Shrook21/Code-Assistant

🤖 Smart Code Assistant

An intelligent code assistant that can generate, explain, and analyze code using advanced AI models. Built with LangChain, LangGraph, and deployed with Gradio.

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🚀 Features

• —Code Generation: Generate code based on natural language descriptions
• —Code Explanation: Get detailed explanations of existing code
• —Code Analysis: Analyze code for improvements and understanding
• —Interactive Web Interface: User-friendly Gradio interface
• —Intelligent Task Classification: Automatically determines the type of request
• —Vector Database Integration: Uses ChromaDB for efficient code retrieval

🏗️ Architecture

LangGraph Workflow

graph TD
    A[User Input] --> B[Classification Node]
    B --> C{Task Type}
    C -->|Generate Code| D[Code Generation Node]
    C -->|Explain Code| E[Code Explanation Node]  
    C -->|Unclear| F[Fallback Node]
    D --> G[Vector Search Tool]
    E --> H[Final Response]
    F --> H
    G --> H
    H --> I[User Interface]

The assistant uses a conditional graph that:

1. 1.Classifies the user's input (generate, explain, or unclear)
2. 2.Routes to appropriate specialized nodes
3. 3.Retrieves relevant code examples from vector database
4. 4.Generates contextual responses using AI models

📁 Project Structure

Code-Assistant/
├── app.py                    # Main Gradio application
├── requirements.txt          # Python dependencies
├── smart_assistant.png       # Project image
├── agents/
│   ├── nodes.py             # LangGraph nodes implementation
│   └── state.py             # Graph state management
├── chroma_code_db/          # Vector database storage
├── config/
│   └── settings.py          # Configuration settings
├── graph/
│   └── conditional_graph.py # LangGraph workflow definition
├── prompts/
│   └── prompts.py           # AI prompts and templates
├── tools/
│   └── tools.py             # Custom tools and utilities
├── utils/
│   └── code_splitter.py     # Code parsing utilities
└── vectorstore/
    ├── builder.py           # Vector database builder
    └── retriever.py         # Document retrieval logic

🛠️ Installation & Setup

Prerequisites

• —Python 3.8+
• —Ollama (for local AI models)

Local Installation

1. 1.Clone the repository:

   git clone https://github.com/yourusername/Code-Assistant.git
   cd Code-Assistant

1. 1.Install dependencies:

   pip install -r requirements.txt

1. 1.Set up Ollama:

   # Install Ollama (visit ollama.ai for installation instructions)
   # Pull your preferred model
   ollama pull llama2  # or your preferred model

1. 1.Configure settings: Edit config/settings.py to set your preferred model and parameters The vector database will be built automatically on first run

1. 1.Run the application:

   python app.py

1. 1.Open your browser: Navigate to http://localhost:7860 Enter your name to start the assistant

🌐 Online Demo

Try the live demo on Hugging Face Spaces:

🔗 Code Assistant - Live Demo

No installation required - just open the link and start using the assistant!

📖 Usage

Getting Started

1. 1.Enter your name when prompted
2. 2.Ask questions about code in natural language:
3. 3."Generate a function to sort a list in Python"
4. 4."Explain how bubble sort works"
5. 5."Create a REST API endpoint for user login"

Example Interactions

👤 User: "Create a function that finds the maximum number in a list"

🤖 Assistant:

🔍 Task Classification: GENERATE CODE

def find_maximum(numbers):
    """
    Find the maximum number in a list.
    
    Args:
        numbers (list): List of numbers
        
    Returns:
        int/float: Maximum number in the list
    """
    if not numbers:
        raise ValueError("List cannot be empty")
    
    max_num = numbers[0]
    for num in numbers[1:]:
        if num > max_num:
            max_num = num
    
    return max_num

# Example usage:
my_list = [3, 1, 4, 1, 5, 9, 2, 6]
result = find_maximum(my_list)
print(f"Maximum number: {result}")  # Output: Maximum number: 9

🧪 Development

Adding New Features

• —Custom Nodes: Add new processing nodes in agents/nodes.py
• —Tools: Extend functionality in tools/tools.py
• —Prompts: Customize AI behavior in prompts/prompts.py
• —Graph Logic: Modify workflow in graph/conditional_graph.py

Testing Locally

# Run with debug mode
python app.py --debug

# Test individual components
python -m vectorstore.builder  # Build vector database
python -m agents.nodes         # Test individual nodes

🤝 Contributing

1. 1.Fork the repository
2. 2.Create a feature branch (git checkout -b feature/amazing-feature)
3. 3.Commit your changes (git commit -m 'Add amazing feature')
4. 4.Push to the branch (git push origin feature/amazing-feature)
5. 5.Open a Pull Request

📝 License

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

🙏 Acknowledgments

• —LangChain - For the AI framework
• —LangGraph - For workflow orchestration
• —Gradio - For the web interface
• —Ollama - For local AI model serving
• —ChromaDB - For vector database functionality
• —Hugging Face - For model hosting and deployment

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