⚝ AI Code Debugger (AI Code Architect)

AI Code Debugger (branded as AI Code Architect) is an advanced real-time code analysis tool designed to help developers identify syntax errors, logical bugs, and optimization opportunities.

Powered by Groq (Llama-3) for high-speed inference and HuggingFace Embeddings for RAG (Retrieval-Augmented Generation), this tool provides instant, context-aware feedback in a professional chat interface.

---

🚀 Key Features

• ⚡ Ultra-Fast Analysis: Uses Groq's LPU inference engine with Llama-3-70b/Mixtral models.
• 🧠 RAG Integration: Retrieves coding "Best Practices" from a local Vector Database (FAISS) to ground the AI's responses.
• 💬 Modern Chat UI: Features a WhatsApp-style interface with "User-Right / Bot-Left" alignment and typing animations.
• 🐞 Dual-Layer Detection: Identifies both Syntax Errors (missing colons, brackets) and Logic Errors (infinite loops, resource leaks).
• 🌊 Streaming Responses: Simulates a natural conversation with typewriter-style text streaming.

---

🛠️ Tech Stack

• Frontend: Streamlit (Custom CSS for Dark Mode & Layout)
• LLM Engine: Groq API (Llama-3 / Mixtral)
• Orchestration: LangChain
• Embeddings: HuggingFace (all-MiniLM-L6-v2)
• Vector Store: FAISS (CPU Optimized)

---

📂 Project Structure

AI-Code-Debugger/
├── .env                  # API Keys (Create this file manually)
├── .gitignore            # Git ignore rules
├── requirements.txt      # Python dependencies
├── main.py               # Application Entry Point (UI & Stream Logic)
└── src/
    ├── __init__.py
    ├── llm_engine.py     # RAG Logic, Groq Client, & Streaming
    └── utils.py          # Custom CSS, Animations, & Layout Configuration

---

⚙️ Installation & Setup

Follow these steps to set up the project locally.

1. Clone the Repository

git clone [https://github.com/yathik-2622/AI-Code-Debugger.git](https://github.com/yathik-2622/AI-Code-Debugger.git)
cd AI-Code-Debugger

2. Create a Virtual Environment (Optional but Recommended)

# Windows
python -m venv venv
venv\Scripts\activate

# Mac/Linux
python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure API Keys

Create a file named .env in the root directory and add your Groq API Key:

GROQ_API_KEY=gsk_your_actual_api_key_here

(You can get a free key from console.groq.com)

5. Run the Application

streamlit run main.py

---

🧪 Usage Guide

1. Select Language: Choose your programming language (Python, C++, Java, etc.) from the sidebar.
2. Paste Code: Paste your buggy code snippet into the chat input bar at the bottom.
3. Analyze: Press Enter. The AI will:

• Show a "Thinking" animation (Bouncing dots).
• Retrieve relevant coding context.
• Stream a detailed report including Diagnosis, Fix, and Explanation.

---

📸 Screenshots

---

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

---

📄 License

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