---
title: DevDeCode
emoji: 💡
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 5.32.0
app_file: app.py
pinned: false
---

# 💡 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.

---

## 🚀 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

---

## 🛠️ 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**
    ```bash
    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**
    ```bash
    python app.py
    ```

---

## 🚀 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.

---

## 🗂️ API Endpoint

- **Base URL:** `/explain`
- **Method:** `POST`
- **Input:**
    ```json
    {
      "code": "your_python_code_here"
    }
    ```
- **Output:**
    ```json
    {
      "output": "Step-by-step explanation of the code..."
    }
    ```

---

## 📄 License

MIT License © 2025 [Your Name]

---

## 🙌 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 for deployment!