Enhance repo organization and structure#1
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hoangsonww merged 2 commits intomainfrom Nov 13, 2025
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This comprehensive reorganization transforms the project from a single monolithic script into a professional, production-ready package. Major Changes: - Created modular package structure under src/sentiment_analysis/ - Separated concerns into dedicated modules: * config.py - Configuration management * data_loader.py - Data loading and preprocessing * model.py - LSTM model architecture with variants * train.py - Training logic with callbacks * predict.py - Prediction logic with batch support * utils.py - Utility functions and helpers * visualization.py - Comprehensive plotting functions * cli.py - Command-line interface New Features: - CLI tools (sentiment-train, sentiment-predict) - Python API for programmatic access - Comprehensive unit tests with pytest - Multiple usage examples (basic, custom, interactive) - Jupyter notebook tutorial - Model persistence (save/load) - Training callbacks (early stopping, checkpointing, LR reduction) - Rich visualizations (training curves, confusion matrix, ROC) - Configuration management system - Logging throughout Documentation: - Complete README rewrite with detailed usage instructions - CONTRIBUTING.md with development guidelines - Comprehensive docstrings in all modules - Example scripts demonstrating various use cases Infrastructure: - setup.py for pip installation - requirements.txt with all dependencies - pytest.ini for test configuration - Updated .gitignore for project structure The old monolithic script is preserved as examples/legacy_monolithic_script.py for reference.
This commit adds comprehensive production-ready features to transform the project into an enterprise-grade application. Docker & Containerization: - Dockerfile with multi-stage build for optimized images - docker-compose.yml for orchestrating multiple services - .dockerignore for efficient builds - Docker documentation (docs/DOCKER.md) CI/CD & Automation: - GitHub Actions workflows for CI/CD pipeline * ci.yml - Comprehensive testing, linting, security scans * release.yml - Automated package publishing - Pre-commit hooks for code quality (.pre-commit-config.yaml) - Makefile with 40+ commands for common development tasks - Shell scripts for training and API deployment API & Web Services: - FastAPI-based REST API (src/sentiment_analysis/api.py) * Single and batch prediction endpoints * Health check and model info endpoints * Comprehensive request/response validation * Error handling and logging * Swagger/ReDoc documentation - API documentation (docs/API.md) - requirements-api.txt for API dependencies Code Quality & Configuration: - pyproject.toml for modern Python packaging - .flake8 configuration for linting - .editorconfig for consistent coding styles - Type checking with mypy configured - Black and isort configurations Error Handling & Validation: - Custom exception classes (src/sentiment_analysis/exceptions.py) * SentimentAnalysisError (base) * ModelNotFoundError * DataLoadError * InvalidInputError * TrainingError * PredictionError * and more... - Environment variable support (.env.example) Security & Best Practices: - SECURITY.md with security policy and best practices - GitHub issue templates (bug reports, feature requests) - Pull request template - Security scanning in CI/CD - Dependency vulnerability checking Documentation: - CHANGELOG.md for tracking changes - Comprehensive Docker guide - API documentation with examples - Security guidelines Scripts & Utilities: - scripts/train_model.sh - Automated training with logging - scripts/start_api.sh - API server startup script This infrastructure enables: - Containerized deployment - Automated testing and quality checks - RESTful API for production use - Professional development workflow - Security-first approach - Comprehensive monitoring and logging
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