A FastAPI application that exposes multiple “task” endpoints and functions for data processing, file formatting, image manipulation, and more. This project is designed to demonstrate how to dispatch tasks using function-calling logic (similar to OpenAI function calls).

Features

• Operations Tasks (Phase A):

  • Install uv and run data generation script (datagen.py).
  • Format files using Prettier.
  • Count weekday occurrences in date files.
  • Sort JSON contacts by name.
  • Extract log lines, index markdown files, etc.
  • Use GPT-like calls to extract email addresses, credit card numbers, or find similar comments.

• Business Tasks (Phase B):

  • Fetch data from an API.
  • Clone a Git repository and commit a message.
  • Run SQL queries on a local SQLite database.
  • Scrape website HTML, resize images, transcribe audio, convert Markdown to HTML, etc.

Requirements

• Python 3.10 (or compatible 3.x).

• Docker (if running inside a Docker container).

• For local usage, install dependencies:

  pip install -r requirements.txt

• You also need Node.js available if you plan to format files using Prettier directly from this code. The provided Dockerfile automatically installs Node.js.

Running with Docker

1. Build the Docker image:

   docker build -t fastapi-task-runner .

2. Run the container:

   docker run -d -p 8000:80 --name my_task_runner fastapi-task-runner

   • This publishes the container’s port 80 to the host’s port 8000.
   • Access the FastAPI application at: http://localhost:8000

Testing the Endpoints

• Check the application health (basic check; you can also do a GET on /docs in your browser to see FastAPI docs):

  curl http://localhost:8000/docs

• Run a Task:

  The main endpoint is /run. You pass a URL-encoded task query parameter which the code interprets. For example:

  curl -X POST "http://localhost:8000/run?task=install%20uv%20and%20run%20datagen"

  Internally, it uses the GPT-like function calling logic to pick the correct Python function. The tasks are auto-dispatched by the handle_task(...) function in main.py.

Environment Variables

• Place sensitive keys in a file named .env at the project root (or set them in your environment):
  • OPENAI_API_KEY: Required for any LLM-based tasks (extracting emails, credit cards, or embeddings).
  • OPENAI_API_BASE: Base URL for the OpenAI-like API endpoint. Defaults to https://aiproxy.sanand.workers.dev/openai/v1 if not set.

Development & Local Usage

1. Clone this repository or copy main.py, requirements.txt, Dockerfile, and README.md into your own project.
2. Install dependencies:

   pip install --no-cache-dir -r requirements.txt

3. Run locally with Uvicorn:

   uvicorn main:app --host 0.0.0.0 --port 8000

4. Visit http://localhost:8000/docs to see interactive API docs.

Notes

• Some tasks require data files in a local data/ directory (e.g., logs, images, CSVs, Markdown files). Make sure your Docker container or local environment has those files mounted or included.
• The code checks if the path is under /data for safety. This ensures only files within the data/ directory can be read or written.
• Node.js is installed in the Docker image for the Prettier-based tasks. If you do not need Prettier or do not plan to run those tasks, feel free to remove Node.js installation from the Dockerfile.