# LLaVA Implementation Developer Guide

## Overview

This guide is intended for developers who want to contribute to or extend the LLaVA implementation. The project is structured as a Python package with a Gradio web interface, using modern best practices and tools.

## Project Structure

```
llava_implementation/
├── src/                    # Source code
│   ├── api/               # API endpoints and FastAPI app
│   │   ├── __init__.py
│   │   └── app.py        # Gradio interface
│   ├── models/            # Model implementations
│   │   ├── __init__.py
│   │   └── llava_model.py # LLaVA model wrapper
│   ├── utils/             # Utility functions
│   │   ├── __init__.py
│   │   └── logging.py     # Logging utilities
│   └── configs/           # Configuration files
│       ├── __init__.py
│       └── settings.py    # Application settings
├── tests/                 # Test suite
│   ├── __init__.py
│   └── test_model.py      # Model tests
├── docs/                  # Documentation
│   ├── api/              # API documentation
│   ├── examples/         # Usage examples
│   └── guides/           # User and developer guides
├── assets/               # Static assets
│   ├── images/          # Example images
│   └── icons/           # UI icons
├── scripts/              # Utility scripts
└── examples/             # Example images for the web interface
```

## Development Setup

### Prerequisites

- Python 3.8+
- Git
- CUDA-capable GPU (recommended)
- Virtual environment tool (venv, conda, etc.)

### Installation

1. Clone the repository:
```bash
git clone https://github.com/Prashant-ambati/llava-implementation.git
cd llava-implementation
```

2. Create and activate a virtual environment:
```bash
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
```

3. Install development dependencies:
```bash
pip install -r requirements.txt
pip install -r requirements-dev.txt  # Development dependencies
```

### Development Tools

1. **Code Formatting**
   - Black for code formatting
   - isort for import sorting
   - flake8 for linting

2. **Testing**
   - pytest for testing
   - pytest-cov for coverage
   - pytest-mock for mocking

3. **Type Checking**
   - mypy for static type checking
   - types-* packages for type hints

## Code Style

### Python Style Guide

1. Follow PEP 8 guidelines
2. Use type hints
3. Write docstrings (Google style)
4. Keep functions focused and small
5. Use meaningful variable names

### Example

```python
from typing import Optional, List
from PIL import Image

def process_image(
    image: Image.Image,
    prompt: str,
    max_tokens: Optional[int] = None
) -> List[str]:
    """
    Process an image with the given prompt.
    
    Args:
        image: Input image as PIL Image
        prompt: Text prompt for the model
        max_tokens: Optional maximum tokens to generate
        
    Returns:
        List of generated responses
        
    Raises:
        ValueError: If image is invalid
        RuntimeError: If model fails to process
    """
    # Implementation
```

## Testing

### Running Tests

```bash
# Run all tests
pytest

# Run with coverage
pytest --cov=src

# Run specific test file
pytest tests/test_model.py

# Run with verbose output
pytest -v
```

### Writing Tests

1. Use pytest fixtures
2. Mock external dependencies
3. Test edge cases
4. Include both unit and integration tests

Example test:
```python
import pytest
from PIL import Image

def test_process_image(model, sample_image):
    """Test image processing functionality."""
    prompt = "What color is this image?"
    response = model.process_image(
        image=sample_image,
        prompt=prompt
    )
    assert isinstance(response, str)
    assert len(response) > 0
```

## Model Development

### Adding New Models

1. Create a new model class in `src/models/`
2. Implement required methods
3. Add tests
4. Update documentation

Example:
```python
class NewModel:
    """New model implementation."""
    
    def __init__(self, config: dict):
        """Initialize the model."""
        self.config = config
        self.model = self._load_model()
    
    def process(self, *args, **kwargs):
        """Process inputs and generate output."""
        pass
```

### Model Configuration

1. Add configuration in `src/configs/settings.py`
2. Use environment variables for secrets
3. Document all parameters

## API Development

### Adding New Endpoints

1. Create new endpoint in `src/api/app.py`
2. Add input validation
3. Implement error handling
4. Add tests
5. Update documentation

### Error Handling

1. Use custom exceptions
2. Implement proper logging
3. Return appropriate status codes
4. Include error messages

Example:
```python
class ModelError(Exception):
    """Base exception for model errors."""
    pass

def process_request(request):
    try:
        result = model.process(request)
        return result
    except ModelError as e:
        logger.error(f"Model error: {e}")
        raise HTTPException(status_code=500, detail=str(e))
```

## Deployment

### Local Deployment

1. Build the package:
```bash
python -m build
```

2. Run the server:
```bash
python src/api/app.py
```

### Hugging Face Spaces

1. Update `README.md` with Space metadata
2. Ensure all dependencies are in `requirements.txt`
3. Test the Space locally
4. Push changes to the Space

### Production Deployment

1. Set up proper logging
2. Configure security measures
3. Implement rate limiting
4. Set up monitoring
5. Use environment variables

## Contributing

### Workflow

1. Fork the repository
2. Create a feature branch
3. Make changes
4. Run tests
5. Update documentation
6. Create a pull request

### Pull Request Process

1. Update documentation
2. Add tests
3. Ensure CI passes
4. Get code review
5. Address feedback
6. Merge when approved

## Performance Optimization

### Model Optimization

1. Use model quantization
2. Implement caching
3. Batch processing
4. GPU optimization

### API Optimization

1. Response compression
2. Request validation
3. Connection pooling
4. Caching strategies

## Security

### Best Practices

1. Input validation
2. Error handling
3. Rate limiting
4. Secure configuration
5. Regular updates

### Security Checklist

- [ ] Validate all inputs
- [ ] Sanitize outputs
- [ ] Use secure dependencies
- [ ] Implement rate limiting
- [ ] Set up monitoring
- [ ] Regular security audits

## Monitoring and Logging

### Logging

1. Use structured logging
2. Include context
3. Set appropriate levels
4. Rotate logs

### Monitoring

1. Track key metrics
2. Set up alerts
3. Monitor resources
4. Track errors

## Future Development

### Planned Features

1. Video support
2. Batch processing
3. Model fine-tuning
4. API authentication
5. Advanced caching

### Contributing Ideas

1. Open issues
2. Discuss in PRs
3. Join discussions
4. Share use cases

## Resources

### Documentation

- [Python Documentation](https://docs.python.org/)
- [Gradio Documentation](https://gradio.app/docs/)
- [Hugging Face Docs](https://huggingface.co/docs)
- [Pytest Documentation](https://docs.pytest.org/)

### Tools

- [Black](https://black.readthedocs.io/)
- [isort](https://pycqa.github.io/isort/)
- [flake8](https://flake8.pycqa.org/)
- [mypy](https://mypy.readthedocs.io/)

### Community

- [GitHub Issues](https://github.com/Prashant-ambati/llava-implementation/issues)
- [Hugging Face Forums](https://discuss.huggingface.co/)
- [Stack Overflow](https://stackoverflow.com/)