📝 Python Code Analyzer

This project is a Code Docstring Generator that takes Python code as input and generates docstrings for the functions in the code using Large Language Models (LLM). It is built with a Flask backend, utilizes OpenAI's GPT model for natural language generation, and demonstrates skills in backend development, Python programming, frontend integration, and modern LLMs.

🚀 Features

• Backend Development: Uses Flask to build a robust API that handles code uploads and docstring generation.
• Code Parsing: Leverages Python's AST (Abstract Syntax Tree) to extract function definitions and their source code.
• LLM Integration: Utilizes OpenAI GPT models to generate meaningful and descriptive docstrings for Python functions.
• REST API: Offers a POST API endpoint that accepts a Python file, processes the functions, and returns generated docstrings in JSON format.
• Frontend: A simple HTML page to upload Python files, built using Flask's template engine.
• CORS Enabled: Supports cross-origin requests using Flask-CORS, making it easy to integrate with frontend or external applications.

📂 Project Structure

.

├── app.py # Main Flask application

├── templates/

│└── index.html # Frontend HTML page

├── requirements.txt # Dependencies for the project

└── README.md # Project documentation (this file)

📋 Requirements

• Python 3.8+
• Flask: A micro web framework for Python.
• OpenAI Python SDK: For interaction with OpenAI's API.
• Transformers: For managing models and tokenization (if needed for other model usage).
• Flask-CORS: To enable cross-origin requests.

Install the necessary dependencies using:

pip install -r requirements.txt

🛠️ Key Technologies

• Backend: Flask
• Frontend: Basic HTML (with index.html in the templates folder)
• Natural Language Processing (NLP): OpenAI's GPT models for generating docstrings.
• Code Parsing: Python's built-in ast library to analyze and extract function definitions.
• API Development: RESTful API to handle file uploads and return responses in JSON.
• CORS Handling: Flask-CORS for cross-origin resource sharing.
• Model Loading: Using transformers for managing models and tokenizers.

🧑‍💻 Usage

1. Set up OpenAI API Key: Make sure to have an OpenAI API key and set it in your environment variables.

export OPENAI_API_KEY='your-openai-api-key'

2. Run the Flask App:

python app.py

3. Access the Frontend:

Once the Flask app is running, open your browser and navigate to:

http://127.0.0.1:5000

4. API Endpoint: The application exposes a single POST endpoint:

   /generate_doc: This accepts a .py file, processes the functions, and generates a docstring for each function.

   Example request using curl:

curl -X POST -F 'code_file=@your_script.py' http://127.0.0.1:5000/generate_doc

The API will return a JSON object containing the function names and the generated docstrings.