Gimeltra Improvement Plan

Executive Summary

Gimeltra is a specialized transliteration tool focusing on Semitic scripts. While the core functionality is solid, the project would benefit from modernization, better documentation, improved error handling, and enhanced deployment options. This plan outlines a comprehensive strategy to make Gimeltra more stable, elegant, and easily deployable.

Current State Analysis

Strengths

  • Clean, focused API for transliteration
  • Support for 24 writing systems with bidirectional conversion
  • Well-structured JSON database for mapping rules
  • Command-line interface for easy usage
  • Automatic script detection capability

Weaknesses

  • License inconsistency (README says MIT, setup.py says GPLv2)
  • Minimal error handling and validation
  • No test suite
  • Limited documentation beyond basic usage
  • No type hints for better IDE support
  • Dependencies could be optimized (yaplon seems unnecessary)
  • No CI/CD pipeline
  • No Docker support for easy deployment
  • Version still at 1.0.0 despite 4 years of commits

Detailed Improvement Plan

1. Project Infrastructure & Documentation

1.1 License Resolution

The project has conflicting license information. The README.md states MIT license, but setup.py declares GPLv2. This needs immediate resolution:

  • Decide on the intended license
  • Update all files to reflect the correct license
  • Ensure LICENSE file matches the declaration

1.2 Version Management

  • Implement semantic versioning properly
  • Update version to 1.1.0 for the next release
  • Consider using bump2version or similar tool for version management
  • Add version history in CHANGELOG.md

1.3 Documentation Enhancement

  • Add comprehensive API documentation using Sphinx
  • Create a detailed user guide with more examples
  • Document the transliteration rules and conventions
  • Add docstrings to all functions and classes
  • Create a CONTRIBUTING.md file for potential contributors
  • Add badges to README (build status, coverage, PyPI version)

2. Code Quality & Stability

2.1 Type Hints

Add comprehensive type hints throughout the codebase:

def tr(text: str, sc: Optional[str] = None, to_sc: str = "Latn") -> str:

This will improve IDE support and catch potential type-related bugs early.

2.2 Error Handling

Implement robust error handling:

  • Validate input scripts against supported scripts
  • Handle Unicode encoding/decoding errors gracefully
  • Add custom exceptions for transliteration-specific errors
  • Provide helpful error messages for common mistakes

2.3 Input Validation

  • Validate ISO 15924 script codes
  • Check for empty or null inputs
  • Validate file paths in CLI
  • Add warnings for unsupported character combinations

2.4 Logging Improvements

  • Use structured logging instead of print statements
  • Add configurable log levels
  • Include timing information for performance monitoring
  • Log transliteration statistics (characters processed, scripts detected)

3. Testing Strategy

3.1 Unit Tests

Create comprehensive test suite using pytest:

  • Test each script conversion pair
  • Test edge cases (empty strings, mixed scripts, special characters)
  • Test contextual forms and ligatures
  • Test automatic script detection
  • Test CLI functionality

3.2 Integration Tests

  • Test full transliteration workflows
  • Test file input/output operations
  • Test piping and stdin functionality
  • Test error scenarios

3.3 Performance Tests

  • Benchmark transliteration speed for different scripts
  • Test with large texts
  • Memory usage profiling
  • Identify and optimize bottlenecks

4. Code Architecture Improvements

4.1 Refactoring Suggestions

  • Split Transliterator class into smaller, focused components
  • Create a separate module for script detection
  • Move configuration to a dedicated config module
  • Consider using dataclasses for data structures

4.2 Dependency Optimization

  • Evaluate if yaplon is necessary (only used in update.py)
  • Consider using standard library csv module instead
  • Review if all fonttools features are needed
  • Pin dependency versions for reproducibility

4.3 Data Management

  • Consider loading JSON data lazily to improve startup time
  • Add data validation when loading transliteration mappings
  • Implement caching for frequently used conversions
  • Add ability to extend mappings without modifying core data

5. Deployment & Distribution

5.1 Docker Support

Create Docker configuration for easy deployment:

FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
RUN pip install -e .
ENTRYPOINT ["gimeltrapy"]

5.2 CI/CD Pipeline

Implement GitHub Actions workflow:

  • Run tests on multiple Python versions (3.9, 3.10, 3.11, 3.12)
  • Check code formatting with black/ruff
  • Run type checking with mypy
  • Generate coverage reports
  • Automated PyPI deployment on tags

5.3 Package Distribution

  • Ensure proper wheel distribution
  • Add platform-specific tags if needed
  • Consider conda-forge distribution
  • Create standalone executables using PyInstaller

6. Feature Enhancements

6.1 New Functionality

  • Add batch processing capability for multiple files
  • Implement parallel processing for large texts
  • Add REST API endpoint option
  • Support for custom transliteration rules
  • Add reverse transliteration confidence scoring

6.2 Output Formats

  • Support JSON output with metadata
  • Add HTML output with script information
  • Implement streaming for large files
  • Add progress bars for long operations

6.3 Script Support

  • Review and update script mappings for accuracy
  • Add support for additional related scripts
  • Implement variant handling (e.g., different Arabic styles)
  • Add diacritic preservation option

7. Performance Optimization

7.1 Algorithmic Improvements

  • Use string builders for better performance
  • Implement lookup table optimizations
  • Consider Cython for hot paths
  • Profile and optimize regular expression usage

7.2 Memory Optimization

  • Load data on-demand
  • Implement memory-efficient streaming
  • Use generators for large text processing
  • Optimize data structure memory footprint

8. Community & Maintenance

8.1 Community Building

  • Set up issue templates
  • Create discussion forums
  • Add code of conduct
  • Establish contribution guidelines

8.2 Maintenance Plan

  • Regular dependency updates
  • Security vulnerability scanning
  • Performance regression testing
  • User feedback incorporation

Implementation Priority

Phase 1: Critical Fixes (Week 1)

  1. Resolve license inconsistency
  2. Add basic error handling
  3. Fix any critical bugs
  4. Update version number

Phase 2: Testing & Documentation (Weeks 2-3)

  1. Implement comprehensive test suite
  2. Add type hints
  3. Enhance documentation
  4. Set up CI/CD pipeline

Phase 3: Code Quality (Weeks 4-5)

  1. Refactor code architecture
  2. Optimize dependencies
  3. Improve logging
  4. Add input validation

Phase 4: Deployment (Week 6)

  1. Create Docker support
  2. Set up automated releases
  3. Improve distribution options

Phase 5: Feature Enhancement (Weeks 7-8)

  1. Add new functionality
  2. Performance optimizations
  3. Extended script support

Success Metrics

  • 95%+ test coverage
  • Zero critical bugs
  • <100ms transliteration time for typical texts
  • Successful deployment to PyPI with wheel support
  • Active CI/CD pipeline with all checks passing
  • Comprehensive documentation with examples
  • Docker image available on Docker Hub
  • Clear contribution guidelines and active community

Conclusion

This improvement plan will transform Gimeltra from a functional tool into a professional, well-maintained library suitable for production use. The phased approach ensures that critical issues are addressed first while building a foundation for long-term sustainability and growth.

[Github](https://github.com/twardoch/gimeltra)