizing visualization...", + "🔗 Connecting citation and document data...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + # Run analysis + num_of_local_cited_docs = input.num_of_local_cited_docs() + field_separator = input.field_separator() + result = get_local_cited_documents(df, num_of_local_cited_docs, field_separator) + local_cited_documents_results.set(result) + finally: + ui.modal_remove() + + with ui.navset_underline(id="most_local_cited_documents_tab"): + with ui.nav_panel("Plot"): + @render.ui + def placeholder_local_cited_documents(): + result = local_cited_documents_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the most local cited documents plot.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + return "" + + @render_widget + def show_local_cited_documents(): + result = local_cited_documents_results.get() + if result is not None: + plot_local_cited_documents, _ = result + return plot_local_cited_documents + + with ui.nav_panel("Table"): + @render.ui + def table_local_cited_documents(): + result = local_cited_documents_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the most local cited documents data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, local_cited_documents_tab = result + return ui.HTML(DT(local_cited_documents_tab, style="width=100%;")) + + # --- Most Local Cited References Section --- + with ui.nav_panel("None", value="most_local_cited_references"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("📘 Most Local Cited References", style="color: #5567BB;") + ui.p("The most local cited references of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_local_cited_references", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("most_local_cited_references_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"MostLocalCitedReferences-{todaydate}.png" + ) + def download_most_local_cited_references(): + result = local_cited_refs_results.get() + if result is not None: + plot_most_local_cited_references, _ = result + yield plotly_download( + plot_most_local_cited_references, + title="Most Local Cited References", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.most_local_cited_references_report) + def show_most_local_cited_references_report(): + result = local_cited_refs_results.get() + if result is not None: + plots, most_local_cited_references_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [most_local_cited_references_tab], [plots], "mostloccitrefs")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Most Local Cited References added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_most_local_cited_references", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the most local cited references.") + ui.input_numeric("num_of_cited_refs", "Select the number of references to apply to the dataset:", value=10) + ui.input_select("field_separator_ref", "Field separator character", {"dot_comma": ";", "dot_dot": ":", "dot":"."}, selected="dot_comma") + + # Initialize reactive value for storing results + local_cited_refs_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_local_cited_references) + def run_local_cited_references_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "🔍 Processing local cited references...", + "📊 Analyzing reference patterns...", + "🌐 Generating reference rankings...", + "📈 Computing citation metrics...", + "✨ Almost there! Preparing your dashboard...", + "🎯 Finalizing visualization...", + "🔗 Connecting citation and reference data...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + # Run analysis + num_of_cited_refs = input.num_of_cited_refs() + field_separator_ref = input.field_separator_ref() + result = get_local_cited_refs(df, num_of_cited_refs, field_separator_ref) + local_cited_refs_results.set(result) + finally: + ui.modal_remove() + + with ui.navset_underline(id="most_local_cited_references_tab"): + with ui.nav_panel("Plot"): + @render.ui + def placeholder_local_cited_refs(): + result = local_cited_refs_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the most local cited references plot.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + return "" + + @render_widget + def show_local_cited_refs(): + result = local_cited_refs_results.get() + if result is not None: + plot_local_cited_refs, _ = result + return plot_local_cited_refs + + with ui.nav_panel("Table"): + @render.ui + def table_local_cited_refs(): + result = local_cited_refs_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the most local cited references data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, local_cited_refs_tab = result + return ui.HTML(DT(local_cited_refs_tab, style="width=100%;")) + + # --- References Spectroscopy Section --- + with ui.nav_panel("None", value="references_spectroscopy"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("📘 References Spectroscopy", style="color: #5567BB;") + ui.p("The references spectroscopy of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_references_spectroscopy", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("references_spectroscopy_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"ReferencesSpectroscopy-{todaydate}.png" + ) + def download_references_spectroscopy(): + result = ref_spectroscopy_results.get() + if result is not None: + plot_references_spectroscopy, _, _ = result + yield plotly_download( + plot_references_spectroscopy, + title="References Spectroscopy", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.references_spectroscopy_report) + def show_references_spectroscopy_report(): + result = ref_spectroscopy_results.get() + if result is not None: + plots, ref_rpy_tab, ref_spectroscopy_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [ref_rpy_tab, ref_spectroscopy_tab], [plots], "rpys")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ References Spectroscopy added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_references_spectroscopy", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the references spectroscopy.") + ui.input_select("field_separator_spec", "Field separator character", {";": ";", ",": ",", ".":"."}, selected="dot_comma") + with ui.layout_column_wrap(width=1 / 2): + ui.input_numeric("start_year", "Star Year", value=None) + current_year = datetime.now().year + ui.input_numeric("end_year", "EndYear", value=None) + + # Initialize reactive value for storing results + ref_spectroscopy_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_references_spectroscopy) + def run_references_spectroscopy_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "🔍 Processing references spectroscopy...", + "📊 Analyzing temporal patterns...", + "🌐 Computing reference age distribution...", + "📈 Generating spectroscopy metrics...", + "✨ Almost there! Preparing your dashboard...", + "🎯 Finalizing visualization...", + "🔗 Connecting temporal and reference data...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + # Run analysis + start_year = input.start_year() + end_year = input.end_year() + field_separator_spec = input.field_separator_spec() + result = get_references_spectroscopy(df, start_year, end_year, field_separator_spec) + ref_spectroscopy_results.set(result) + finally: + ui.modal_remove() + + with ui.navset_underline(id="references_spectroscopy_tab"): + with ui.nav_panel("Plot"): + @render.ui + def placeholder_references_spectroscopy(): + result = ref_spectroscopy_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the references spectroscopy plot.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + return "" + + @render_widget + def show_references_spectroscopy(): + result = ref_spectroscopy_results.get() + if result is not None: + plot_references_spectroscopy, _, _ = result + return plot_references_spectroscopy + + with ui.nav_panel("Table - RPY"): + @render.ui + def table_references_rpy(): + result = ref_spectroscopy_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the RPY data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, ref_rpy_tab, _ = result + return ui.HTML(DT(ref_rpy_tab, style="width=100%;")) + + with ui.nav_panel("Table - Cited References"): + @render.ui + def table_references_spectroscopy(): + result = ref_spectroscopy_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the cited references data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, _, ref_spectroscopy_tab = result + return ui.HTML(DT(ref_spectroscopy_tab, style="width=100%;")) + + # --- Most Frequent Words --- + with ui.nav_panel("None", value="most_frequent_words"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("🔑 Most Frequent Words", style="color: #5567BB;") + ui.p("The most frequent words in the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_frequent_words", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("most_frequent_words_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"MostFrequentWords-{todaydate}.png" + ) + def download_most_frequent_words(): + result = frequent_words_results.get() + if result is not None: + plot_most_frequent_words, _ = result + yield plotly_download( + plot_most_frequent_words, + title="Most Frequent Words", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.most_frequent_words_report) + def show_most_frequent_words_report(): + result = frequent_words_results.get() + if result is not None: + plots, most_frequent_words_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [most_frequent_words_tab], [plots], "mostfreqwords")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Most Frequent Words added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_most_frequent_words", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the most frequent words.") + ui.input_select("field_mfw", "Field", {"ID": "Keywords Plus", "DE": "Author's Keywords (DE)", "TI": "Titles", "AB": "Abstracts", "WC": "Subject Categories (WoS)"}, selected="ID") + + @render.express() + @reactive.event(input.field_mfw) + def get_frequent_ngrams(): + if input.field_mfw() == "AB" or input.field_mfw() == "TI": + ui.input_select("ngram_mfw", "N-Grams", {1:"Unigrams", 2:"Bigrams", 3:"Trigrams"}, selected=1) + + ui.input_numeric("num_of_words_mfw", "Number of words", value=10) + + with ui.accordion(id="acc_mfw", open=False): + with ui.accordion_panel("Text Editing"): + ui.input_switch("remove_terms_mfw", "Load a list of terms to remove", value=False) + @render.express() + @reactive.event(input.remove_terms_mfw) + def get_frequent_ui(): + if input.remove_terms_mfw(): + ui.markdown("**Upload a TXT or CSV file containing a list of terms you want to remove from the analysis.** \nTerms have to be separated by a standard separator (comma, semicolon or tabulator).") + ui.input_file("upload_terms_mfw", "", accept=[".txt", ".csv"]) + ui.input_select("terms_separator_mfw", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") + + ui.input_switch("remove_synonyms_mfw", "Load a list of synonyms", value=False) + @render.express() + @reactive.event(input.remove_synonyms_mfw) + def get_synonyms_ui(): + if input.remove_synonyms_mfw(): + ui.markdown("**Upload a TXT or CSV file containing, in each row, a list of synonyms, that will be merged into a single term (the first word contained in the row).** \n Synonyms have to be separated by a standard separator (comma, semicolon or tabulator). Rows have to be separated by return separator.") + ui.input_file("upload_synonyms_mfw", "", accept=[".txt", ".csv"]) + ui.input_select("synonyms_separator_mfw", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") + + # Initialize reactive value for storing results + frequent_words_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_frequent_words) + def run_frequent_words_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "🔍 Processing frequent words...", + "📊 Analyzing word patterns...", + "🌐 Computing frequency metrics...", + "📈 Generating word rankings...", + "✨ Almost there! Preparing your dashboard...", + "🎯 Finalizing visualization...", + "🔗 Connecting word and frequency data...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + # Run analysis with current parameters + num_of_words_mfw = input.num_of_words_mfw() + field_mfw = input.field_mfw() + ngram_mfw = input.ngram_mfw() if field_mfw in ["AB", "TI"] else 1 + + file_upload_terms_mfw = None + file_upload_synonyms_mfw = None + terms_data_mfw = None + synonyms_data_mfw = None + + if input.remove_terms_mfw(): + file_upload_terms_mfw: list[FileInfo] | None = input.upload_terms_mfw() + if file_upload_terms_mfw: + with open(file_upload_terms_mfw[0]['datapath'], 'r', encoding='utf-8') as file: + terms_data_mfw = pd.DataFrame([line.strip() for line in file], columns=["Terms"]) + else: + file_upload_terms_mfw = None + terms_data_mfw = None + + if input.remove_synonyms_mfw(): + file_upload_synonyms_mfw: list[FileInfo] | None = input.upload_synonyms_mfw() + if file_upload_synonyms_mfw: + with open(file_upload_synonyms_mfw[0]['datapath'], 'r', encoding='utf-8') as file: + synonyms_data_mfw = pd.DataFrame([line.strip() for line in file], columns=["Synonyms"]) + else: + file_upload_synonyms_mfw = None + synonyms_data_mfw = None + + result = get_frequent_words(df, ngram_mfw, num_of_words_mfw, field_mfw, file_upload_terms_mfw, file_upload_synonyms_mfw) + frequent_words_results.set(result) + except Exception as e: + ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) + finally: + ui.modal_remove() + + # Show additional modal for terms/synonyms if needed + modal_content_files = [] + if terms_data_mfw is not None: + modal_content_files.append(ui.markdown("""

Terms to Remove

""")) + modal_content_files.append(ui.HTML(DT(terms_data_mfw))) + + if synonyms_data_mfw is not None: + modal_content_files.append(ui.markdown("""

Synonyms to Remove

""")) + modal_content_files.append(ui.HTML(DT(synonyms_data_mfw))) + + if modal_content_files: + modal = ui.modal( + *modal_content_files, + easy_close=True, + footer=ui.modal_button("Close", style="background: #5865B9; color: white") + ) + ui.modal_show(modal) + + with ui.navset_underline(id="most_frequent_words_tab"): + with ui.nav_panel("Plot"): + @render.ui + def placeholder_frequent_words(): + result = frequent_words_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the most frequent words plot.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + return "" + + @render_widget + def show_frequent_words(): + result = frequent_words_results.get() + if result is not None: + plot_frequent_words, _ = result + return plot_frequent_words + + with ui.nav_panel("Table"): + @render.ui + def table_frequent_words(): + result = frequent_words_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the most frequent words data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, frequent_words_tab = result + return ui.HTML(DT(frequent_words_tab, style="width=100%;")) + + # --- WordCloud Section --- + with ui.nav_panel("None", value="wordcloud"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("🔑 WordCloud", style="color: #5567BB;") + ui.p("The wordcloud of the most frequent words in the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_wordcloud", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("wordcloud_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"WordCloud-{todaydate}.png" + ) + def download_wordcloud(): + result = wordcloud_results.get() + if result is not None: + plot_wordcloud, _ = result + yield html_download( + plot_wordcloud, + title="WordCloud", + height=height.get(), + dpi=dpi.get(), + ) + + @render.ui + @reactive.event(input.wordcloud_report) + def show_wordcloud_report(): + result = wordcloud_results.get() + if result is not None: + plots, wordcloud_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [wordcloud_tab], [plots], "wordcloud")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ WordCloud added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_wordcloud", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the wordcloud.") + ui.input_select("field_wc", "Field", {"ID": "Keywords Plus", "DE": "Author's Keywords (DE)", "TI": "Titles", "AB": "Abstracts", "WC": "Subject Categories (WoS)"}, selected="ID") + + @render.express() + @reactive.event(input.field_wc) + def get_ngrams_wc(): + if input.field_wc() == "AB" or input.field_wc() == "TI": + ui.input_select("ngram_wc", "N-Grams", {1:"Unigrams", 2:"Bigrams", 3:"Trigrams"}, selected=1) + + ui.input_numeric("num_of_words_wc", "Number of words", value=50) + + with ui.accordion(id="acc_wc", open=False): + with ui.accordion_panel("Text Editing"): + ui.input_switch("remove_terms_wc", "Load a list of terms to remove", value=False) + @render.express() + @reactive.event(input.remove_terms_wc) + def get_frequent_ngrams_wc(): + if input.remove_terms_wc(): + ui.markdown("**Upload a TXT or CSV file containing a list of terms you want to remove from the analysis.** \nTerms have to be separated by a standard separator (comma, semicolon or tabulator).") + ui.input_file("upload_terms_wc", "", accept=[".txt", ".csv"]) + ui.input_select("terms_separator_wc", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") + + ui.input_switch("remove_synonyms_wc", "Load a list of synonyms", value=False) + @render.express() + @reactive.event(input.remove_synonyms_wc) + def get_synonyms_wc(): + if input.remove_synonyms_wc(): + ui.markdown("**Upload a TXT or CSV file containing, in each row, a list of synonyms, that will be merged into a single term (the first word contained in the row).** \n Synonyms have to be separated by a standard separator (comma, semicolon or tabulator). Rows have to be separated by return separator.") + ui.input_file("upload_synonyms_wc", "", accept=[".txt", ".csv"]) + ui.input_select("synonyms_separator_wc", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") + + # Initialize reactive value for storing results + wordcloud_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_wordcloud) + def run_wordcloud_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "🌫️ Generating your wordcloud...", + "🔍 Processing word frequencies...", + "🎨 Creating visual word representation...", + "📊 Calculating word weights...", + "✨ Almost there! Preparing your wordcloud...", + "🌐 Mapping word relationships...", + "🔗 Connecting words and frequencies...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + # Run analysis with current parameters + num_of_words_wc = input.num_of_words_wc() + field_wc = input.field_wc() + ngram_wc = input.ngram_wc() if field_wc in ["AB", "TI"] else 1 + + file_upload_terms_wc = None + file_upload_synonyms_wc = None + terms_data_wc = None + synonyms_data_wc = None + + if input.remove_terms_wc(): + file_upload_terms_wc: list[FileInfo] | None = input.upload_terms_wc() + if file_upload_terms_wc: + with open(file_upload_terms_wc[0]['datapath'], 'r', encoding='utf-8') as file: + terms_data_wc = pd.DataFrame([line.strip() for line in file], columns=["Terms"]) + else: + file_upload_terms_wc = None + terms_data_wc = None + + if input.remove_synonyms_wc(): + file_upload_synonyms_wc: list[FileInfo] | None = input.upload_synonyms_wc() + if file_upload_synonyms_wc: + with open(file_upload_synonyms_wc[0]['datapath'], 'r', encoding='utf-8') as file: + synonyms_data_wc = pd.DataFrame([line.strip() for line in file], columns=["Synonyms"]) + else: + file_upload_synonyms_wc = None + synonyms_data_wc = None + + result = get_wordcloud(df, ngram_wc, num_of_words_wc, field_wc, file_upload_terms_wc, file_upload_synonyms_wc) + wordcloud_results.set(result) + except Exception as e: + ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) + finally: + ui.modal_remove() + + # Show additional modal for terms/synonyms if needed + modal_content_files = [] + if terms_data_wc is not None: + modal_content_files.append(ui.markdown("""

Terms to Remove

""")) + modal_content_files.append(ui.HTML(DT(terms_data_wc))) + + if synonyms_data_wc is not None: + modal_content_files.append(ui.markdown("""

Synonyms to Remove

""")) + modal_content_files.append(ui.HTML(DT(synonyms_data_wc))) + + if modal_content_files: + modal_wc = ui.modal( + *modal_content_files, + easy_close=True, + footer=ui.modal_button("Close", style="background: #5865B9; color: white") + ) + ui.modal_show(modal_wc) + + with ui.navset_underline(id="wordcloud_tab"): + with ui.nav_panel("Plot"): + @render.ui + def placeholder_wordcloud(): + result = wordcloud_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the wordcloud.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + return "" + + @render.ui + def show_wordcloud(): + result = wordcloud_results.get() + if result is not None: + plot_wordcloud, _ = result + return ui.HTML(f'') + + with ui.nav_panel("Table"): + @render.ui + def table_wordcloud(): + result = wordcloud_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the wordcloud data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, wordcloud_tab = result + return ui.HTML(DT(wordcloud_tab, style="width=100%;")) + + # --- TreeMap Section --- + with ui.nav_panel("None", value="treemap"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("🔑 TreeMap", style="color: #5567BB;") + ui.p("The treemap of the most frequent words in the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_treemap", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("treemap_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"TreeMap-{todaydate}.png" + ) + def download_treemap(): + result = treemap_results.get() + if result is not None: + plot_treemap, _ = result + yield plotly_download( + plot_treemap, + title="TreeMap", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.treemap_report) + def show_treemap_report(): + result = treemap_results.get() + if result is not None: + plots, treemap_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [treemap_tab], [plots], "treemap")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ TreeMap added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_treemap", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the treemap.") + ui.input_select("field_tm", "Field", {"ID": "Keywords Plus", "DE": "Author's Keywords (DE)", "TI": "Titles", "AB": "Abstracts", "WC": "Subject Categories (WoS)"}, selected="ID") + + @render.express() + @reactive.event(input.field_tm) + def get_ngrams_tm(): + if input.field_tm() == "AB" or input.field_tm() == "TI": + ui.input_select("ngram_tm", "N-Grams", {1:"Unigrams", 2:"Bigrams", 3:"Trigrams"}, selected=1) + + ui.input_numeric("num_of_words_tm", "Number of words", value=50) + + with ui.accordion(id="acc_tm", open=False): + with ui.accordion_panel("Text Editing"): + ui.input_switch("remove_terms_tm", "Load a list of terms to remove", value=False) + @render.express() + @reactive.event(input.remove_terms_tm) + def get_frequent_ngrams_tm(): + if input.remove_terms_tm(): + ui.markdown("**Upload a TXT or CSV file containing a list of terms you want to remove from the analysis.** \nTerms have to be separated by a standard separator (comma, semicolon or tabulator).") + ui.input_file("upload_terms_tm", "", accept=[".txt", ".csv"]) + ui.input_select("terms_separator_tm", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") + + ui.input_switch("remove_synonyms_tm", "Load a list of synonyms", value=False) + @render.express() + @reactive.event(input.remove_synonyms_tm) + def get_synonyms_tm(): + if input.remove_synonyms_tm(): + ui.markdown("**Upload a TXT or CSV file containing, in each row, a list of synonyms, that will be merged into a single term (the first word contained in the row).** \n Synonyms have to be separated by a standard separator (comma, semicolon or tabulator). Rows have to be separated by return separator.") + ui.input_file("upload_synonyms_tm", "", accept=[".txt", ".csv"]) + ui.input_select("synonyms_separator_tm", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") + + # Initialize reactive value for storing results + treemap_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_treemap) + def run_treemap_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "🌳 Building your treemap...", + "📊 Analyzing hierarchical data...", + "🔍 Processing word categories...", + "📈 Calculating size proportions...", + "✨ Almost there! Preparing your treemap...", + "🎨 Creating visual hierarchy...", + "🔗 Connecting data and visualization...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + # Run analysis with current parameters + num_of_words_tm = input.num_of_words_tm() + field_tm = input.field_tm() + ngram_tm = input.ngram_tm() if field_tm in ["AB", "TI"] else 1 + + file_upload_terms_tm = None + file_upload_synonyms_tm = None + terms_data_tm = None + synonyms_data_tm = None + + if input.remove_terms_tm(): + file_upload_terms_tm: list[FileInfo] | None = input.upload_terms_tm() + if file_upload_terms_tm: + with open(file_upload_terms_tm[0]['datapath'], 'r', encoding='utf-8') as file: + terms_data_tm = pd.DataFrame([line.strip() for line in file], columns=["Terms"]) + else: + file_upload_terms_tm = None + terms_data_tm = None + + if input.remove_synonyms_tm(): + file_upload_synonyms_tm: list[FileInfo] | None = input.upload_synonyms_tm() + if file_upload_synonyms_tm: + with open(file_upload_synonyms_tm[0]['datapath'], 'r', encoding='utf-8') as file: + synonyms_data_tm = pd.DataFrame([line.strip() for line in file], columns=["Synonyms"]) + else: + file_upload_synonyms_tm = None + synonyms_data_tm = None + + result = get_treemap(df, ngram_tm, num_of_words_tm, field_tm, file_upload_terms_tm, file_upload_synonyms_tm) + treemap_results.set(result) + except Exception as e: + ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) + finally: + ui.modal_remove() + + # Show additional modal for terms/synonyms if needed + modal_content_files = [] + if terms_data_tm is not None: + modal_content_files.append(ui.markdown("""

Terms to Remove

""")) + modal_content_files.append(ui.HTML(DT(terms_data_tm))) + + if synonyms_data_tm is not None: + modal_content_files.append(ui.markdown("""

Synonyms to Remove

""")) + modal_content_files.append(ui.HTML(DT(synonyms_data_tm))) + + if modal_content_files: + modal_tm = ui.modal( + *modal_content_files, + easy_close=True, + footer=ui.modal_button("Close", style="background: #5865B9; color: white") + ) + ui.modal_show(modal_tm) + + with ui.navset_underline(id="treemap_tab"): + with ui.nav_panel("Plot"): + @render.ui + def placeholder_treemap(): + result = treemap_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the treemap plot.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + return "" + + @render_widget + def show_treemap(): + result = treemap_results.get() + if result is not None: + plot_treemap, _ = result + return plot_treemap + + with ui.nav_panel("Table"): + @render.ui + def table_treemap(): + result = treemap_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the treemap data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, treemap_tab = result + return ui.HTML(DT(treemap_tab, style="width=100%;")) + + # --- References Spectroscopy Section --- + with ui.nav_panel("None", value="words_frequency_over_time"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("🔑 Words' Frequency over Time", style="color: #5567BB;") + ui.p("The trend of words' frequency over time in the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_words_frequency_over_time", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("words_frequency_over_time_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"WordsFrequencyOverTime-{todaydate}.png" + ) + def download_words_frequency_over_time(): + result = word_frequency_results.get() + if result is not None: + plot_words_frequency_over_time, _ = result + yield plotly_download( + plot_words_frequency_over_time, + title="Words Frequency Over Time", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.words_frequency_over_time_report) + def show_words_frequency_over_time_report(): + result = word_frequency_results.get() + if result is not None: + plots, words_frequency_over_time_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [words_frequency_over_time_tab], [plots], "wordfreqovertime")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Words Frequency Over Time added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_words_frequency", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the words' frequency over time.") + ui.input_select("field_wf", "Field", {"ID": "Keywords Plus", "DE": "Author's Keywords (DE)", "TI": "Titles", "AB": "Abstracts"}, selected="ID") + + @render.express() + @reactive.event(input.field_wf) + def get_ngrams_wf(): + if input.field_wf() == "AB" or input.field_wf() == "TI": + ui.input_select("ngram_wf", "N-Grams", {1:"Unigrams", 2:"Bigrams", 3:"Trigrams"}, selected=1) + + with ui.accordion(id="acc_wf", multiple=True, open=False): + with ui.accordion_panel("Text Editing"): + ui.input_switch("remove_terms_wf", "Load a list of terms to remove", value=False) + @render.express() + @reactive.event(input.remove_terms_wf) + def get_frequent_ngrams_wf(): + if input.remove_terms_wf(): + ui.markdown("**Upload a TXT or CSV file containing a list of terms you want to remove from the analysis.** \nTerms have to be separated by a standard separator (comma, semicolon or tabulator).") + ui.input_file("upload_terms_wf", "", accept=[".txt", ".csv"]) + ui.input_select("terms_separator_wf", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") + + ui.input_switch("remove_synonyms_wf", "Load a list of synonyms", value=False) + @render.express() + @reactive.event(input.remove_synonyms_wf) + def get_synonyms_wf(): + if input.remove_synonyms_wf(): + ui.markdown("**Upload a TXT or CSV file containing, in each row, a list of synonyms, that will be merged into a single term (the first word contained in the row).** \n Synonyms have to be separated by a standard separator (comma, semicolon or tabulator). Rows have to be separated by return separator.") + ui.input_file("upload_synonyms_wf", "", accept=[".txt", ".csv"]) + ui.input_select("synonyms_separator_wf", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") + + with ui.accordion_panel("Additional Options"): + ui.input_select('occurrences', 'Occurrences', {'cumulate': 'Cumulate', 'per_year': 'Per Year'}, selected='cumulate') + ui.input_slider('top_words', 'Number of words', min=1, max=100, value=[1, 10]) + + # Initialize reactive value for storing results + word_frequency_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_words_frequency_over_time) + def run_words_frequency_over_time_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "📈 Analyzing words frequency over time...", + "� Processing temporal word patterns...", + "📊 Computing frequency trends...", + "⏰ Tracking word evolution over time...", + "✨ Almost there! Preparing your analysis...", + "📉 Calculating temporal indicators...", + "🔗 Connecting words and time periods...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + # Run analysis with current parameters + field_wf = input.field_wf() + ngram_wf = input.ngram_wf() if field_wf in ["AB", "TI"] else 1 + occurrences = input.occurrences() + top_words = input.top_words() + + file_upload_terms_wf = None + file_upload_synonyms_wf = None + terms_data_wf = None + synonyms_data_wf = None + + if input.remove_terms_wf(): + file_upload_terms_wf: list[FileInfo] | None = input.upload_terms_wf() + if file_upload_terms_wf: + with open(file_upload_terms_wf[0]['datapath'], 'r', encoding='utf-8') as file: + terms_data_wf = pd.DataFrame([line.strip() for line in file], columns=["Terms"]) + else: + file_upload_terms_wf = None + terms_data_wf = None + + if input.remove_synonyms_wf(): + file_upload_synonyms_wf: list[FileInfo] | None = input.upload_synonyms_wf() + if file_upload_synonyms_wf: + with open(file_upload_synonyms_wf[0]['datapath'], 'r', encoding='utf-8') as file: + synonyms_data_wf = pd.DataFrame([line.strip() for line in file], columns=["Synonyms"]) + else: + file_upload_synonyms_wf = None + synonyms_data_wf = None + + result = get_word_frequency(df, ngram_wf, field_wf, file_upload_terms_wf, file_upload_synonyms_wf, occurrences, top_words) + word_frequency_results.set(result) + except Exception as e: + ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) + finally: + ui.modal_remove() + + # Show additional modal for terms/synonyms if needed + modal_content_files = [] + if terms_data_wf is not None: + modal_content_files.append(ui.markdown("""

Terms to Remove

""")) + modal_content_files.append(ui.HTML(DT(terms_data_wf))) + + if synonyms_data_wf is not None: + modal_content_files.append(ui.markdown("""

Synonyms to Remove

""")) + modal_content_files.append(ui.HTML(DT(synonyms_data_wf))) + + if modal_content_files: + modal_wf = ui.modal( + *modal_content_files, + easy_close=True, + footer=ui.modal_button("Close", style="background: #5865B9; color: white") + ) + ui.modal_show(modal_wf) + + with ui.navset_underline(id="words_frequency_tab"): + with ui.nav_panel("Plot"): + @render.ui + def placeholder_word_frequency(): + result = word_frequency_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the words frequency over time plot.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + return "" + + @render_widget + def show_word_frequency(): + result = word_frequency_results.get() + if result is not None: + plot_word_frequency, _ = result + return plot_word_frequency + + with ui.nav_panel("Table"): + @render.ui + def table_word_frequency(): + result = word_frequency_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the words frequency over time data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, word_frequency_tab = result + return ui.HTML(DT(word_frequency_tab, style="width:100%;")) + + # --- Trend Topics Section --- + with ui.nav_panel("None", value="trend_topics"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("🔑 Trend Topics", style="color: #5567BB;") + ui.p("The trend topics in the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_trend_topics", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("trend_topics_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"TrendTopics-{todaydate}.png" + ) + def download_trend_topics(): + result = trend_topics_results.get() + if result is not None: + plot_trend_topics, _ = result + yield plotly_download( + plot_trend_topics, + title="Trend Topics", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.trend_topics_report) + def show_trend_topics_report(): + result = trend_topics_results.get() + if result is not None: + plots, trend_topics_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [trend_topics_tab], [plots], "trendtopics")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Trend Topics added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Sidebar for data import options + with ui.sidebar(id="sidebar_trend_topics", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the trend topics.") + ui.input_select("field_tt", "Field", {"ID": "Keywords Plus", "DE": "Author's Keywords (DE)", "TI": "Titles", "AB": "Abstracts"}, selected="ID") + + @render.express() + @reactive.event(input.field_tt) + def get_ngrams_tt(): + if input.field_tt() == "AB" or input.field_tt() == "TI": + ui.input_select("ngram_tt", "N-Grams", {1:"Unigrams", 2:"Bigrams", 3:"Trigrams"}, selected=1) + ui.input_select("stemming_tt", "Word Stemming", {True: "Yes", False: "No"}, selected=True) + + @render.express() + def show_timespan(): + data_temp = main_informations() + ui.input_slider("time_window", "Timespan", sep="", ticks=True, min=data_temp['Min_Year'][0], max=data_temp['Max_Year'][0], value=[data_temp['Min_Year'][0], data_temp['Max_Year'][0]], step=1, time_format="YYYY") + + with ui.accordion(id="acc_tt", multiple=True, open=False): + with ui.accordion_panel("Text Editing"): + ui.input_switch("remove_terms_tt", "Load a list of terms to remove", value=False) + @render.express() + @reactive.event(input.remove_terms_tt) + def get_frequent_ngrams_tt(): + if input.remove_terms_tt(): + ui.markdown("**Upload a TXT or CSV file containing a list of terms you want to remove from the analysis.** \nTerms have to be separated by a standard separator (comma, semicolon or tabulator).") + ui.input_file("upload_terms_tt", "", accept=[".txt", ".csv"]) + ui.input_select("terms_separator_tt", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") + + ui.input_switch("remove_synonyms_tt", "Load a list of synonyms", value=False) + @render.express() + @reactive.event(input.remove_synonyms_tt) + def get_synonyms_tt(): + if input.remove_synonyms_tt(): + ui.markdown("**Upload a TXT or CSV file containing, in each row, a list of synonyms, that will be merged into a single term (the first word contained in the row).** \n Synonyms have to be separated by a standard separator (comma, semicolon or tabulator). Rows have to be separated by return separator.") + ui.input_file("upload_synonyms_tt", "", accept=[".txt", ".csv"]) + ui.input_select("synonyms_separator_tt", "File Separator", {",":'Comma ","', ";": 'Semicolon ";"', "tab": "Tab"}, selected=",") + + with ui.accordion_panel("Additional Options"): + with ui.layout_column_wrap(width=1 / 2): + ui.input_numeric("word_mimimum_frequency", "Word Minimum Frequency", value=5) + ui.input_numeric("number_of_words_year", "Number of Words per Year", value=3) + + # Initialize reactive value for storing results + trend_topics_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_trend_topics) + def run_trend_topics_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "� Analyzing trend topics...", + "🔍 Processing topic evolution...", + "� Computing temporal patterns...", + "⏰ Tracking topic trends over time...", + "✨ Almost there! Preparing your analysis...", + "📉 Calculating topic indicators...", + "🔗 Connecting topics and time periods...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + # Run analysis with current parameters + field_tt = input.field_tt() + ngram_tt = input.ngram_tt() if field_tt in ["AB", "TI"] else 1 + time_window = input.time_window() + + file_upload_terms_tt = None + file_upload_synonyms_tt = None + terms_data_tt = None + synonyms_data_tt = None + + if input.remove_terms_tt(): + file_upload_terms_tt: list[FileInfo] | None = input.upload_terms_tt() + if file_upload_terms_tt: + with open(file_upload_terms_tt[0]['datapath'], 'r', encoding='utf-8') as file: + terms_data_tt = pd.DataFrame([line.strip() for line in file], columns=["Terms"]) + else: + file_upload_terms_tt = None + terms_data_tt = None + + if input.remove_synonyms_tt(): + file_upload_synonyms_tt: list[FileInfo] | None = input.upload_synonyms_tt() + if file_upload_synonyms_tt: + with open(file_upload_synonyms_tt[0]['datapath'], 'r', encoding='utf-8') as file: + synonyms_data_tt = pd.DataFrame([line.strip() for line in file], columns=["Synonyms"]) + else: + file_upload_synonyms_tt = None + synonyms_data_tt = None + + word_mimimum_frequency = input.word_mimimum_frequency() + number_of_words_year = input.number_of_words_year() + + result = get_trend_topics(df, ngram_tt, field_tt, time_window, file_upload_terms_tt, file_upload_synonyms_tt, word_mimimum_frequency, number_of_words_year) + trend_topics_results.set(result) + except Exception as e: + ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) + finally: + ui.modal_remove() + + # Show additional modal for terms/synonyms if needed + modal_content_files = [] + if terms_data_tt is not None: + modal_content_files.append(ui.markdown("""

Terms to Remove

""")) + modal_content_files.append(ui.HTML(DT(terms_data_tt))) + + if synonyms_data_tt is not None: + modal_content_files.append(ui.markdown("""

Synonyms to Remove

""")) + modal_content_files.append(ui.HTML(DT(synonyms_data_tt))) + + if modal_content_files: + modal_tt = ui.modal( + *modal_content_files, + easy_close=True, + footer=ui.modal_button("Close", style="background: #5865B9; color: white") + ) + ui.modal_show(modal_tt) + + with ui.navset_underline(id="trend_topics_tab"): + with ui.nav_panel("Plot"): + @render.ui + def placeholder_trend_topics(): + result = trend_topics_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the trend topics plot.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + return "" + + @render_widget + def show_trend_topics(): + result = trend_topics_results.get() + if result is not None: + plot_trend_topics, _ = result + return plot_trend_topics + + with ui.nav_panel("Table"): + @render.ui + def table_trend_topics(): + result = trend_topics_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the trend topics data table.", style="text-align: center; color: #666; font-size: 16px;"), + style="height: 400px; display: flex; flex-direction: column; justify-content: center; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + _, trend_topics_tab = result + return ui.HTML(DT(trend_topics_tab, style="width:100%;")) + + # --- Cluster Coupling --- + with ui.nav_panel("None", value="cluster_analysis"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("🔍 Cluster by Coupling", style="color: #5567BB;") + ui.p("The cluster analysis by coupling in the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_cluster_analysis", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("cluster_analysis_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"ClusterAnalysis-{todaydate}.png" + ) + def download_cluster_analysis(): + result = clustering_coupling_results.get() + if result is not None: + plot_cluster_analysis, _, _, _ = result + yield plotly_download( + plot_cluster_analysis, + title="Cluster Analysis", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.cluster_analysis_report) + def show_cluster_analysis_report(): + result = clustering_coupling_results.get() + if result is not None: + plot_clustering_coupling, clustering_coupling_network, clustering_coupling_tab, clustering_coupling_clusters = result + report_excel.set(add_to_report(report_choices, report_excel, [clustering_coupling_tab, clustering_coupling_clusters], [plot_clustering_coupling, clustering_coupling_network], "couplingmap")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Cluster Analysis added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=True, fill=True): + with ui.sidebar(id="sidebar_cluster_analysis", position="right"): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the cluster analysis.") + ui.input_select("unit_of_analysis", "Unit of Analysis", {"documents": "Documents", "authors": "Authors", "sources": "Sources"}, selected="documents") + ui.input_select("coupling_field", "Coupling measured by", {"CR": "References", "ID": "Keywords Plus", "DE": "Author's Keywords (DE)", "TI": "Titles", "AB": "Abstracts"}, selected="CR") + + @render.express() + @reactive.event(input.coupling_field) + def get_stemmer_gc(): + if input.coupling_field() == "AB" or input.coupling_field() == "TI": + ui.input_select("stemmer", "Word Stemming:", {True:"Yes", False:"No"}, selected=False) + + with ui.accordion(id="accordion_cluster_labeling", multiple=True, open=False): + with ui.accordion_panel("Impact Parameters"): + ui.input_select("impact_measure", "Impact measure", {"local": "Local Citation Score", "global": "Global Citation Score"}, selected="local") + ui.input_select("cluster_labeling", "Cluster labeling by", {"none": "None", "ID": "Keywords Plus", "DE": "Author's Keywords (DE)", "TI": "Titles", "AB": "Abstracts"}, selected="ID") + + @render.express() + @reactive.event(input.cluster_labeling) + def get_ngrams_gc(): + if input.cluster_labeling() == "AB" or input.cluster_labeling() == "TI": + ui.input_select("cl_ngram", "Select the n-gram to apply to the dataset:", {1:"Unigrams", 2:"Bigrams", 3:"Trigrams"}, selected=1) + + with ui.accordion_panel("Cluster Labeling Parameters"): + with ui.layout_column_wrap(width=1 / 2): + ui.input_numeric("num_of_units", "Number of Units", value=250) + ui.input_numeric("min_cluster_freq", "Min Cluster Freq.", value=5) + with ui.layout_column_wrap(width=1 / 2): + ui.input_numeric("label_per_cluster", "Label per cluster", value=3) + ui.input_numeric("label_size", "Label size", value=0.3) + with ui.layout_column_wrap(width=1 / 2): + ui.input_numeric("community_repulsion", "Community repulsion", value=0) + ui.input_select("clustering_algorithm", "Clustering algorithm", {"none": "None", "edge_betweenness": "Edge Betweenness", "infomap": "InfoMap", "leading_eigen": "Leading Eigenvalues", "leiden": "Leiden", "louvain": "Louvain", "spinglass": "Spinglass", "walktrap": "Walktrap"}, selected="walktrap") + + # Initialize reactive value for storing results + clustering_coupling_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_cluster_analysis) + def run_cluster_analysis_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "� Processing cluster by coupling...", + "📊 Computing coupling relationships...", + "🌐 Generating cluster structure...", + "📈 Optimizing community detection...", + "✨ Almost there! Preparing your network...", + "🎯 Finalizing network visualization...", + "🔗 Connecting documents and citations...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + # Run analysis with current parameters + unit_of_analysis = input.unit_of_analysis() + coupling_field = input.coupling_field() + stemmer = input.stemmer() if coupling_field in ["AB", "TI"] else False + impact_measure = input.impact_measure() + cluster_labeling = input.cluster_labeling() + ngram = input.cl_ngram() if cluster_labeling in ["AB", "TI"] else 1 + num_of_units = input.num_of_units() + min_cluster_freq = input.min_cluster_freq() + label_per_cluster = input.label_per_cluster() + label_size = input.label_size() + community_repulsion = input.community_repulsion() + clustering_algorithm = input.clustering_algorithm() + + result = get_clustering_coupling(df, unit_of_analysis, coupling_field, stemmer, impact_measure, cluster_labeling, ngram, num_of_units, min_cluster_freq, label_per_cluster, label_size, community_repulsion, clustering_algorithm) + clustering_coupling_results.set(result) + except Exception as e: + ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) + finally: + ui.modal_remove() + + with ui.navset_underline(id="clustering_coupling_tab"): + with ui.nav_panel("Map"): + @render.ui + def show_clustering_coupling_map(): + result = clustering_coupling_results.get() + if result is not None: + return None # Hide placeholder when data is available + else: + return ui.div( + ui.p("Click the Run Analysis button to run cluster by coupling", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + @render_widget + def clustering_coupling_map_widget(): + result = clustering_coupling_results.get() + if result is not None: + plot_clustering_coupling, _, _, _ = result + return plot_clustering_coupling + return None + + with ui.nav_panel("Network"): + @render.ui + def network_clustering_coupling(): + result = clustering_coupling_results.get() + if result is not None: + _, clustering_coupling_network, _, _ = result + return ui.HTML(f'') + else: + return ui.div( + ui.p("Click the Run Analysis button to run cluster by coupling", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Table"): + @render.ui + def table_clustering_coupling(): + result = clustering_coupling_results.get() + if result is not None: + _, _, clustering_coupling_tab, _ = result + return ui.HTML(DT(clustering_coupling_tab)) + else: + return ui.div( + ui.p("Click the Run Analysis button to run cluster by coupling", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Clusters"): + @render.ui + def cluster_clustering_coupling(): + result = clustering_coupling_results.get() + if result is not None: + _, _, _, clustering_coupling_clusters = result + return ui.HTML(DT(clustering_coupling_clusters)) + else: + return ui.div( + ui.p("Click the Run Analysis button to run cluster by coupling", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + # ui.busy_indicators.options(spinner_type="bars3") + + # # Loader indicator + # @render.ui + # def indicator_types_ui_cluster(): + # return ui.busy_indicators.use( + # spinners=input.go_clustering_coupling() > 0 + # ) + + # --- Co-occurrence Network Section --- + with ui.nav_panel("None", value="co_occurrence_network"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("🔑 Co-occurrence Network", style="color: #5567BB;") + ui.p("The co-occurrence network in the dataset") + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_co_occurrence_analysis", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("co_occurrence_network_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"CoOccurrenceNetwork-{todaydate}.png" + ) + def download_co_occurrence_network(): + result = co_occurrence_network_results.get() + if result is not None: + plot_co_occurrence_network, _, _, _ = result + yield html_download( + plot_co_occurrence_network, + title="Co-occurrence Network", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.co_occurrence_network_report) + def show_co_occurrence_network_report(): + result = co_occurrence_network_results.get() + if result is not None: + plot_co_occurrence_network, plot_density_plot, co_occurrence_network_tab, plot_degree_plot = result + report_excel.set(add_to_report(report_choices, report_excel, [co_occurrence_network_tab], [plot_co_occurrence_network, plot_density_plot, plot_degree_plot], "cowordnet")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Co-occurrence Network added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=True, fill=True): + # Sidebar for data import options + with ui.sidebar(id="sidebar_co_occurrence_network", position="right", ): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the co-occurrence network.") + ui.input_select("field_cn", "Field", {"ID": "Keywords Plus", "DE": "Author's Keywords (DE)", "TI": "Titles", "AB": "Abstracts"}, selected="ID") + + @render.express() + @reactive.event(input.field_cn) + def get_ngrams_co_occurrence(): + if input.field_cn() == "AB" or input.field_cn() == "TI": + ui.input_select("cn_ngram", "Select the n-gram to apply to the dataset:", {1:"Unigrams", 2:"Bigrams", 3:"Trigrams"}, selected=1) + + with ui.accordion(id="accordion_co_occurrence_network", multiple=True, open=False): + with ui.accordion_panel("Text Editing Parameters"): + ui.input_switch("remove_terms_cn", "Remove terms", value=False) + @render.express() + @reactive.event(input.remove_terms_cn) + def get_frequent_ui_cn(): + if input.remove_terms_cn(): + ui.input_file("upload_terms", "Load a list of terms to remove", accept=[".txt", ".csv"]) + + ui.input_switch("remove_synonyms_cn", "Remove punctuation", value=False) + @render.express() + @reactive.event(input.remove_synonyms_cn) + def get_synonyms_ui_cn(): + if input.remove_synonyms_cn(): + ui.input_file("upload_synonyms", "Load a list of synonyms to remove", accept=[".txt", ".csv"]) + + with ui.accordion_panel("Network Parameters"): + with ui.layout_column_wrap(width=1 / 2): + ui.input_select("network_layout", "Network Layout", {"auto": "Automatic layout", "fr": "Fruchterman-Reingold", "kk": "Kamada-Kawai", "circle": "Circle", "mdrl": "Multi Dimension Scaling", "graphopt": "Graphopt", "sphere": "Sphere", "star": "Star"}, selected="auto") + ui.input_select("clustering_algorithm_cn", "Clustering Algorithm", {"none": "None", "edge_betweenness": "Edge Betweenness", "infomap": "InfoMap", "leading_eigen": "Leading Eigenvalues", "leiden": "Leiden", "louvain": "Louvain", "spinglass": "Spinglass", "walktrap": "Walktrap"}, selected="walktrap") + ui.input_select("normalization_cn", "Normalization", {"association": "Association", "jaccard": "Jaccard", "inclusion": "Inclusion", "salton": "Salton", "equivalence": "Equivalence", "none": "None"}, selected="association") + ui.input_switch("color_by_year", "Node Color by Year", value=False) + + with ui.layout_column_wrap(width=1 / 2): + ui.input_numeric("num_of_nodes", "Number of Nodes", value=50, min=10) + ui.input_numeric("repulsion_force", "Repulsion Force", value=1, min=0, step=0.1) + ui.input_switch("remove_isolated", "Remove Isolated Nodes", value=True) + ui.input_numeric("min_edges", "Minimum Number of Edges", value=2, min=1) + + with ui.accordion_panel("Graphical Parameters"): + with ui.layout_column_wrap(width=1 / 2): + ui.input_numeric("node_opacity", "Node Opacity", value=0.7, min=0.1, max=1, step=0.05) + ui.input_numeric("num_of_labels", "Number of Labels", value=1000, min=0) + + with ui.layout_column_wrap(width=1 / 2): + ui.input_switch("label_cex", "Label Cex", value=True) + ui.input_select("node_shape", "Node Shape", {"circle": "Circle", "square": "Square", "dot": "Dot","ellipse": "Ellipse", "square": "Square", "text":"Text"}, selected="dot") + + with ui.layout_column_wrap(width=1 / 2): + ui.input_numeric("label_size_ls", "Label Size", value=3, min=0.1, step=0.1) + ui.input_numeric("edge_size", "Edge Size", value=5, min=0.1, step=0.1) + ui.input_switch("node_shadow", "Show Node Shadow", value=True) + ui.input_switch("edit_nodes", "Enable Node Editing", value=False) + + # Initialize reactive value for storing results + co_occurrence_network_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_co_occurrence_analysis) + def run_co_occurrence_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "�️ Processing co-occurrence network...", + "📊 Computing node relationships...", + "🌐 Building network structure...", + "📈 Optimizing layout algorithm...", + "✨ Almost there! Preparing your network...", + "🎯 Finalizing network visualization...", + "🔗 Connecting terms and concepts...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + # Run analysis with current parameters + field_cn = input.field_cn() + ngram_cn = input.cn_ngram() if field_cn in ["AB", "TI"] else 1 + network_layout = input.network_layout() + clustering_algorithm_cn = input.clustering_algorithm_cn() + normalization_cn = input.normalization_cn() + color_by_year = input.color_by_year() + num_of_nodes = input.num_of_nodes() + repulsion_force = input.repulsion_force() + remove_isolated = input.remove_isolated() + min_edges = input.min_edges() + node_opacity = input.node_opacity() + num_of_labels = input.num_of_labels() + node_shape = input.node_shape() + label_size_ls = input.label_size_ls() + edge_size = input.edge_size() + node_shadow = input.node_shadow() + edit_nodes = input.edit_nodes() + label_cex = input.label_cex() + + file_upload_terms = None + file_upload_synonyms = None + terms_data = None + synonyms_data = None + + if input.remove_terms_cn(): + file_upload_terms: list[FileInfo] | None = input.upload_terms() + if file_upload_terms: + with open(file_upload_terms[0]['datapath'], 'r', encoding='utf-8') as file: + terms_data = pd.DataFrame([line.strip() for line in file], columns=["Terms"]) + else: + # Reset terms file if checkbox is disabled + file_upload_terms = None + terms_data = None + + if input.remove_synonyms_cn(): + file_upload_synonyms: list[FileInfo] | None = input.upload_synonyms() + if file_upload_synonyms: + with open(file_upload_synonyms[0]['datapath'], 'r', encoding='utf-8') as file: + synonyms_data = pd.DataFrame([line.strip() for line in file], columns=["Synonyms"]) + else: + # Reset synonyms file if checkbox is disabled + file_upload_synonyms = None + synonyms_data = None + + # Show modal for terms/synonyms if needed + modal_content = [] + + if terms_data is not None: + modal_content.append(ui.markdown("""

Terms to Remove

""")) + modal_content.append(ui.HTML(DT(terms_data))) + + if synonyms_data is not None: + modal_content.append(ui.markdown("""

Synonyms to Remove

""")) + modal_content.append(ui.HTML(DT(synonyms_data))) + + result = get_co_occurence_network(df, field_cn, ngram_cn, network_layout, clustering_algorithm_cn, normalization_cn, color_by_year, num_of_nodes, + repulsion_force, remove_isolated, min_edges, node_opacity, num_of_labels, node_shape, label_size_ls, + edge_size, node_shadow, edit_nodes, label_cex, file_upload_terms, file_upload_synonyms) + co_occurrence_network_results.set(result) + except Exception as e: + ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) + finally: + ui.modal_remove() + + # Show modal for terms/synonyms after main processing if needed + if modal_content: + file_modal = ui.modal( + *modal_content, + easy_close=True, + footer=ui.modal_button("Close", style="background: #5865B9; color: white") + ) + ui.modal_show(file_modal) + + with ui.navset_underline(id="co_occurrence_network_tab"): + with ui.nav_panel("Network"): + @render.ui + def show_co_occurrence_network(): + result = co_occurrence_network_results.get() + if result is not None: + plot_co_occurrence_network, _, _, _ = result + return ui.HTML(f'') + else: + return ui.div( + ui.p("Click the Run Analysis button to run co-occurrence network", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Density Plot"): + @render_widget + def density_plot_widget(): + result = co_occurrence_network_results.get() + if result is not None: + _, plot_density_plot, _, _ = result + return plot_density_plot + return None + + with ui.nav_panel("Table"): + @render.ui + def table_co_occurrence_network(): + result = co_occurrence_network_results.get() + if result is not None: + _, _, co_occurrence_network_tab, _ = result + return ui.HTML(DT(co_occurrence_network_tab, style="width=100%;")) + else: + return ui.div( + ui.p("Click the Run Analysis button to run co-occurrence network", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Degree Plot"): + @render_widget + def degree_plot_widget(): + result = co_occurrence_network_results.get() + if result is not None: + _, _, _, plot_degree_plot = result + return plot_degree_plot + return None + + # --- Thematic Map Section --- + with ui.nav_panel("None", value="thematic_map"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("🗺️ Thematic Map", style="color: #5567BB;") + ui.p("The thematic map of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_thematic_map_analysis", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("thematic_map_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"ThematicMap-{todaydate}.png" + ) + def download_thematic_map(): + result = thematic_map_results.get() + if result is not None: + plot_thematic_map, _, _, _, _ = result + yield plotly_download( + plot_thematic_map, + title="Thematic Map", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.thematic_map_report) + def show_thematic_map_report(): + result = thematic_map_results.get() + if result is not None: + plot_thematic_map, thematic_map_net, thematic_map_tab, thematic_map_clusters, thematic_map_docs = result + report_excel.set(add_to_report(report_choices, report_excel, [thematic_map_tab, thematic_map_clusters, thematic_map_docs], [plot_thematic_map, thematic_map_net], "thematicmap")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Thematic Map added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + with ui.sidebar(id="sidebar_thematic_map", position="right"): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the thematic map.") + ui.input_select("thematic_field", "Text source for analysis", { + "ID": "Keywords Plus (ID)", + "DE": "Author's Keywords (DE)", + "TI": "Titles", + "AB": "Abstracts" + }, selected="ID") + + @render.express() + @reactive.event(input.thematic_field) + def show_stemmer_input(): + if input.thematic_field() in ["TI", "AB"]: + ui.input_select("thematic_stemmer", "Apply stemming?", {True: "Yes", False: "No"}, selected=False) + + @render.express() + @reactive.event(input.thematic_field) + def show_ngram_input(): + if input.thematic_field() in ["TI", "AB"]: + ui.input_select("thematic_ngram", "N-grams", { + 1: "Unigrams", + 2: "Bigrams", + 3: "Trigrams", + 4: "4-grams" + }, selected=1) + + with ui.accordion(id="accordion_thematic_map", multiple=True, open=False): + with ui.accordion_panel("Text Editing Parameters"): + with ui.layout_column_wrap(width=1/2): + ui.input_numeric("thematic_n", "Number of terms to include", value=250) + ui.input_numeric("thematic_minfreq", "Minimum frequency", value=5) + + with ui.layout_column_wrap(width=1/2): + ui.input_numeric("thematic_label_size", "Label size", value=0.5) + ui.input_numeric("thematic_n_labels", "Labels per cluster", value=1) + + ui.input_select("thematic_clustering", "Clustering algorithm", { + "walktrap": "Walktrap", + "louvain": "Louvain", + "leiden": "Leiden", + "infomap": "Infomap" + }, selected="walktrap") + + ui.input_numeric("thematic_repulsion", "Community repulsion", value=0.1) + + # Initialize reactive value for storing results + thematic_map_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_thematic_map_analysis) + def run_thematic_map_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "�️ Processing thematic map...", + "📊 Computing keyword clusters...", + "🌐 Building thematic structure...", + "📈 Optimizing cluster positioning...", + "✨ Almost there! Preparing your map...", + "🎯 Finalizing thematic visualization...", + "🔗 Connecting themes and keywords...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + # Run analysis with current parameters + field = input.thematic_field() + stemming = input.thematic_stemmer() if field in ["TI", "AB"] else False + ngram = input.thematic_ngram() if field in ["TI", "AB"] else 1 + n = input.thematic_n() + minfreq = input.thematic_minfreq() + label_size = input.thematic_label_size() + n_labels = input.thematic_n_labels() + cluster = input.thematic_clustering() + repulsion = input.thematic_repulsion() + + result = get_thematic_map(df, field, n, minfreq, ngram, stemming, + label_size, n_labels, repulsion, cluster) + thematic_map_results.set(result) + except Exception as e: + ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) + finally: + ui.modal_remove() + + with ui.navset_underline(id="thematic_tab"): + with ui.nav_panel("Map"): + @render.ui + def show_thematic_map(): + result = thematic_map_results.get() + if result is not None: + return None # Hide placeholder when data is available + else: + return ui.div( + ui.p("Click the Run Analysis button to run thematic map", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + @render_widget + def thematic_map_widget(): + result = thematic_map_results.get() + if result is not None: + plot_map, _, _, _, _ = result + return plot_map + return None + + with ui.nav_panel("Network"): + @render.ui + def network_thematic_map(): + result = thematic_map_results.get() + if result is not None: + _, thematic_map_network, _, _, _ = result + return ui.HTML(f'') + else: + return ui.div( + ui.p("Click the Run Analysis button to run thematic map", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Table"): + @render.ui + def table_thematic_map(): + result = thematic_map_results.get() + if result is not None: + _, _, thematic_map_table, _, _ = result + return ui.HTML(DT(thematic_map_table, style="width=100%;")) + else: + return ui.div( + ui.p("Click the Run Analysis button to run thematic map", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Clusters"): + @render.ui + def clusters_thematic_map(): + result = thematic_map_results.get() + if result is not None: + _, _, _, thematic_map_cluster, _ = result + return ui.HTML(DT(thematic_map_cluster, style="width=100%;")) + else: + return ui.div( + ui.p("Click the Run Analysis button to run thematic map", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Documents"): + @render.ui + def documents_thematic_map(): + result = thematic_map_results.get() + if result is not None: + _, _, _, _, thematic_map_documents = result + return ui.HTML(DT(thematic_map_documents, maxBytes="10MB", style="width=100%;")) + else: + return ui.div( + ui.p("Click the Run Analysis button to run thematic map", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + # --- Thematic Evolution Section --- + with ui.nav_panel("None", value="thematic_evolution"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("📈 Thematic Evolution", style="color: #5567BB;") + ui.p("The thematic evolution of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_thematic_evolution_analysis", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("thematic_evolution_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"ThematicEvolution-{todaydate}.png" + ) + def download_thematic_evolution(): + result = thematic_evolution_results.get() + if result is not None: + plot_thematic_evolution, _, _ = result + yield html_download( + plot_thematic_evolution, + title="Thematic Evolution", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.thematic_evolution_report) + def show_thematic_evolution_report(): + result = thematic_evolution_results.get() + if result is not None: + plot_thematic_evolution, thematic_evolution_tab, TM = result + report_excel.set(add_to_report(report_choices, report_excel, [thematic_evolution_tab], [plot_thematic_evolution], "thematicevolution")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + for i in range(len(TM)): + report_excel.set(add_to_report(report_choices, report_excel, [TM[i]['words'], TM[i]['clusters'], TM[i]['documentToClusters']], [TM[i]['map'], TM[i]['net_html']], f"te_period_{i+1}")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + + return ui.notification_show("✅ Thematic Evolution added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=True): + with ui.sidebar(id="sidebar_thematic_evolution", position="right"): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the thematic evolution.") + ui.input_select("thematic_evolution_field", "Text source for analysis", { + "ID": "Keywords Plus (ID)", + "DE": "Author's Keywords (DE)", + "TI": "Titles", + "AB": "Abstracts" + }, selected="ID") + + ui.input_switch("overlap", "Avoid Label Overlap", value=True) + + @render.express() + @reactive.event(input.thematic_evolution_field) + def show_text_editing_evolution(): + if input.thematic_evolution_field() in ["TI", "AB"]: + with ui.layout_column_wrap(width=1 / 2): + ui.input_select("thematic_evolution_stemmer", "Apply stemming?", {True: "Yes", False: "No"}, selected=False) + ui.input_select("thematic_evolution_ngram", "N-grams", { + 1: "Unigrams", + 2: "Bigrams", + 3: "Trigrams", + 4: "4-grams" + }, selected=1) + + with ui.accordion(id="accordion_thematic_evolution", multiple=True, open=False): + with ui.accordion_panel("Text Editing Parameters"): + ui.input_switch("remove_terms_te", "Remove terms", value=False) + + @render.express() + @reactive.event(input.remove_terms_te) + def get_frequent_ngrams_te(): + if input.remove_terms_te(): + ui.input_file("upload_terms_te", "Load a list of terms to remove", accept=[".txt", ".csv"]) + + ui.input_switch("remove_synonyms_te", "Remove punctuation", value=False) + + @render.express() + @reactive.event(input.remove_synonyms_te) + def get_synonyms_te(): + if input.remove_synonyms_te(): + ui.input_file("upload_synonyms_te", "Load a list of synonyms to remove", accept=[".txt", ".csv"]) + + + with ui.accordion_panel("Thematic Evolution Parameters"): + with ui.layout_column_wrap(width=1 / 2): + ui.input_numeric("thematic_evolution_n", "Number of terms to include", value=250) + ui.input_numeric("thematic_evolution_minfreq", "Minimum frequency", value=5) + + with ui.layout_column_wrap(width=1 / 2): + ui.input_select("weight_index", "Weight Index", { + "inc_index": "Inclusion Index", + "inc_weight_word_occ": "Inclusion Index weighted by Word Occurrences", + "stab_index": "Stability Index", + }, selected="inc_weight_word_occ") + ui.input_numeric("min_weight_index", "Minimum Weight Index", value=0.1, min=0.01, max=1.0, step=0.01) + + with ui.layout_column_wrap(width=1 / 2): + ui.input_numeric("thematic_evolution_label_size", "Label size", value=0.5) + ui.input_numeric("thematic_evolution_n_labels", "Labels per cluster", value=1) + + ui.input_select("thematic_evolution_clustering", "Clustering algorithm", { + "walktrap": "Walktrap", + "louvain": "Louvain", + "leiden": "Leiden", + "infomap": "Infomap" + }, selected="walktrap") + + ui.markdown("Time Slices:") + with ui.layout_column_wrap(width=1 / 2): + ui.input_numeric("number_of_cutting_points", "Number of cutting points", value=1, min=1) + + @render.express() + @reactive.event(input.number_of_cutting_points) + def get_cutting_years(): + if input.number_of_cutting_points() > 1: + for i in range(1, input.number_of_cutting_points()+1): + value = 2019 - input.number_of_cutting_points() + i + ui.input_numeric(f"cutting_year_{i}", f"Cutting Year {i}", value=value, min=1) + else: + ui.input_numeric("cutting_year", "Cutting Year", value=2019, min=1) + + # Initialize reactive value for storing results + thematic_evolution_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_thematic_evolution_analysis) + def run_thematic_evolution_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "⏰ Processing thematic evolution...", + "📊 Computing temporal patterns...", + "🌐 Building evolution structure...", + "📈 Optimizing time periods...", + "✨ Almost there! Preparing your evolution...", + "🎯 Finalizing evolution visualization...", + "🔗 Connecting themes across time...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + # Run analysis with current parameters + field = input.thematic_evolution_field() + overlap = input.overlap() + n = input.thematic_evolution_n() + minfreq = input.thematic_evolution_minfreq() + weight_index = input.weight_index() + min_weight_index = input.min_weight_index() + label_size = input.thematic_evolution_label_size() + n_labels = input.thematic_evolution_n_labels() + cluster = input.thematic_evolution_clustering() + + file_upload_terms_te = None + file_upload_synonyms_te = None + terms_data_te = None + synonyms_data_te = None + + if input.remove_terms_te(): + file_upload_terms_te: list[FileInfo] | None = input.upload_terms_te() + if file_upload_terms_te: + with open(file_upload_terms_te[0]['datapath'], 'r', encoding='utf-8') as file: + terms_data_te = pd.DataFrame([line.strip() for line in file], columns=["Terms"]) + else: + # Reset terms file if checkbox is disabled + file_upload_terms_te = None + terms_data_te = None + + if input.remove_synonyms_te(): + file_upload_synonyms_te: list[FileInfo] | None = input.upload_synonyms_te() + if file_upload_synonyms_te: + with open(file_upload_synonyms_te[0]['datapath'], 'r', encoding='utf-8') as file: + synonyms_data_te = pd.DataFrame([line.strip() for line in file], columns=["Synonyms"]) + else: + # Reset synonyms file if checkbox is disabled + file_upload_synonyms_te = None + synonyms_data_te = None + + # Show modal for terms/synonyms if needed + modal_content_te = [] + + if terms_data_te is not None: + modal_content_te.append(ui.markdown("""

Terms to Remove

""")) + modal_content_te.append(ui.HTML(DT(terms_data_te))) + + if synonyms_data_te is not None: + modal_content_te.append(ui.markdown("""

Synonyms to Remove

""")) + modal_content_te.append(ui.HTML(DT(synonyms_data_te))) + + cutting_points = input.number_of_cutting_points() + + # Prepare arguments for get_thematic_evolution + # Build the list of cutting years for thematic evolution + if cutting_points == 1: + years = [input.cutting_year()] + elif cutting_points > 1: + years = [] + for i in range(1, cutting_points+1): + years.append(input[f"cutting_year_{i}"]()) + else: + years = [] + + # Handle remove.terms and synonyms (if you want to support file upload for these) + remove_terms = terms_data_te["Terms"].tolist() if terms_data_te is not None else None + synonyms = synonyms_data_te["Synonyms"].tolist() if synonyms_data_te is not None else None + + # ngrams and stemming: only for TI/AB + ngrams = input.thematic_evolution_ngram() if field in ["TI", "AB"] else 1 + stemming = input.thematic_evolution_stemmer() if field in ["TI", "AB"] else False + + result = get_thematic_evolution(df, field, years, n, weight_index, min_weight_index, minfreq, label_size, ngrams, stemming, n_labels, overlap, remove_terms, synonyms, cluster) + thematic_evolution_results.set(result) + except Exception as e: + ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) + finally: + ui.modal_remove() + + # Show modal for terms/synonyms after main processing if needed + if modal_content_te: + file_modal_te = ui.modal( + *modal_content_te, + easy_close=True, + footer=ui.modal_button("Close", style="background: #5865B9; color: white") + ) + ui.modal_show(file_modal_te) + + @render.express() + @reactive.event(input.number_of_cutting_points) + def show_time_slices(): + with ui.navset_underline(id="thematic_evolution_tab"): + with ui.nav_panel("Thematic Evolution"): + with ui.navset_underline(id="thematic_evolution_map_tab"): + with ui.nav_panel("Map"): + @render.ui + def show_thematic_evolution_map(): + result = thematic_evolution_results.get() + if result is not None: + plot_map, _, _ = result + return ui.HTML(f'') + else: + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Table"): + @render.ui + def table_thematic_evolution(): + result = thematic_evolution_results.get() + if result is not None: + _, thematic_evolution_table, _ = result + return ui.HTML(DT(thematic_evolution_table, style="width=100%;")) + else: + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + if input.number_of_cutting_points() >= 0: + with ui.nav_panel("Time Slice 1"): + with ui.navset_underline(id="thematic_evolution_map_tab_2"): + with ui.nav_panel("Map"): + @render_widget + def show_thematic_evolution_map_2(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + return TM[0]["map"] if len(TM) > 0 else None + return None + + with ui.nav_panel("Network"): + @render.ui + def network_thematic_evolution_2(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 0: + return ui.HTML(f'') + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Table"): + @render.ui + def table_thematic_evolution_2(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 0: + return ui.HTML(DT(TM[0]["words"], style="width=100%;")) + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Clusters"): + @render.ui + def clusters_thematic_evolution_2(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 0: + return ui.HTML(DT(TM[0]["clusters"], style="width=100%;")) + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Documents"): + @render.ui + def documents_thematic_evolution_2(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 0: + return ui.HTML(DT(TM[0]["documentToClusters"], maxBytes="10MB", style="width=100%;")) + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + if input.number_of_cutting_points() >= 1: + with ui.nav_panel("Time Slice 2"): + with ui.navset_underline(id="thematic_evolution_map_tab_3"): + with ui.nav_panel("Map"): + @render_widget + def show_thematic_evolution_map_3(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + return TM[1]["map"] if len(TM) > 1 else None + return None + + with ui.nav_panel("Network"): + @render.ui + def network_thematic_evolution_3(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 1: + return ui.HTML(f'') + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Table"): + @render.ui + def table_thematic_evolution_3(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 1: + return ui.HTML(DT(TM[1]["words"], style="width=100%;")) + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Clusters"): + @render.ui + def clusters_thematic_evolution_3(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 1: + return ui.HTML(DT(TM[1]["clusters"], style="width=100%;")) + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Documents"): + @render.ui + def documents_thematic_evolution_3(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 1: + return ui.HTML(DT(TM[1]["documentToClusters"], maxBytes="10MB", style="width=100%;")) + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + if input.number_of_cutting_points() >= 2: + with ui.nav_panel("Time Slice 3"): + with ui.navset_underline(id="thematic_evolution_map_tab_4"): + with ui.nav_panel("Map"): + @render_widget + def show_thematic_evolution_map_4(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + return TM[2]["map"] if len(TM) > 2 else None + return None + + with ui.nav_panel("Network"): + @render.ui + def network_thematic_evolution_4(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 2: + return ui.HTML(f'') + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Table"): + @render.ui + def table_thematic_evolution_4(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 2: + return ui.HTML(DT(TM[2]["words"], style="width=100%;")) + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Clusters"): + @render.ui + def clusters_thematic_evolution_4(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 2: + return ui.HTML(DT(TM[2]["clusters"], style="width=100%;")) + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Documents"): + @render.ui + def documents_thematic_evolution_4(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 2: + return ui.HTML(DT(TM[2]["documentToClusters"], maxBytes="10MB", style="width=100%;")) + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + if input.number_of_cutting_points() >= 3: + with ui.nav_panel("Time Slice 4"): + with ui.navset_underline(id="thematic_evolution_map_tab_5"): + with ui.nav_panel("Map"): + @render_widget + def show_thematic_evolution_map_5(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + return TM[3]["map"] if len(TM) > 3 else None + return None + + with ui.nav_panel("Network"): + @render.ui + def network_thematic_evolution_5(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 3: + return ui.HTML(f'') + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Table"): + @render.ui + def table_thematic_evolution_5(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 3: + return ui.HTML(DT(TM[3]["words"], style="width=100%;")) + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Clusters"): + @render.ui + def clusters_thematic_evolution_5(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 3: + return ui.HTML(DT(TM[3]["clusters"], style="width=100%;")) + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Documents"): + @render.ui + def documents_thematic_evolution_5(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 3: + return ui.HTML(DT(TM[3]["documentToClusters"], maxBytes="10MB", style="width=100%;")) + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + if input.number_of_cutting_points() >= 4: + with ui.nav_panel("Time Slice 5"): + with ui.navset_underline(id="thematic_evolution_map_tab_6"): + with ui.nav_panel("Map"): + @render_widget + def show_thematic_evolution_map_6(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + return TM[4]["map"] if len(TM) > 4 else None + return None + + with ui.nav_panel("Network"): + @render.ui + def network_thematic_evolution_6(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 4: + return ui.HTML(f'') + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Table"): + @render.ui + def table_thematic_evolution_6(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 4: + return ui.HTML(DT(TM[4]["words"]), style="width=100%;") + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Clusters"): + @render.ui + def clusters_thematic_evolution_6(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 4: + return ui.HTML(DT(TM[4]["clusters"], style="width=100%;")) + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Documents"): + @render.ui + def documents_thematic_evolution_6(): + result = thematic_evolution_results.get() + if result is not None: + _, _, TM = result + if len(TM) > 4: + return ui.HTML(DT(TM[4]["documentToClusters"], maxBytes="10MB", style="width=100%;")) + return ui.div( + ui.p("Click the Run Analysis button to run thematic evolution", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + # --- Factorial Analysis Section --- + with ui.nav_panel("None", value="factorial_analysis"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("📊 Factorial Approach", style="color: #5567BB;") + ui.p("The factorial approach of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_factorial_analysis", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("factorial_analysis_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"FactorialAnalysis-{todaydate}.png" + ) + def download_factorial_analysis(): + result = factorial_analysis_results.get() + if result is not None: + plot_factorial_analysis, _, _, _ = result + yield plotly_download( + plot_factorial_analysis, + title="Factorial Analysis", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.factorial_analysis_report) + def show_factorial_analysis_report(): + result = factorial_analysis_results.get() + if result is not None: + plot_factorial_analysis, plot_dendogram, words_by_cluster, articles_by_cluster = result + report_excel.set(add_to_report(report_choices, report_excel, [words_by_cluster, articles_by_cluster], [plot_factorial_analysis, plot_dendogram], "factorialanalysis")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Factorial Analysis added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first", type="warning", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + with ui.sidebar(id="sidebar_factorial_approach", position="right"): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the factorial approach.") + + # Accordion for Factorial Analysis parameters + with ui.accordion(id="accordion_factorial_analysis", multiple=True, open=False): + with ui.accordion_panel("Analysis Method & Text Field"): + # 1 — algoritmo di riduzione + ui.input_select( + "wordmap_method", + "Analysis Method", + { + "MCA": "Multiple Correspondence Analysis (MCA)", + "CA": "Correspondence Analysis (CA)", + "MDS": "Multidimensional Scaling (MDS)", + }, + selected="MCA", + ) + + # 2 — campo testuale da analizzare + ui.input_select( + "wordmap_field", + "Text Field", + { + "ID": "Keywords Plus (ID)", + "DE": "Author's Keywords (DE)", + "TI": "Titles", + "AB": "Abstracts" + }, + selected="ID", + ) + + @render.express() + @reactive.event(input.wordmap_method) + def get_ngrams_fa(): + if input.field_tt() == "AB" or input.field_tt() == "TI": + ui.input_select("ngram_fa", "Select the n-gram to apply to the dataset:", {1:"Unigrams", 2:"Bigrams", 3:"Trigrams"}, selected=1) + + with ui.accordion_panel("Basic Plot Settings"): + with ui.layout_column_wrap(width=1 / 3): + ui.input_numeric("wordmap_dimX", "X Axis Dimension", value=0) + ui.input_numeric("wordmap_dimY", "Y Axis Dimension", value=1) + ui.input_numeric("wordmap_top_words", "Num. of terms", value=50) + + ui.input_slider( "wordmap_threshold", "Label Overlap Threshold", min=0.01, max=1.0, value=0.10 ) + + with ui.layout_column_wrap(width=1 / 2): + ui.input_numeric("wordmap_labelsize", "Label Font Size", value=16) + ui.input_numeric("wordmap_dot_size", "Base Dot Size", value=16) + + with ui.accordion_panel("Analysis Parameters"): + with ui.layout_column_wrap(width=1 / 2): + ui.input_numeric("wordmap_n_terms", "Number of Terms", value=50, min=10) + ui.input_numeric("wordmap_n_clusters", "Number of Clusters", value=1, min=1) + ui.input_numeric("wordmap_n_docs", "Documents to Use (0 = all)", value=0, min=0) + + with ui.accordion_panel("Text Editing"): + ui.input_switch("remove_terms_wm", "Remove terms", value=False) + + @render.express() + @reactive.event(input.remove_terms_wm) + def get_frequent_ngrams_wm(): + if input.remove_terms_wm(): + ui.input_file("upload_terms_wm", "Load a list of terms to remove", accept=[".txt", ".csv"]) + + ui.input_switch("remove_synonyms_wm", "Remove punctuation", value=False) + + @render.express() + @reactive.event(input.remove_synonyms_wm) + def get_synonyms_wm(): + if input.remove_synonyms_wm(): + ui.input_file("upload_synonyms_wm", "Load a list of synonyms to remove", accept=[".txt", ".csv"]) + + # Initialize reactive value for storing results + factorial_analysis_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_factorial_analysis) + def run_factorial_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "� Processing factorial analysis...", + "📊 Computing factor loadings...", + "🌐 Building dimensional space...", + "📈 Optimizing clustering algorithm...", + "✨ Almost there! Preparing your analysis...", + "🎯 Finalizing factorial visualization...", + "🔗 Connecting words and dimensions...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + # Run analysis with current parameters + field = input.wordmap_field() + ngram = input.ngram_fa() if field in ["TI", "AB"] else 1 + + file_upload_terms_wm = None + file_upload_synonyms_wm = None + terms_data_wm = None + synonyms_data_wm = None + + if input.remove_terms_wm(): + file_upload_terms_wm: list[FileInfo] | None = input.upload_terms_wm() + if file_upload_terms_wm: + with open(file_upload_terms_wm[0]['datapath'], 'r', encoding='utf-8') as file: + terms_data_wm = pd.DataFrame([line.strip() for line in file], columns=["Terms"]) + else: + # Reset terms file if checkbox is disabled + file_upload_terms_wm = None + terms_data_wm = None + + if input.remove_synonyms_wm(): + file_upload_synonyms_wm: list[FileInfo] | None = input.upload_synonyms_wm() + if file_upload_synonyms_wm: + with open(file_upload_synonyms_wm[0]['datapath'], 'r', encoding='utf-8') as file: + synonyms_data_wm = pd.DataFrame([line.strip() for line in file], columns=["Synonyms"]) + else: + # Reset synonyms file if checkbox is disabled + file_upload_synonyms_wm = None + synonyms_data_wm = None + + # Show modal for terms/synonyms if needed + modal_content_wm = [] + + if terms_data_wm is not None: + modal_content_wm.append(ui.markdown("""

Terms to Remove

""")) + modal_content_wm.append(ui.HTML(DT(terms_data_wm))) + + if synonyms_data_wm is not None: + modal_content_wm.append(ui.markdown("""

Synonyms to Remove

""")) + modal_content_wm.append(ui.HTML(DT(synonyms_data_wm))) + + n_terms=input.wordmap_n_terms() + n_clusters=input.wordmap_n_clusters() + num_documents=(input.wordmap_n_docs() or None) + method=input.wordmap_method() + dimX=input.wordmap_dimX() + dimY=input.wordmap_dimY() + topWordPlot=input.wordmap_top_words() + threshold=input.wordmap_threshold() + labelsize=input.wordmap_labelsize() + size=input.wordmap_dot_size() + + result = get_factorial_analysis(df, ngram, field, terms_data_wm, synonyms_data_wm, n_terms, n_clusters, num_documents, method, dimX, dimY, topWordPlot, threshold, labelsize, size) + factorial_analysis_results.set(result) + except Exception as e: + ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) + finally: + ui.modal_remove() + + # Show modal for terms/synonyms after main processing if needed + if modal_content_wm: + file_modal_wm = ui.modal( + *modal_content_wm, + easy_close=True, + footer=ui.modal_button("Close", style="background: #5865B9; color: white") + ) + ui.modal_show(file_modal_wm) + + with ui.navset_underline(): + with ui.nav_panel("Word Map"): + @render.ui + def show_word_map_placeholder(): + result = factorial_analysis_results.get() + if result is not None: + return None # Hide placeholder when data is available + else: + return ui.div( + ui.p("Click the Run Analysis button to run factorial analysis", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + @render_widget + def word_map_widget(): + result = factorial_analysis_results.get() + if result is not None: + plot_word_map, _, _, _ = result + return plot_word_map + return None + + with ui.nav_panel("Topic Dendrogram"): + @render.ui + def show_topic_dendrogram(): + result = factorial_analysis_results.get() + if result is not None: + _, plot_dendrogram, _, _ = result + return ui.HTML(f'') + else: + return ui.div( + ui.p("Click the Run Analysis button to run factorial analysis", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Words by Cluster"): + @render.ui + def show_words_by_cluster(): + result = factorial_analysis_results.get() + if result is not None: + _, _, words_by_cluster, _ = result + return ui.HTML(DT(words_by_cluster, style="width=100%;")) + else: + return ui.div( + ui.p("Click the Run Analysis button to run factorial analysis", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Articles by Cluster"): + @render.ui + def show_articles_by_cluster(): + result = factorial_analysis_results.get() + if result is not None: + _, _, _, articles_by_cluster = result + return ui.HTML(DT(articles_by_cluster, style="width=100%;")) + else: + return ui.div( + ui.p("Click the Run Analysis button to run factorial analysis", style="text-align: center; color: #999; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + # --- Co-citation Network Section --- + with ui.nav_panel("None", value="co-citation-network"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("📊 Co-citation Network", style="color: #5567BB;") + ui.p("The co-citation network of the dataset") + + # Reactive value to store analysis results + co_citation_results = reactive.Value(None) + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_co_citation_network", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("co_citation_network_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"CoCitationNetwork-{todaydate}.png" + ) + def download_co_citation_network(): + plot_co_citation_network, _, _, _ = co_citation_network_results.get() + yield html_download( + plot_co_citation_network, + title="Co-Citation Network", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.co_citation_network_report) + def show_co_citation_network_report(): + co_citation_net, density_plot, co_citation_tab, degree_plot = co_citation_network_results.get() + report_excel.set(add_to_report(report_choices, report_excel, [co_citation_tab], [co_citation_net, density_plot, degree_plot], "cocitnet")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Co-Citation Network added to report", duration=5, close_button=False) + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=True): + # Initialize reactive values for results + co_citation_network_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_co_citation_network) + def run_co_citation_network_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "🔗 Processing co-citation network...", + "📊 Analyzing citations relationships...", + "🌐 Building citation network structure...", + "📈 Computing network layouts...", + "✨ Almost there! Preparing your network...", + "🎯 Finalizing network visualization...", + "🔗 Connecting citations and references...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + # Get parameters + field = input.co_citation_field() + sep = input.citSep() + cocit_network_layout = input.cocit_network_layout() + cocit_clustering_algorithm = input.cocit_clustering_algorithm() + cocit_repulsion = input.cocit_repulsion() + cocit_shape = input.cocit_shape() + cocit_shadow = input.cocit_shadow() + cocit_curved = input.cocit_curved() + citlabelsize = input.citlabelsize() + citedgesize = input.citedgesize() + citlabel_cex = input.citlabel_cex() + citNodes = input.citNodes() + cit_isolates = input.cit_isolates() + citedges_min = input.citedges_min() + + # Execute analysis + result = get_co_citation( + df=df, + field=field, + sep=sep, + cocit_network_layout=cocit_network_layout, + cocit_clustering_algorithm=cocit_clustering_algorithm, + cocit_repulsion=cocit_repulsion, + cocit_shape=cocit_shape, + cocit_shadow=cocit_shadow, + cocit_curved=cocit_curved, + citlabelsize=citlabelsize, + citedgesize=citedgesize, + citlabel_cex=citlabel_cex, + citNodes=citNodes, + cit_isolates=cit_isolates, + citedges_min=citedges_min + ) + + # Store results + co_citation_network_results.set(result) + except Exception as e: + ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) + finally: + ui.modal_remove() + + with ui.sidebar(id="sidebar_co_citation_network", position="right"): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the co-citation network.") + ui.input_select("co_citation_field", "Field", + { + "CR": "Papers", + "CR_AU": "Authors", + "CR_SO": "Sources" + }, + selected="CR" + ) + ui.input_select("citSep", "Field separator character", + { + ";": '";" (Semicolon)', + ". ": '". " (Dot and 3 or more spaces)', + ",": '"," (Comma)' + }, + selected=";" + ) + + with ui.accordion(id="accordion_co_citation_network", multiple=True, open=False): + with ui.accordion_panel("Method Parameters"): + with ui.layout_column_wrap(width=1 / 2): + ui.input_select("cocit_network_layout", "Network Layout", {"auto": "Automatic layout", "fr": "Fruchterman-Reingold", "kk": "Kamada-Kawai", "circle": "Circle", "mdrl": "Multi Dimension Scaling", "graphopt": "Graphopt", "sphere": "Sphere", "star": "Star"}, selected="auto") + ui.input_select("cocit_clustering_algorithm", "Clustering Algorithm", {"none": "None", "edge_betweenness": "Edge Betweenness", "infomap": "InfoMap", "leading_eigen": "Leading Eigenvalues", "leiden": "Leiden", "louvain": "Louvain", "spinglass": "Spinglass", "walktrap": "Walktrap"}, selected="walktrap") + + with ui.layout_column_wrap(width=1 / 2): + ui.input_numeric("citNodes", "Number of Nodes", min=5, max=1000, value=50, step=1) + ui.input_numeric("cocit_repulsion", "Repulsion Force", min=0, max=1, value=0.1, step=0.1) + + with ui.layout_column_wrap(width=1 / 2): + ui.input_select("cit_isolates", "Remove Isolated Nodes", {"yes": "Yes", "no": "No"}, selected="yes") + ui.input_numeric("citedges_min", "Minimum Number of Edges", value=2, step=1, min=0) + + with ui.accordion_panel("Graphical Parameters"): + with ui.layout_column_wrap(width=1 / 2): + ui.input_select( + "citShortlabel", + "Short Label", + {"Yes": "Yes", "No": "No"}, + selected="Yes" + ) + ui.input_numeric( + "citLabels", + "Number of labels", + min=0, + max=1000, + value=1000, + step=1 + ) + with ui.layout_column_wrap(width=1 / 2): + ui.input_select( + "citlabel_cex", + "Label cex", + {"Yes": "Yes", "No": "No"}, + selected="Yes" + ) + ui.input_select( + "cocit_shape", + "Node Shape", + { + "box": "Box", + "circle": "Circle", + "dot": "Dot", + "ellipse": "Ellipse", + "square": "Square", + "text": "Text" + }, + selected="dot" + ) + with ui.layout_column_wrap(width=1 / 2): + ui.input_numeric( + "citlabelsize", + "Label size", + min=0.0, + max=20, + value=2, + step=0.10 + ) + ui.input_numeric( + "citedgesize", + "Edge size", + min=0.5, + max=20, + value=2, + step=0.5 + ) + with ui.layout_column_wrap(width=1 / 2): + ui.input_select( + "cocit_shadow", + "Node shadow", + {"Yes": "Yes", "No": "No"}, + selected="Yes" + ) + ui.input_select( + "cocit_curved", + "Edit Nodes", + {"Yes": "Yes", "No": "No"}, + selected="No" + ) + + with ui.navset_underline(): + with ui.nav_panel("Network"): + @render.ui + def show_cocitation_network(): + result = co_citation_network_results.get() + if result is not None: + plot_cocit, _, _, _ = result + return ui.HTML(f'') + else: + return ui.div( + ui.p("Click the Run Analysis button to generate the co-citation network.", style="text-align: center; color: #666; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + + with ui.nav_panel("Density"): + @render_widget + def show_cocitation_density_table(): + result = co_citation_network_results.get() + if result is not None: + _, density_plot, _, _ = result + return density_plot + else: + return ui.div( + ui.p("Click the Run Analysis button to generate the density plot.", style="text-align: center; color: #666; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Table"): + @render.ui + def show_cocitation_table(): + result = co_citation_network_results.get() + if result is not None: + _, _, cocit_table, _ = result + return ui.HTML(DT(cocit_table, style="width=100%;")) + else: + return ui.div( + ui.p("Click the Run Analysis button to generate the co-citation table.", style="text-align: center; color: #666; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Degree Plot"): + @render_widget + def show_cocitation_clusters(): + result = co_citation_network_results.get() + if result is not None: + _, _, _, degree_plot = result + return degree_plot + else: + return ui.div( + ui.p("Click the Run Analysis button to generate the degree plot.", style="text-align: center; color: #666; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + # --- Historiograph Section --- + with ui.nav_panel("None", value="historiograph"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("📊 Historiograph", style="color: #5567BB;") + ui.p("The historiograph of the dataset") + + # Reactive value to store analysis results + historiograph_results = reactive.Value(None) + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_historiograph", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("historiograph_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"Historiograph-{todaydate}.png" + ) + def download_historiograph(): + result = historiograph_results.get() + if result is not None: + _, _, plot_historiograph = result + yield html_download( + plot_historiograph, + title="Historiograph", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.historiograph_report) + def show_historiograph_report(): + result = historiograph_results.get() + if result is not None: + _, historiograph_tab, historiograph_net = result + report_excel.set(add_to_report(report_choices, report_excel, [historiograph_tab], [historiograph_net], "historiograph")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Historiograph added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first by clicking the Run Analysis button", duration=5, close_button=False, type="warning") + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=True): + # Initialize reactive values for results + historiograph_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_historiograph) + def run_historiograph_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "📚 Processing historiograph...", + "📊 Analyzing citation chronology...", + "🌐 Building historical network...", + "📈 Computing temporal layouts...", + "✨ Almost there! Preparing your timeline...", + "🎯 Finalizing historical visualization...", + "🔗 Connecting historical citations...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + # Get parameters + histNodes = input.histNodes() + histlabelsize = input.histlabelsize() + histsize = input.histsize() + # Execute analysis with correct parameters + result = get_historiograph( + df=df, + node_label="AU1", + histNodes=histNodes, + hist_isolates=True, + histlabelsize=histlabelsize, + histsize=histsize, + sep=";" + ) + + # Store results + historiograph_results.set(result) + except Exception as e: + ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) + finally: + ui.modal_remove() + + with ui.sidebar(id="sidebar_historiograph", position="right"): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the historiograph.") + ui.input_numeric( + "histNodes", + "Number of Nodes", + min=5, + max=100, + value=20, + step=1 + ) + + ui.markdown("**Graphical Parameters**") + ui.input_select( + "titlelabel", + "Node label", + { + "AU1": "Short id (1st Author, Year)", + "TI": "Document Title", + "ID": "Authors' Keywords", + "DE": "Keywords Plus" + }, + selected="AU1" + ) + + ui.input_select( + "hist_isolates", + "Remove Isolated Nodes", + {"True": "Yes", "False": "No"}, + selected="True" + ) + + with ui.layout_column_wrap(width=1/2): + ui.input_numeric( + "histlabelsize", + "Label size", + min=0.0, + max=20, + value=20, + step=1 + ) + ui.input_numeric( + "histsize", + "Node size", + min=0, + max=20, + value=20, + step=1 + ) + + with ui.navset_underline(): + with ui.nav_panel("Network"): + @render.ui + def show_hist_network(): + result = historiograph_results.get() + if result is not None: + _, _, html_path = result + return ui.HTML(f'') + else: + return ui.div( + ui.p("Click the Run Analysis button to generate the historiograph network.", style="text-align: center; color: #666; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Table"): + @render.ui + def show_hist_table(): + result = historiograph_results.get() + if result is not None: + _, hist_tab, _ = result + return ui.HTML(DT(hist_tab, style="width=100%;")) + else: + return ui.div( + ui.p("Click the Run Analysis button to generate the historiograph table.", style="text-align: center; color: #666; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + # --- Collaboration Network Section --- + with ui.nav_panel("None", value="collaboration_network"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("📊 Collaboration Network", style="color: #5567BB;") + ui.p("The collaboration network of the dataset") + + # Reactive value to store analysis results + collaboration_network_results = reactive.Value(None) + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_collaboration_network", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("collaboration_network_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"CollaborationNetwork-{todaydate}.png" + ) + def download_collaboration_network(): + result = collaboration_network_results.get() + if result is not None: + plot_collaboration_network, _, _, _ = result + yield html_download( + plot_collaboration_network, + title="Collaboration Network", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.collaboration_network_report) + def show_collaboration_network_report(): + result = collaboration_network_results.get() + if result is not None: + collaboration_net, collaboration_density_plot, collaboration_network_tab, collaboration_degree_plot = result + report_excel.set(add_to_report(report_choices, report_excel, [collaboration_network_tab], [collaboration_net, collaboration_density_plot, collaboration_degree_plot], "collabnet")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Collaboration Network added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first by clicking the Run Analysis button", duration=5, close_button=False, type="warning") + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Initialize reactive values for results + collaboration_network_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_collaboration_network) + def run_collaboration_network_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "🤝 Processing collaboration network...", + "📊 Analyzing research collaborations...", + "🌐 Building collaboration structure...", + "📈 Computing network layouts...", + "✨ Almost there! Preparing your network...", + "🎯 Finalizing collaboration visualization...", + "🔗 Connecting authors and institutions...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + # Get parameters + field = input.collab_field() + network_layout = input.collab_network_layout() + clustering_algorithm = input.collab_clustering_algorithm() + repulsion = input.collab_repulsion() + shape = input.collab_shape() + # Set default values for missing parameters + opacity = 1.0 + shadow = "Yes" + curved = "No" + colnormalize = "none" + labelsize = 2.0 + edgesize = 2.0 + label_cex = "Yes" + nodes = 50 + isolates = "yes" + edges_min = 2 + + # Execute analysis + result = get_collaboration_network( + df=df, + field=field, + network_layout=network_layout, + clustering_algorithm=clustering_algorithm, + repulsion=repulsion, + shape=shape, + opacity=opacity, + shadow=shadow, + curved=curved, + colnormalize=colnormalize, + labelsize=labelsize, + edgesize=edgesize, + label_cex=label_cex, + nodes=nodes, + isolates=isolates, + edges_min=edges_min + ) + + # Store results + collaboration_network_results.set(result) + except Exception as e: + ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) + finally: + ui.modal_remove() + + with ui.sidebar(id="sidebar_collaboration_network", position="right"): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the collaboration network.") + ui.input_select("collab_field", "Field", + { + "COL_AU": "Authors", + "COL_UN": "Institutions", + "COL_CO": "Countries" + }, + selected="COL_AU" + ) + + ui.input_select( + "colnormalize", + "Normalization", + { + "none": "None", + "association": "Association", + "jaccard": "Jaccard", + "salton": "Salton", + "inclusion": "Inclusion", + "equivalence": "Equivalence" + }, + selected="association" + ) + + with ui.accordion(id="accordion_collaboration_network", multiple=True, open=False): + with ui.accordion_panel("Method Parameters"): + with ui.layout_column_wrap(width=1 / 2): + ui.input_select("collab_network_layout", "Network Layout", {"auto": "Automatic layout", "fr": "Fruchterman-Reingold", "kk": "Kamada-Kawai", "circle": "Circle", "mdrl": "Multi Dimension Scaling", "graphopt": "Graphopt", "sphere": "Sphere", "star": "Star"}, selected="auto") + ui.input_select("collab_clustering_algorithm", "Clustering Algorithm", {"none": "None", "edge_betweenness": "Edge Betweenness", "infomap": "InfoMap", "leading_eigen": "Leading Eigenvalues", "leiden": "Leiden", "louvain": "Louvain", "spinglass": "Spinglass", "walktrap": "Walktrap"}, selected="walktrap") + + with ui.layout_column_wrap(width=1 / 2): + ui.input_numeric("collabNodes", "Number of Nodes", min=5, max=1000, value=50, step=1) + ui.input_numeric("collab_repulsion", "Repulsion Force", min=0, max=1, value=0.1, step=0.1) + + with ui.layout_column_wrap(width=1 / 2): + ui.input_select("collab_isolates", "Remove Isolated Nodes", {"yes": "Yes", "no": "No"}, selected="yes") + ui.input_numeric("collabedges_min", "Minimum Number of Edges", value=2, step=1, min=0) + + with ui.accordion_panel("Graphical Parameters"): + with ui.layout_column_wrap(width=1 / 2): + ui.input_numeric( + "collab_opacity", + "Opacity", + min=0, + max=1, + value=0.7, + step=0.05 + ) + ui.input_select( + "collabShortlabel", + "Short Label", + {"Yes": "Yes", "No": "No"}, + selected="Yes" + ) + + ui.input_numeric( + "collabLabels", + "Number of labels", + min=0, + max=1000, + value=1000, + step=1 + ) + + with ui.layout_column_wrap(width=1 / 2): + ui.input_select( + "collablabel_cex", + "Label cex", + {"Yes": "Yes", "No": "No"}, + selected="Yes" + ) + ui.input_select( + "collab_shape", + "Node Shape", + { + "box": "Box", + "circle": "Circle", + "dot": "Dot", + "ellipse": "Ellipse", + "square": "Square", + "text": "Text" + }, + selected="dot" + ) + with ui.layout_column_wrap(width=1 / 2): + ui.input_numeric( + "collablabelsize", + "Label size", + min=0.0, + max=20, + value=2, + step=0.10 + ) + ui.input_numeric( + "collabedgesize", + "Edge size", + min=0.5, + max=20, + value=2, + step=0.5 + ) + with ui.layout_column_wrap(width=1 / 2): + ui.input_select( + "collab_shadow", + "Node shadow", + {"Yes": "Yes", "No": "No"}, + selected="Yes" + ) + ui.input_select( + "collab_curved", + "Edit Nodes", + {"Yes": "Yes", "No": "No"}, + selected="No" + ) + + with ui.navset_underline(): + with ui.nav_panel("Network"): + @render.ui + def show_collaboration_network(): + result = collaboration_network_results.get() + if result is not None: + plot_collab, _, _, _ = result + return ui.HTML(f'') + else: + return ui.div( + ui.p("Click the Run Analysis button to generate the collaboration network.", style="text-align: center; color: #666; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Density"): + @render_widget + def show_collaboration_density_table(): + result = collaboration_network_results.get() + if result is not None: + _, density_plot, _, _ = result + return density_plot + else: + return ui.div( + ui.p("Click the Run Analysis button to generate the density plot.", style="text-align: center; color: #666; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Table"): + @render.ui + def show_collaboration_table(): + result = collaboration_network_results.get() + if result is not None: + _, _, collab_table, _ = result + return ui.HTML(DT(collab_table, style="width=100%;")) + else: + return ui.div( + ui.p("Click the Run Analysis button to generate the collaboration table.", style="text-align: center; color: #666; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + with ui.nav_panel("Degree Plot"): + @render_widget + def show_collaboration_clusters(): + result = collaboration_network_results.get() + if result is not None: + _, _, _, degree_plot = result + return degree_plot + else: + return ui.div( + ui.p("Click the Run Analysis button to generate the degree plot.", style="text-align: center; color: #666; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + # --- Countries Collaboration Network Section --- + with ui.nav_panel("None", value="countries_collaboration_network"): + with ui.layout_columns( + col_widths=(8, 4), + style="margin-bottom: -21px;" + ): + with ui.tags.div(style="flex: 1; bottom: 0px;"): + ui.h3("📊 Countries Collaboration Network", style="color: #5567BB;") + ui.p("The countries collaboration network of the dataset") + + with ui.tags.div(style="flex: 2; display: flex; justify-content: flex-end; gap: 5px; align-items: flex-start; bottom: 0px; width: 100%; flex-wrap: nowrap; min-width: 0;"): + ui.input_action_button("run_countries_collaboration_network", "Run Analysis", icon=ICONS["play"]) + ui.input_action_button("countries_collaboration_network_report", "Add in Report", icon=ICONS["plus"]) + + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + @render.download( + label='💾 Download', + filename=f"CountriesCollaborationNetwork-{todaydate}.png" + ) + def download_countries_collaboration_network(): + result = countries_collaboration_network_results.get() + if result is not None: + plot_countries_map, _ = result + yield plotly_download( + plot_countries_map, + title="Countries Collaboration Network", + height=height.get(), + dpi=dpi.get() + ) + + @render.ui + @reactive.event(input.countries_collaboration_network_report) + def show_countries_collaboration_network_report(): + result = countries_collaboration_network_results.get() + if result is not None: + plot_countries_map, countries_collaboration_tab = result + report_excel.set(add_to_report(report_choices, report_excel, [countries_collaboration_tab], [plot_countries_map], "collabworldmap")) + selection.set(selection.get() + (f"{list(report_choices.get().keys())[-1]}",)) + return ui.notification_show("✅ Countries Collaboration Network added to report", duration=5, close_button=False) + else: + return ui.notification_show("⚠️ Please run the analysis first by clicking the Run Analysis button", duration=5, close_button=False, type="warning") + + with ui.card(full_screen=True): + with ui.layout_sidebar(fillable=False, fill=False): + # Initialize reactive values for results + countries_collaboration_network_results = reactive.Value(None) + + @reactive.effect + @reactive.event(input.run_countries_collaboration_network) + def run_countries_collaboration_network_analysis(): + # Show loading modal while calculating (same style as Sources Production over Time) + def loading_modal(): + phrases = [ + "⏳ Loading... Please wait.", + "🌍 Processing countries collaboration network...", + "📊 Analyzing international collaborations...", + "🌐 Building world map network...", + "📈 Computing geographical layouts...", + "✨ Almost there! Preparing your world map...", + "🎯 Finalizing global visualization...", + "🔗 Connecting countries and collaborations...", + "🌐 Exploring your scientific landscape...", + "🚀 Science mapping in progress...", + ] + modal = ui.modal( + ui.div( + ui.img( + src="https://cisslaboral.laleynext.es/Img/loader-circle.gif", + height="150px", + style="display: block; margin: 0 auto; text-align: center;", + ), + ui.h4( + phrases[0], + id="loading-phrase", + style="font-size: 15px; text-align: center; margin-top: 20px; color: gray;", + ), + ), + easy_close=False, + footer=None, + ) + js = f""" + + """ + return ui.HTML(str(modal) + js) + + ui.modal_show(loading_modal()) + try: + # Execute analysis (with default parameters for world map collaboration) + result = get_world_map_collaboration( + df=df, + edges_min=1, + edgesize=5 + ) + + # Store results + countries_collaboration_network_results.set(result) + except Exception as e: + ui.notification_show(f"❌ Error in analysis: {str(e)}", type="error", duration=10) + finally: + ui.modal_remove() + + with ui.sidebar(id="sidebar_countries_collaboration_network", position="right"): + ui.h4("Parameters", style="color: #5567BB;") + ui.p("Select the parameters for the countries collaboration network.") + ui.markdown("Method Parameters:") + ui.input_numeric( + "WMedges_min", + label="Min edges", + value=2, + step=1 + ) + ui.input_numeric( + "WMedgesize", + label="Edge size", + min=0.1, + max=20, + value=5 + ) + + with ui.navset_underline(): + with ui.nav_panel("World Map"): + @render.ui + def show_world_map_placeholder(): + result = countries_collaboration_network_results.get() + if result is None: + return ui.div( + ui.p("Click the Run Analysis button to generate the world map collaboration.", style="text-align: center; color: #666; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + else: + return ui.div() # Empty when there's data + + @render_widget + def show_world_map_collaboration(): + result = countries_collaboration_network_results.get() + if result is not None: + plot_world_map, _ = result + return plot_world_map + else: + return None + + with ui.nav_panel("Table"): + @render.ui + def show_world_map_collaboration_table(): + result = countries_collaboration_network_results.get() + if result is not None: + _, world_map_table = result + return ui.HTML(DT(world_map_table, style="width=100%;")) + else: + return ui.div( + ui.p("Click the Run Analysis button to generate the world map collaboration table.", style="text-align: center; color: #666; font-size: 16px;"), + style="display: flex; flex-direction: column; justify-content: center; align-items: center; height: 300px; border: 2px dashed #ddd; border-radius: 10px; margin: 20px;" + ) + + # --- Report Section --- + with ui.nav_panel("None", value="report"): + ui.h3("⚙️ Report", style="color: #5567BB;") + ui.p("The report of the analysis") + + with ui.card(full_screen=True): + with ui.layout_column_wrap(width=1 / 2): + with ui.card(): + ui.card_header("Select results to include in the Report", style="background: #4379cd; color: white;") + choices = {"empty": ui.span("Empty Report")} + ui.input_checkbox_group( + "report_selection", + "", + choices=choices, + selected=["empty"], + ) + + @reactive.effect + def update_selection(): + user_selection = input.report_selection() + selection.set(user_selection) + + @render.ui + @reactive.event(input.go_report) + def show_report_selection(): + choices = report_choices.get() if report_choices.get() != {} else {"empty": ui.span("Empty Report")} + return ui.update_checkbox_group( + "report_selection", + choices=choices, + selected=selection.get(), + ) + + @render.ui + @reactive.event(input.select_all) + def select_all_report(): + selected_choices = choices if report_choices.get() == {} else report_choices.get() + selection.set(list(selected_choices.keys())) + return ui.update_checkbox_group( + "report_selection", + selected=selection.get(), + ) + + @render.ui + @reactive.event(input.deselect_all) + def deselect_all_report(): + selection.set([]) + return ui.update_checkbox_group( + "report_selection", + selected=selection.get(), + ) + + @render.ui + @reactive.event(input.delete) + def delete_report(): + report_choices.set({}) + report_excel.set(io.BytesIO()) + selection.set(["empty"]) + return ui.update_checkbox_group( + "report_selection", + choices={"empty": ui.span("Empty Report")}, + selected=selection.get(), + ) + + with ui.layout_column_wrap(width=1 / 1): + ui.input_action_button("select_all", "Select All", width="50%", icon=ICONS["plus"], class_="btn btn-primary") + ui.input_action_button("deselect_all", "Deselect All", width="50%", icon=ICONS["minus"], class_="btn btn-primary") + ui.input_action_button("delete", "Delete Report", width="50%", icon=ICONS["delete"], class_="btn btn-danger") + + with ui.layout_columns( + col_widths={"sm": (6, 6)}, + ): + with ui.tags.div(style="flex: 1;"): + @render.express() + @reactive.event(input.go_report, input.delete) + def show_download_report(): + todaydate = datetime.today() + todaydate = todaydate.strftime("%Y-%m-%d") + if report_excel.get().getbuffer().nbytes > 0: + @render.download(label="💾 Generate Report", filename=f"BiblioshinyReport-{todaydate}.xlsx") + def download_report(): + report = report_excel.get() + yield report.getvalue() + + # --- Settings Section --- + with ui.nav_panel("None", value="settings"): + ui.h3("⚙️ Settings", style="color: #5567BB;") + + with ui.card(full_screen=True, style="margin-top: 20px;"): + ui.h4("✨ Biblio AI API Key:") + ui.markdown("Set a valid API Key to use 'Biblio AI' features powered by Google Gemini. If you don’t have one yet, you can generate it by logging into [AI Studio](https://aistudio.google.com/app/apikey) with your Google account and creating a new API Key.") + ui.input_password("gemini_api_key", "Enter your Gemini API Key:", placeholder="Enter your Gemini API key here", width="50%") + + @render.ui + @reactive.event(input.save_api) + def save_api_key(): + if len(input.gemini_api_key())>=10: + gemini_api_key.set(input.gemini_api_key()) + masked = "*" * (len(input.gemini_api_key()) - 4) + input.gemini_api_key()[-4:] + return ui.markdown(f"✅ API Key has been set: {masked}") + else: + return ui.markdown("❌ API Key seems to be not valid") + + @render.express() + @reactive.event(input.reset_api) + def reset_api_key(): + _ = gemini_api_key.set("") + + with ui.layout_columns( + col_widths={"sm": (12)}, + ): + with ui.tags.div(style="flex: 1;"): + ui.input_action_button("save_api", "Set API Key", icon=ICONS["save"]) + ui.input_action_button("reset_api", "Remove API Key", icon=ICONS["delete"]) + + ui.hr() + ui.h4("📊 Plot settings:") + ui.input_slider("plot_dpi", "Please select the desired DPI", sep="", ticks=True, min=75, max=600, value=300, step=15) + ui.input_slider("plot_height", "Please select the desired height in inches", sep="", ticks=True, min=5, max=15, value=7, step=1) + + @reactive.calc + def plot_settings(): + # Get the values from the inputs + dpi.set(input.plot_dpi()) + height.set(input.plot_height()) + + @render.express() + @reactive.event(input.plot_dpi, input.plot_height, input.gemini_api_key) + def update_plot_settings(): + plot_settings() + + +# --- Sidebar Management --- +@render.express() +@reactive.event(input.start_button) +def toggle_sidebar(): + with ui.tags.div(id="sidebar_2", class_="custom-sidebar"): + with ui.accordion(id="sidebar_accordion_data", multiple=False, open=False): + # Info Section + with ui.accordion_panel("Biblioshiny", icon=ICONS["home_colored"]): + ui.input_action_button("go_about_2", "Biblioshiny", class_="sidebar-button", icon=ICONS["home"]) + # Data Section + with ui.accordion_panel("Data", icon=ICONS["database_colored"]): + ui.input_action_button("go_import_2", "Import or Load", class_="sidebar-button", icon=ICONS["data"]) + ui.input_action_button("go_api_2", "API", class_="sidebar-button", icon=ICONS["api"]) + ui.input_action_button("go_collections_2", "Merge Collection", class_="sidebar-button", icon=ICONS["merge"]) + + # Filters Section + with ui.accordion_panel("Filters", icon=ICONS["filters_colored"]): + ui.input_action_button("go_filters", "Filters", class_="sidebar-button", icon=ICONS["filters"]) + + # Analysis Section + with ui.accordion_panel("Overview", icon=ICONS["play_colored"]): + ui.input_action_button("go_main", "Main Information", class_="sidebar-button", icon=ICONS["overview"]) + ui.input_action_button("go_annual_scientific_production", "Annual Scientific Production", class_="sidebar-button", icon=ICONS["annual_growth_rate"]) + ui.input_action_button("go_average_citations_per_year", "Average Citations per Year", class_="sidebar-button", icon=ICONS["average_citations_per_doc"]) + ui.input_action_button("go_three_field_plot", "Three-Field Plot", class_="sidebar-button", icon=ICONS["overview"]) + with ui.accordion_panel("Sources", icon=ICONS["sources_colored"]): + ui.input_action_button("go_most_relevant_sources", "Most Relevant Sources", class_="sidebar-button", icon=ICONS["book_open"] if "book_open" in ICONS else ICONS["sources"]), + ui.input_action_button("go_most_local_cited_sources", "Most Local Cited Sources", class_="sidebar-button", icon=ICONS["book"] if "book" in ICONS else ICONS["sources"]), + ui.input_action_button("go_bradfords_law", "Bradford's Law", class_="sidebar-button", icon=ICONS["annual_growth_rate"]), + ui.input_action_button("go_sources_local_impact", "Sources' Local Impact", class_="sidebar-button", icon=ICONS["star"] if "star" in ICONS else ICONS["sources"]), + ui.input_action_button("go_sources_production_over_time", "Sources' Production over Time", class_="sidebar-button", icon=ICONS["calendar"] if "calendar" in ICONS else ICONS["timespan"]), + with ui.accordion_panel("Authors", icon=ICONS["authors_colored"]): + # Authors Section + ui.span("Authors", style="color: gray;") + ui.input_action_button("go_most_relevant_authors", "Most Relevant Authors", class_="sidebar-button", icon=ICONS["authors"]) + ui.input_action_button("go_most_local_cited_authors", "Most Local Cited Authors", class_="sidebar-button", icon=ICONS["authors_single_authored_docs"]) + ui.input_action_button("go_authors_production_over_time", "Authors' Production over Time", class_="sidebar-button", icon=ICONS["annual_growth_rate"]) + ui.input_action_button("go_lotkas_law", "Lotka's Law", class_="sidebar-button", icon=ICONS["overview"]) + ui.input_action_button("go_authors_local_impact", "Authors' Local Impact", class_="sidebar-button", icon=ICONS["star"] if "star" in ICONS else ICONS["authors"]) + # Affiliations Section + ui.span("Affiliations", style="color: gray;") + ui.input_action_button("go_most_relevant_affiliations", "Most Relevant Affiliations", class_="sidebar-button", icon=ICONS["database"]) + ui.input_action_button("go_affiliations_production_over_time", "Affiliations' Production over Time", class_="sidebar-button", icon=ICONS["annual_growth_rate"]) + # Countries Section + ui.span("Countries", style="color: gray;") + ui.input_action_button("go_corresponding_authors_countries", "Corresponding Author's Countries", class_="sidebar-button", icon=ICONS["international_co_authorship"]) + ui.input_action_button("go_countries_scientific_production", "Countries' Scientific Production", class_="sidebar-button", icon=ICONS["international_co_authorship"]) + ui.input_action_button("go_countries_production_over_time", "Countries' Production over Time", class_="sidebar-button", icon=ICONS["annual_growth_rate"]) + ui.input_action_button("go_most_cited_countries", "Most Cited Countries", class_="sidebar-button", icon=ICONS["book"]) + with ui.accordion_panel("Documents", icon=ICONS["documents_colored"]): + # Documents Section + ui.span("Documents", style="color: gray;") + ui.input_action_button("go_most_global_cited_documents", "Most Global Cited Documents", class_="sidebar-button", icon=ICONS["documents"]) + ui.input_action_button("go_most_local_cited_documents", "Most Local Cited Documents", class_="sidebar-button", icon=ICONS["documents"]) + + # Cited References Section + ui.span("Cited References", style="color: gray;") + ui.input_action_button("go_most_local_cited_references", "Most Local Cited References", class_="sidebar-button", icon=ICONS["references"]) + ui.input_action_button("go_references_spectroscopy", "References Spectroscopy", class_="sidebar-button", icon=ICONS["references"]) + + # Words Section + ui.span("Words", style="color: gray;") + ui.input_action_button("go_most_frequent_words", "Most Frequent Words", class_="sidebar-button", icon=ICONS["authors_keywords_de"]) + ui.input_action_button("go_wordcloud", "WordCloud", class_="sidebar-button", icon=ICONS["authors_keywords_de"]) + ui.input_action_button("go_treemap", "TreeMap", class_="sidebar-button", icon=ICONS["overview"]) + ui.input_action_button("go_words_frequency_over_time", "Words' Frequency over Time", class_="sidebar-button", icon=ICONS["annual_growth_rate"]) + ui.input_action_button("go_trend_topics", "Trend Topics", class_="sidebar-button", icon=ICONS["annual_growth_rate"]) + + with ui.accordion_panel("Clustering", icon=ICONS["clustering_colored"]): + ui.input_action_button("go_clustering", "Clustering", class_="sidebar-button", icon=ICONS["clustering"]) + + with ui.accordion_panel("Conceptual Structure", icon=ICONS["conceptual_structure_colored"]): + ui.span("Network Approach", style="color: gray;") + ui.input_action_button("go_cooccurrence_network", "Co-occurrence Network", class_="sidebar-button", icon=ICONS["clustering"]) + ui.input_action_button("go_thematic_map", "Thematic Map", class_="sidebar-button", icon=ICONS["overview"]) + ui.input_action_button("go_thematic_evolution", "Thematic Evolution", class_="sidebar-button", icon=ICONS["annual_growth_rate"]) + + ui.span("Factorial Approach", style="color: gray;") + ui.input_action_button("go_factorial_analysis", "Factorial Analysis", class_="sidebar-button", icon=ICONS["overview"]) + + with ui.accordion_panel("Intellectual Structure", icon=ICONS["intellectual_structure_colored"]): + ui.input_action_button("go_citation_network", "Citation Network", class_="sidebar-button", icon=ICONS["references"]) + ui.input_action_button("historiograph", "Historiograph", class_="sidebar-button", icon=ICONS["annual_growth_rate"]) + + with ui.accordion_panel("Social Structure", icon=ICONS["social_structure_colored"]): + ui.input_action_button("go_collaboration_network", "Collaboration Network", class_="sidebar-button", icon=ICONS["co_authors_per_doc"]) + ui.input_action_button("go_countries_collaboration_network", "Countries Collaboration Network", class_="sidebar-button", icon=ICONS["international_co_authorship"]) + + with ui.accordion_panel("Report", icon=ICONS["report_colored"]): + ui.input_action_button("go_report", "Report", class_="sidebar-button", icon=ICONS["report"]) + with ui.accordion_panel("Settings", icon=ICONS["settings_colored"]): + ui.input_action_button("go_settings", "Settings", class_="sidebar-button", icon=ICONS["settings"]) + + # --- Footer --- + # Use static positioning and margin-top to avoid overlap with accordion content + with ui.tags.footer( + class_="custom-footer", + style=( + "background: #5567BB; color: white; text-align: center; padding: 10px 0;" + "position: static; width: 300px; z-index: 1000; margin-top: 30px;" + "transition: background-color 0.3s ease;" + ), + ): + ui.markdown( + """ +
+ © 2025 + + + + + Bibliometrix + +
+

Version: 1.0.0 - Shiny for Python Based Application

+ """ + ) + + +# --- Javascript for Sidebar --- +ui.tags.script(""" + // Helper to show/hide both sidebars in a coordinated way + function setSidebarState(show) { + const sidebar = document.getElementById("sidebar"); + const sidebar_2 = document.getElementById("sidebar_2"); + const content = document.getElementById("mainContent"); + if (sidebar) sidebar.classList.toggle("sidebar-hidden", !show); + if (sidebar_2) sidebar_2.classList.toggle("sidebar-hidden", !show); + if (content) content.classList.toggle("full-width", !show); + } + + // Hide sidebars on page load + document.addEventListener("DOMContentLoaded", function() { + setSidebarState(false); + }); + + // Toggle both sidebars on button click + document.getElementById("toggleSidebar").addEventListener("click", function() { + const sidebar = document.getElementById("sidebar"); + // If either sidebar is visible, hide both; otherwise, show both + const isVisible = sidebar && !sidebar.classList.contains("sidebar-hidden"); + setSidebarState(!isVisible); + }); + + // Listen for Shiny events that might add/remove sidebar_2 dynamically + // and keep them in sync + const observer = new MutationObserver(function(mutations) { + mutations.forEach(function(mutation) { + if (mutation.addedNodes.length > 0 || mutation.removedNodes.length > 0) { + // Always keep both sidebars in the same state + const sidebar = document.getElementById("sidebar"); + const sidebar_2 = document.getElementById("sidebar_2"); + if (sidebar && sidebar_2) { + const sidebarHidden = sidebar.classList.contains("sidebar-hidden"); + sidebar_2.classList.toggle("sidebar-hidden", sidebarHidden); + } + } + }); + }); + observer.observe(document.body, { childList: true, subtree: true }); + + // Show both sidebars when 'start_button' is clicked + document.addEventListener("click", function(e) { + if (e.target && e.target.id === "start_button") { + setSidebarState(true); + } + }); +""") + + +# --- Navigation Logic --- +# This section handles the navigation logic between the main panels of the app. +# Each handler listens for an event (typically a click on a sidebar button) +# and updates the selected main panel via ui.update_navs("hidden_tabs", selected=...) + +# --- Section 1: Info --- +@reactive.effect +@reactive.event(input.btn_import_data) +def _(): + ui.update_navs("hidden_tabs", selected="import") + +@reactive.effect +@reactive.event(input.go_about) +def _(): + ui.update_navs("hidden_tabs", selected="info") + +@reactive.effect +@reactive.event(input.go_about_2) +def _(): + ui.update_navs("hidden_tabs", selected="info") + +# --- Section 2: Data --- +@reactive.effect +@reactive.event(input.go_import) +def _(): + ui.update_navs("hidden_tabs", selected="import") + +@reactive.effect +@reactive.event(input.go_import_2) +def _(): + ui.update_navs("hidden_tabs", selected="import") + +@reactive.effect +@reactive.event(input.go_api) +def _(): + ui.update_navs("hidden_tabs", selected="API") + +@reactive.effect +@reactive.event(input.go_api_2) +def _(): + ui.update_navs("hidden_tabs", selected="API") + +@reactive.effect +@reactive.event(input.go_collections) +def _(): + ui.update_navs("hidden_tabs", selected="collections") + +@reactive.effect +@reactive.event(input.go_collections_2) +def _(): + ui.update_navs("hidden_tabs", selected="collections") + +# --- Section 3: Filters --- +@reactive.effect +@reactive.event(input.go_filters) +def _(): + ui.update_navs("hidden_tabs", selected="filters") + +# --- Section 4: Analysis (Overview) --- +@reactive.effect +@reactive.event(input.go_main) +def _(): + ui.update_navs("hidden_tabs", selected="overview") + +@reactive.effect +@reactive.event(input.go_annual_scientific_production) +def _(): + ui.update_navs("hidden_tabs", selected="annual_scientific_production") + +@reactive.effect +@reactive.event(input.go_average_citations_per_year) +def _(): + ui.update_navs("hidden_tabs", selected="average_citations_per_year") + +@reactive.effect +@reactive.event(input.go_three_field_plot) +def _(): + ui.update_navs("hidden_tabs", selected="three_field_plot") + +# --- Section 5: Sources --- +@reactive.effect +@reactive.event(input.go_most_relevant_sources) +def _(): + ui.update_navs("hidden_tabs", selected="most_relevant_sources") + +@reactive.effect +@reactive.event(input.go_most_local_cited_sources) +def _(): + ui.update_navs("hidden_tabs", selected="most_local_cited_sources") + +@reactive.effect +@reactive.event(input.go_bradfords_law) +def _(): + ui.update_navs("hidden_tabs", selected="bradfords_law") + +@reactive.effect +@reactive.event(input.go_sources_local_impact) +def _(): + ui.update_navs("hidden_tabs", selected="sources_local_impact") + +@reactive.effect +@reactive.event(input.go_sources_production_over_time) +def _(): + ui.update_navs("hidden_tabs", selected="sources_production") + +# --- Section 6: Authors --- +@reactive.effect +@reactive.event(input.go_most_relevant_authors) +def _(): + ui.update_navs("hidden_tabs", selected="most_relevant_authors") + +@reactive.effect +@reactive.event(input.go_most_local_cited_authors) +def _(): + ui.update_navs("hidden_tabs", selected="most_local_cited_authors") + +@reactive.effect +@reactive.event(input.go_authors_production_over_time) +def _(): + ui.update_navs("hidden_tabs", selected="authors_production") + +@reactive.effect +@reactive.event(input.go_lotkas_law) +def _(): + ui.update_navs("hidden_tabs", selected="lotka_law") + +@reactive.effect +@reactive.event(input.go_authors_local_impact) +def _(): + ui.update_navs("hidden_tabs", selected="authors_local_impact") + +@reactive.effect +@reactive.event(input.go_most_relevant_affiliations) +def _(): + ui.update_navs("hidden_tabs", selected="most_relevant_affiliations") + +@reactive.effect +@reactive.event(input.go_affiliations_production_over_time) +def _(): + ui.update_navs("hidden_tabs", selected="affiliations_production") + +@reactive.effect +@reactive.event(input.go_corresponding_authors_countries) +def _(): + ui.update_navs("hidden_tabs", selected="corresponding_authors") + +@reactive.effect +@reactive.event(input.go_countries_scientific_production) +def _(): + ui.update_navs("hidden_tabs", selected="countries_scientific_production") + +@reactive.effect +@reactive.event(input.go_countries_production_over_time) +def _(): + ui.update_navs("hidden_tabs", selected="countries_production_over_time") + +@reactive.effect +@reactive.event(input.go_most_cited_countries) +def _(): + ui.update_navs("hidden_tabs", selected="most_cited_countries") + +# --- Section 7: Documents --- +@reactive.effect +@reactive.event(input.go_most_global_cited_documents) +def _(): + ui.update_navs("hidden_tabs", selected="most_global_cited_documents") + +@reactive.effect +@reactive.event(input.go_most_local_cited_documents) +def _(): + ui.update_navs("hidden_tabs", selected="most_local_cited_documents") + +@reactive.effect +@reactive.event(input.go_most_local_cited_references) +def _(): + ui.update_navs("hidden_tabs", selected="most_local_cited_references") + +@reactive.effect +@reactive.event(input.go_references_spectroscopy) +def _(): + ui.update_navs("hidden_tabs", selected="references_spectroscopy") + +@reactive.effect +@reactive.event(input.go_most_frequent_words) +def _(): + ui.update_navs("hidden_tabs", selected="most_frequent_words") + +@reactive.effect +@reactive.event(input.go_wordcloud) +def _(): + ui.update_navs("hidden_tabs", selected="wordcloud") + +@reactive.effect +@reactive.event(input.go_treemap) +def _(): + ui.update_navs("hidden_tabs", selected="treemap") + +@reactive.effect +@reactive.event(input.go_words_frequency_over_time) +def _(): + ui.update_navs("hidden_tabs", selected="words_frequency_over_time") + +@reactive.effect +@reactive.event(input.go_trend_topics) +def _(): + ui.update_navs("hidden_tabs", selected="trend_topics") + +# --- Section 8: Network Analysis --- +@reactive.effect +@reactive.event(input.go_cooccurrence_network) +def _(): + ui.update_navs("hidden_tabs", selected="co_occurrence_network") + +@reactive.effect +@reactive.event(input.go_citation_network) +def _(): + ui.update_navs("hidden_tabs", selected="co_citation_network") + +# --- Section 9: Cluster Analysis --- +@reactive.effect +@reactive.event(input.go_clustering) +def _(): + ui.update_navs("hidden_tabs", selected="cluster_analysis") + +# --- Section 10: Co-occurrence Network (alias) --- +@reactive.effect +@reactive.event(input.go_cooccurrence_network) +def _(): + ui.update_navs("hidden_tabs", selected="co-occurrence-network") + +@reactive.effect +@reactive.event(input.go_thematic_map) +def _(): + ui.update_navs("hidden_tabs", selected="thematic_map") + +# --- Section 11: Thematic Evolution --- +@reactive.effect +@reactive.event(input.go_thematic_evolution) +def _(): + ui.update_navs("hidden_tabs", selected="thematic_evolution") + +# --- Section 12: Factorial Approach --- +@reactive.effect +@reactive.event(input.go_factorial_analysis) +def _(): + ui.update_navs("hidden_tabs", selected="factorial_analysis") + +# --- Section 13: Co-citation Network (alias) --- +@reactive.effect +@reactive.event(input.go_citation_network) +def _(): + ui.update_navs("hidden_tabs", selected="co-citation-network") + +@reactive.effect +@reactive.event(input.historiograph) +def _(): + ui.update_navs("hidden_tabs", selected="historiograph") + +# --- Section 14: Collaboration Network --- +@reactive.effect +@reactive.event(input.go_collaboration_network) +def _(): + ui.update_navs("hidden_tabs", selected="collaboration_network") + +@reactive.effect +@reactive.event(input.go_countries_collaboration_network) +def _(): + ui.update_navs("hidden_tabs", selected="countries_collaboration_network") + +# --- Section 15: Report --- +@reactive.effect +@reactive.event(input.go_report) +def _(): + ui.update_navs("hidden_tabs", selected="report") + +# --- Section 16: Settings --- +@reactive.effect +@reactive.event(input.go_settings) +def _(): + ui.update_navs("hidden_tabs", selected="settings") + +@reactive.effect +@reactive.event(input.go_settings_2) +def _(): + ui.update_navs("hidden_tabs", selected="settings") diff --git a/csv_excel.py b/csv_excel.py new file mode 100644 index 00000000..e6176490 --- /dev/null +++ b/csv_excel.py @@ -0,0 +1,3 @@ +import pandas as pd +df = pd.read_csv("advanced_standardized_data.csv", sep=';') +df.to_excel("advanced_standardized_data.xlsx", index=False) \ No newline at end of file diff --git a/dispatcher.py b/dispatcher.py new file mode 100644 index 00000000..1d1306f9 --- /dev/null +++ b/dispatcher.py @@ -0,0 +1,189 @@ +from mapping import FIELD_MAPPINGS +import ast + +def extract_authors(row): + """Extract list of author names from authorships column.""" + authorships = row.get('authorships') + if isinstance(authorships, str): + try: + authorships = ast.literal_eval(authorships) + except: + return [] + if not isinstance(authorships, list): + return [] + authors = [] + for auth in authorships: + if isinstance(auth, dict): + author_info = auth.get('author') + if isinstance(author_info, dict): + name = author_info.get('display_name') + if name: + authors.append(name) + return authors + +def extract_cited_references(row): + """Extract list of cited reference IDs from referenced_works column.""" + refs = row.get('referenced_works') + if isinstance(refs, str): + try: + refs = ast.literal_eval(refs) + except: + return [] + if not isinstance(refs, list): + return [] + return refs + +def extract_source(row): + """Extract source/journal name from primary_location.""" + primary_location = row.get('primary_location') + if isinstance(primary_location, str): + try: + primary_location = ast.literal_eval(primary_location) + except: + return '' + if not isinstance(primary_location, dict): + return '' + source = primary_location.get('source', {}) + if isinstance(source, dict): + return source.get('display_name', '') + return '' + +def extract_keywords(row): + """Extract list of keyword strings from keywords column.""" + keywords = row.get('keywords') + if isinstance(keywords, str): + try: + keywords = ast.literal_eval(keywords) + except: + return [] + if not isinstance(keywords, list): + return [] + result = [] + for kw in keywords: + if isinstance(kw, dict): + name = kw.get('display_name') + if name: + result.append(name) + return result + +def extract_concepts(row): + """Extract list of concept display names from concepts column.""" + concepts = row.get('concepts') + if isinstance(concepts, str): + try: + concepts = ast.literal_eval(concepts) + except: + return [] + if not isinstance(concepts, list): + return [] + result = [] + for cpt in concepts: + if isinstance(cpt, dict): + name = cpt.get('display_name') + if name: + result.append(name) + return result + +def extract_affiliations(row): + """Extract list of affiliation names from authorships column.""" + authorships = row.get('authorships') + if isinstance(authorships, str): + try: + authorships = ast.literal_eval(authorships) + except: + return [] + if not isinstance(authorships, list): + return [] + affiliations = set() + for auth in authorships: + if not isinstance(auth, dict): + continue + institutions = auth.get('institutions', []) + for inst in institutions: + if isinstance(inst, dict): + name = inst.get('display_name') + if name: + affiliations.add(name) + return list(affiliations) + +def extract_abstract(row): + """Reconstruct abstract from abstract_inverted_index dict.""" + inverted = row.get('abstract_inverted_index') + if isinstance(inverted, str): + try: + inverted = ast.literal_eval(inverted) + except: + return '' + if not isinstance(inverted, dict): + return '' + max_pos = 0 + for positions in inverted.values(): + if positions: + max_pos = max(max_pos, max(positions)) + words = [''] * (max_pos + 1) + for word, positions in inverted.items(): + for pos in positions: + words[pos] = word + return ' '.join(words).strip() + +def dispatch_and_map(df, source_name): + if source_name not in FIELD_MAPPINGS: + raise ValueError(f"Source '{source_name}' not supported. Check mapping.py.") + + mapping = FIELD_MAPPINGS[source_name] + + # Extract authors if needed (AU) + if 'AU' in mapping and mapping['AU'] == 'authorships': + print("Extracting authors...") + df['AU'] = df.apply(extract_authors, axis=1) + + # Extract cited references if needed (CR) + if 'CR' in mapping and mapping['CR'] == 'referenced_works': + print("Extracting cited references...") + df['CR'] = df.apply(extract_cited_references, axis=1) + + # Extract source if needed (SO) + if 'SO' in mapping and mapping['SO'] == 'primary_location.source.display_name': + print("Extracting source...") + df['SO'] = df.apply(extract_source, axis=1) + + # Extract keywords if needed (DE) + if 'DE' in mapping and mapping['DE'] == 'keywords': + print("Extracting keywords...") + df['DE'] = df.apply(extract_keywords, axis=1) + + # Extract concepts if needed (ID) + if 'ID' in mapping and mapping['ID'] == 'concepts': + print("Extracting concepts...") + df['ID'] = df.apply(extract_concepts, axis=1) + + # Extract affiliations if needed (C1) + if 'C1' in mapping and mapping['C1'] == 'authorships': + print("Extracting affiliations...") + df['C1'] = df.apply(extract_affiliations, axis=1) + + # Extract abstract if needed (AB) + if 'AB' in mapping and mapping['AB'] == 'abstract_inverted_index': + print("Extracting abstract...") + df['AB'] = df.apply(extract_abstract, axis=1) + + # Rename columns (skip the ones we already created) + rename_dict = {} + for target, source in mapping.items(): + if target in ['AU', 'CR', 'SO', 'DE', 'ID', 'C1', 'AB']: + continue # already handled + if source in df.columns: + rename_dict[source] = target + + df = df.rename(columns=rename_dict) + + # Keep only the required WoS columns (as per exam Table 4.2) + required_columns = [ + 'UT', 'DI', 'PMID', 'TI', 'SO', 'JI', 'PY', 'DT', 'LA', 'TC', + 'AU', 'AF', 'C1', 'RP', 'CR', 'DE', 'ID', 'AB', 'VL', 'IS', 'BP', 'EP', 'SR' + ] + # Keep only columns that actually exist in the DataFrame + existing_cols = [col for col in required_columns if col in df.columns] + df = df[existing_cols] + + return df \ No newline at end of file diff --git a/main.py b/main.py new file mode 100644 index 00000000..07f3db47 --- /dev/null +++ b/main.py @@ -0,0 +1,32 @@ +from dispatcher import dispatch_and_map +from validator import enforce_types +from api_retriever import fetch_from_openalex + + +def run_advanced_etl(query): + # 1. EXTRACT: Fetch from OpenAlex API + print(f"Starting Advanced ETL for: '{query}'") + raw_df = fetch_from_openalex(query) + + if raw_df.empty: + print("No data found.") + return + + # 2. TRANSFORM: Dispatch & Map + # Note: We specify 'openalex' as the source + df = dispatch_and_map(raw_df, 'openalex') + + # 3. TRANSFORM: Enforce Types + df = enforce_types(df) + + # 4. LOAD: Export + output_path = "advanced_standardized_data.csv" + df.to_csv(output_path, sep=';', index=False) + print(f"Pipeline complete! File saved: {output_path}") + print("First 5 rows:") + print(df.head()) # Preview the normalized result + print("\nAll column names in the final DataFrame:") + print(df.columns.tolist()) + +# To run it, uncomment the line below: +run_advanced_etl("machine learning") \ No newline at end of file diff --git a/mapping.py b/mapping.py new file mode 100644 index 00000000..c956fbeb --- /dev/null +++ b/mapping.py @@ -0,0 +1,18 @@ +FIELD_MAPPINGS = { + "openalex": { + "UT": "id", + "TI": "title", + "PY": "publication_year", + "TC": "cited_by_count", + "DI": "doi", + "AU": "authorships", + "DT": "type", + "LA": "language", + "SO": "primary_location.source.display_name", + "CR": "referenced_works", + "DE": "keywords", + "ID": "concepts", + "C1": "authorships", + "AB": "abstract_inverted_index" + } +} \ No newline at end of file diff --git a/test_all_functions.py b/test_all_functions.py new file mode 100644 index 00000000..adb25119 --- /dev/null +++ b/test_all_functions.py @@ -0,0 +1,46 @@ +import pandas as pd +import sys +import os +import traceback + +# Add the path to the functions folder (adjust to your actual path) +functions_path = r"C:\Users\179518\Desktop\All Files\Data Science\Second semester\Hardware & Software_2nd_semester\bibliometrix-python\functions" +sys.path.append(functions_path) + +# Import functions +from functions.get_annualproduction import get_annual_production +from functions.get_averagecitations import get_average_citations +from functions.get_relevantsources import get_relevant_sources +from functions.get_relevantauthors import get_relevant_authors + +# Load your standardized data +df = pd.read_csv("advanced_standardized_data.csv", sep=';') + +# Wrapper class +class DataWrapper: + def __init__(self, df): + self.data = df + def get(self): + return self.data + +wrapped = DataWrapper(df) + +# Test each function +tests = [ + ("Annual Production", get_annual_production, []), + ("Average Citations", get_average_citations, []), + ("Relevant Sources", get_relevant_sources, [10]), + ("Relevant Authors", get_relevant_authors, [10, "n_docs"]), +] + +print("Column names:", df.columns.tolist()) +print("TC NaN count:", df['TC'].isna().sum() if 'TC' in df.columns else "TC missing") +print("PY NaN count:", df['PY'].isna().sum() if 'PY' in df.columns else "PY missing") + +for name, func, args in tests: + try: + result = func(wrapped, *args) + print(f"✅ {name} passed") + except Exception as e: + print(f"❌ {name} failed: {e}") + print(traceback.format_exc()) \ No newline at end of file diff --git a/test_functions.py b/test_functions.py new file mode 100644 index 00000000..e39fc982 --- /dev/null +++ b/test_functions.py @@ -0,0 +1,47 @@ +import pandas as pd +import sys +import os + +# Add the project root to sys.path to ensure local imports work +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +# Import the corrected function name +from functions.get_annualproduction import get_annual_production + + +# Create a helper class to mimic the custom data object the function expects +class DataWrapper: + def __init__(self, df): + self.data = df + + def get(self): + return self.data + + +# 1. Load your standardized data +file_path = "advanced_standardized_data.csv" + +if not os.path.exists(file_path): + print(f"Error: {file_path} not found. Run main.py first to generate it.") +else: + df = pd.read_csv(file_path, sep=';') + + # 2. Run the function + try: + print("Running analysis...") + + # Wrap the DataFrame to satisfy the .get() requirement + wrapped_df = DataWrapper(df) + + # Capture both the figure and the data returned by the function + fig, pub_data = get_annual_production(wrapped_df) + + print("Function passed!") + print("--- First 5 rows of calculated annual production ---") + print(pub_data.head()) + + except Exception as e: + print(f"Function crashed! Error: {e}") + import traceback + + traceback.print_exc() \ No newline at end of file diff --git a/validator.py b/validator.py new file mode 100644 index 00000000..be81ea6d --- /dev/null +++ b/validator.py @@ -0,0 +1,23 @@ +import pandas as pd + + +def enforce_types(df): + # 1. Ensure TC is integer + if 'TC' in df.columns: + df['TC'] = pd.to_numeric(df['TC'], errors='coerce').fillna(0).astype(int) + + # 2. Ensure PY is integer (publication year) + if 'PY' in df.columns: + df['PY'] = pd.to_numeric(df['PY'], errors='coerce').fillna(0).astype(int) + + # 3. Ensure multi-value fields are lists, preserve existing lists + list_columns = ['AU', 'AF', 'C1', 'CR', 'DE', 'ID'] + for col in list_columns: + if col in df.columns: + df[col] = df[col].apply( + lambda x: x if isinstance(x, list) else (x.split(';') if isinstance(x, str) else [])) + + # 4. Compute SR if needed (your existing SR code here) + # ... (keep your SR calculation code) + + return df \ No newline at end of file