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Contributing to TensorFlow Lite Plus

Thank you for your interest in contributing to TensorFlow Lite Plus! This document outlines the guidelines for contributing to this project.

How to Contribute

Reporting Issues

  1. Search existing issues first to avoid duplicates
  2. Use the issue template when creating new issues
  3. Provide detailed information including:
    • Flutter version
    • Platform (Android/iOS)
    • Device information
    • Steps to reproduce
    • Expected vs actual behavior
    • Code samples or screenshots

Suggesting Features

  1. Check the roadmap to see if the feature is already planned
  2. Create a feature request with:
    • Clear description of the feature
    • Use case and benefits
    • Proposed API design (if applicable)
    • Implementation considerations

Code Contributions

Development Setup

  1. Fork the repository

    git clone https://github.com/yourusername/tflite_plus.git
cd tflite_plus

  2. Install dependencies

    flutter pub get
cd example && flutter pub get

  3. Run tests

    flutter test

Making Changes

  1. Create a feature branch

    git checkout -b feature/your-feature-name

  2. Write clean code following our coding standards:

    • Use proper Dart formatting (dart format)
    • Add comprehensive documentation
    • Follow existing code patterns
    • Add tests for new functionality

  3. Test your changes

    # Run all tests
flutter test

# Test on both platforms
cd example
flutter run # Android
flutter run -d ios # iOS (macOS only)

  4. Update documentation

    • Update README.md if needed
    • Add/update API documentation
    • Update CHANGELOG.md

Code Style Guidelines

Commit Messages

Use conventional commit format:

type(scope): description

[optional body]

[optional footer]

Types:

  • feat: New feature
  • fix: Bug fix
  • docs: Documentation changes
  • style: Code style changes
  • refactor: Code refactoring
  • test: Adding or updating tests
  • chore: Maintenance tasks

Examples:

  • feat(android): add GPU delegate support
  • fix(ios): resolve memory leak in model loading
  • docs(readme): add pose estimation examples

Pull Request Process

  1. Ensure all tests pass

  2. Update documentation as needed

  3. Create a pull request with:

    • Clear title and description
    • Reference related issues
    • List of changes made
    • Screenshots/GIFs for UI changes

  4. Respond to feedback promptly

  5. Maintain a clean commit history

Development Guidelines

Architecture

The plugin follows a layered architecture:

┌─────────────────────┐
│    Dart API         │  ← Public API
├─────────────────────┤
│  Platform Interface │  ← Abstract interface
├─────────────────────┤
│   Method Channel    │  ← Implementation
├─────────────────────┤
│  Native Platforms   │  ← Android/iOS
└─────────────────────┘

Adding New Features

  1. Update the platform interface

    abstract class TflitePlusPlatform {
  Future<ReturnType?> newFeature(Parameters params);
}

  2. Implement in method channel

    class MethodChannelTflitePlus {
  Future<ReturnType?> newFeature(Parameters params) async {
    return await methodChannel.invokeMethod('newFeature', params.toMap());
  }
}

  3. Add to main API

    class TflitePlus {
  static Future<ReturnType?> newFeature(Parameters params) {
    return TflitePlusPlatform.instance.newFeature(params);
  }
}

  4. Implement native code

    • Android: Update TflitePlusPlugin.kt
    • iOS: Update TflitePlusPlugin.swift

  5. Add comprehensive tests

  6. Update documentation

Testing Guidelines

Unit Tests

  • Test all public APIs
  • Mock platform-specific code
  • Test error handling
  • Aim for >90% code coverage

Integration Tests

  • Test on real devices
  • Test with actual models
  • Test hardware acceleration
  • Test memory management

Example Tests

test('loadModel should return success message', () async {
  final result = await TflitePlus.loadModel(
    model: 'test_model.tflite',
  );
  expect(result, contains('success'));
});

Documentation Standards

  • API Documentation: Use proper dartdoc comments
  • README Updates: Keep examples current
  • Code Comments: Explain complex logic
  • Changelog: Document all changes

Community Guidelines

Code of Conduct

This project follows a Code of Conduct. By participating, you agree to:

  • Be respectful and inclusive
  • Welcome newcomers
  • Focus on constructive feedback
  • Maintain professional communication
  • Respect different viewpoints

Getting Help

  • Documentation: Check README and API docs first
  • Issues: Search existing issues
  • Discussions: Use GitHub Discussions for questions
  • Email: Contact support@codebumble.net for private matters

Recognition

Contributors will be:

  • Listed in the Contributors section
  • Mentioned in release notes for significant contributions
  • Invited to join the maintainer team for exceptional contributions

License

By contributing to this project, you agree that your contributions will be licensed under the MIT License.

Thank you for helping make TensorFlow Lite Plus better for everyone! 🚀