docs/guides/developer_guide.md

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:

git clone https://github.com/Prashant-ambati/llava-implementation.git
cd llava-implementation

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install development dependencies:

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

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

# 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:

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:

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:

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:

python -m build

2. Run the server:

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
• Gradio Documentation
• Hugging Face Docs
• Pytest Documentation

Tools

• Black
• isort
• flake8
• mypy

Community

• GitHub Issues
• Hugging Face Forums
• Stack Overflow