💡 DevDeCode — Code Explanation API using Phi-3 & LangChain

DevDeCode is a FastAPI-powered backend that integrates Microsoft's Phi-3 Mini model using Hugging Face Transformers and LangChain. It takes Python code as input and returns a step-by-step explanation. Designed for developers and learners, this API simplifies code understanding using LLMs.

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🚀 Features

• 🧠 Powered by Phi-3 Mini (4K Instruct)
• 🔗 Built with LangChain for structured LLM workflows
• 🌐 Hosted using FastAPI with auto-generated Swagger docs
• 🌍 CORS-enabled for easy frontend integration
• 🧪 Uses StrOutputParser for clean output formatting
• 🌩️ (Optional) Ngrok integration for public URL testing

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🛠️ Tech Stack

Technology	Description
FastAPI	Web framework for building the RESTful API
LangChain	Manages prompt templates, model pipeline, and parsing logic
Transformers	Hugging Face library for using and fine-tuning pretrained models
Phi-3 Mini	Lightweight instruction-tuned language model from Microsoft
Hugging Face Hub	Model access, authentication, and (optional) deployment to Spaces
Uvicorn	ASGI server to run the FastAPI app
PyTorch	Deep learning backend for model execution
Ngrok (optional)	Tunnels localhost for public access during development
CORS Middleware	Enables smooth frontend-to-backend communication

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📦 Setup

1. Install dependencies

   pip install -r requirements.txt

   Make sure your system supports CUDA or fallback to CPU by modifying torch_dtype and device_map in your code.

2. Run Locally

   python app.py

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🚀 Deployment

Deploy to Hugging Face Spaces

Ensure your repo includes:

• README.md
• requirements.txt
• app.py
• huggingface.yml (optional but useful)

You can use the huggingface_hub Python SDK or upload via the UI.

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🗂️ API Endpoint

• Base URL: /explain
• Method: POST
• Input:

  {
    "code": "your_python_code_here"
  }

• Output:

  {
    "output": "Step-by-step explanation of the code..."
  }

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🧪 API Testing

You can test the API using the built-in Swagger UI:

• Swagger UI (API Docs)
  (If running locally, replace with your public URL if using ngrok or Spaces)

Or use tools like Postman to send POST requests to the /explain endpoint.

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📄 License

MIT License © 2025 [Your Name]

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🙌 Acknowledgements

• Microsoft for Phi-3
• Hugging Face for their incredible ecosystem
• LangChain for making LLM orchestration simple

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Let me know if you want me to generate the requirements.txt or a huggingface.yml file