AI Image Classifier 📷

Description 📝

This project is a Streamlit-based web application that allows users to upload an image and have it classified by an AI model. It utilizes the MobileNetV2 architecture, pre-trained on the ImageNet dataset, to predict the top three labels and their confidence scores for the uploaded image.

Table of Contents 📜

• Features
• Tech Stack
• Installation
• Usage
• Project Structure
• Contributing
• License
• Important Links
• Footer

Features 🌟

• Image Upload: Allows users to upload images in JPG, JPEG, and PNG formats.
• AI-Powered Classification: Employs a pre-trained MobileNetV2 model for image recognition.
• Top Predictions: Displays the top 3 predicted labels and their confidence scores.
• User-Friendly Interface: Built with Streamlit for an intuitive and interactive user experience.
• Cached Model Loading: Efficiently loads the AI model using Streamlit's caching mechanism for faster subsequent predictions.

Tech Stack 🛠️

• Language: Python
• Frameworks/Libraries:
  • Streamlit: For building the interactive web application.
  • TensorFlow/Keras: For the machine learning model (MobileNetV2).
  • OpenCV: For image processing tasks.
  • NumPy: For numerical operations.
  • Pillow (PIL): For image manipulation.

Installation 🚀

To set up and run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/leealtair/Image_Classifier.git
   cd Image_Classifier

2. Install dependencies using pip:

   pip install -r requirements.txt

   Alternatively, based on pyproject.toml:

   pip install opencv-python>=4.13.0.92 streamlit>=1.58.0 tensorflow>=2.21.0

3. Run the Streamlit application:

   streamlit run main.py

Usage 🖥️

This application provides a simple web interface for classifying images:

1. Run the application using the command streamlit run main.py.
2. Open your web browser and navigate to the local URL provided by Streamlit (usually http://localhost:8501).
3. Upload an image by clicking the "Choose an image..." button and selecting a file from your local machine (supports JPG, JPEG, PNG).
4. Click the "Classify Image" button to get the AI's predictions.
5. The application will display the uploaded image and then list the top 3 predicted labels with their corresponding confidence percentages.

Real-world Use Case: This tool can be used for educational purposes, quick image content identification, or as a starting point for more complex image analysis applications.

Project Structure 📁

Image_Classifier/
├── README.md
├── pyproject.toml
├── main.py
├── .python-version
└── uv.lock

• README.md: This file, containing project documentation.
• pyproject.toml: Project metadata and dependencies.
• main.py: The main entry point for the Streamlit application, containing the image classification logic.
• .python-version: Specifies the Python version (though content is empty).
• uv.lock: A lock file, likely for dependency management (content is large and unspecified).

Contributing 🤝

Contributions are welcome! If you have suggestions for improvements or new features, please fork the repository and create a pull request. You can also open an issue with the tag "enhancement".

License ⚖️

This project is licensed under the MIT License - see the LICENSE file for details.

Important Links 🔗

• Repository URL: https://github.com/leealtair/Image_Classifier

Footer ✨

This README was generated based on the analysis of the Image_Classifier repository.

• Repository: Image_Classifier
• Author: leealtair
• Contact: Please refer to the repository for contact information.

Feel free to fork this repository, like it, give it a star ⭐, and report any issues!

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