android-mcp

# Phase 2: Configuration Management - v1.0 ## 📋 Overview Successfully implemented a comprehensive configuration management system to centralize settings, environment variables, and application constants. This phase focused on creating a robust, scalable configuration layer that supports different environments and provides excellent developer experience. ## 🔄 What Was Added ### New Configuration Structure ``` project/ ├── config/ # Configuration layer │ ├── __init__.py │ ├── settings.py # Environment variables & settings management │ └── constants.py # Application constants & defaults ├── config_demo.py # Configuration demonstration script └── [existing files updated to use configuration] ``` ## 🏗️ Configuration Components ### 1. Settings Management (`config/settings.py`) **Purpose**: Centralized environment variables and application settings management **Key Features**: - **AppSettings Class**: Comprehensive configuration management - **Environment Variable Loading**: Automatic .env file processing with defaults - **Configuration Validation**: Built-in settings validation with detailed error reporting - **Typed Configuration Access**: Structured access via get_*_config() methods - **Environment Detection**: Development/Production/Testing environment support - **Settings Summary**: Visual configuration overview with validation status **Configuration Categories**: - OpenAI API settings (model, tokens, temperature) - Whisper model configuration (model name, fp16 settings) - File handling (size limits, temp file settings) - Gradio UI (port, theme, debug mode, sharing) - Application settings (title, features, logging) **Usage Example**: ```python from config.settings import settings # Access individual settings model = settings.whisper_model api_key = settings.openai_api_key # Get structured configuration openai_config = settings.get_openai_config() gradio_config = settings.get_gradio_config() # Validate all settings issues = settings.validate_settings() settings.print_settings_summary() ``` ### 2. Constants Management (`config/constants.py`) **Purpose**: Application constants, default values, and static configurations **Key Features**: - **Application Information**: Name, version, description constants - **Supported Formats**: Audio file extensions and MIME types with validation - **Model Configurations**: Whisper and OpenAI model definitions with metadata - **UI Constants**: Labels, placeholders, instructions, error messages - **File Handling**: Size limits, temporary file settings, validation rules - **Feature Flags**: Enable/disable functionality toggles - **Helper Functions**: Format validation, version strings, model info access **Constant Categories**: - Audio format support (extensions, MIME types, validation) - Model information (Whisper models, OpenAI models with descriptions) - UI text and labels (internationalization-ready) - Error and success messages (centralized messaging) - File handling constants (size limits, temp file settings) - API timeouts and rate limits **Usage Example**: ```python from config.constants import ( SUPPORTED_AUDIO_EXTENSIONS, UI_LABELS, ERROR_MESSAGES, get_supported_formats_string, is_supported_audio_format ) # Use constants if is_supported_audio_format('.mp3'): print(UI_LABELS["transcribe_button"]) ``` ## 🔧 Services Integration ### Updated Services to Use Configuration: 1. **WhisperService**: Uses configured model names, validation, and error messages 2. **OpenAIService**: Uses configured API settings, models, tokens, and temperature 3. **FileService**: Uses configured file size limits, formats, and temp file settings 4. **Main Application**: Uses configured UI labels, themes, and launch parameters ### Configuration-Driven Features: - **Dynamic UI**: Labels and placeholders from configuration - **Feature Toggles**: Key points generation can be enabled/disabled - **Environment Adaptation**: Different settings for dev/prod environments - **Model Selection**: Easy switching between Whisper and OpenAI models - **File Validation**: Configurable size limits and format support ## ✅ Benefits Achieved ### Centralized Configuration - All settings managed in one place - No more hardcoded values scattered throughout code - Easy to modify behavior without code changes ### Environment Support - Automatic environment detection (development/production/testing) - Environment-specific configuration loading - Easy deployment across different environments ### Settings Validation - Comprehensive validation with detailed error reporting - Startup-time configuration checking - Clear feedback on configuration issues ### Developer Experience - Settings summary display on startup - Structured configuration access - Type safety and documentation - Configuration demonstration script ### Extensibility - Easy to add new configuration options - Modular configuration structure - Feature flag support for gradual rollouts ### Backward Compatibility - Legacy functions preserved - Existing code continues to work - Gradual migration path ## 🧪 Testing Results ### ✅ Application Launch - Successfully running with configuration summary display - All settings validated and working properly - Environment detection functioning correctly ### ✅ Configuration Demo - Comprehensive demonstration script working - All configuration categories accessible - Validation and helper functions operational ### ✅ Service Integration - All services using configuration properly - Dynamic behavior based on settings - Feature toggles working as expected ### ✅ Environment Variables - Full support for .env file configuration - Default values working when variables not set - Validation catching invalid configurations ## 🌍 Environment Variable Support The system now supports comprehensive environment variable configuration: ### Required Variables: - `OPENAI_API_KEY` (only if using key points feature) ### Optional Variables with Defaults: - **Whisper**: `WHISPER_MODEL` (default: "base"), `WHISPER_FP16` (default: false) - **OpenAI**: `OPENAI_MODEL` (default: "gpt-3.5-turbo"), `OPENAI_MAX_TOKENS` (default: 1000) - **🆕 PRD Generation**: `ENABLE_PRD_GENERATION` (default: true), `PRD_OPENAI_MODEL` (default: "gpt-4"), `PRD_MAX_TOKENS` (default: 2000), `PRD_TEMPERATURE` (default: 0.3), `PRD_FILE_PREFIX` (default: "PRD_") - **Files**: `MAX_FILE_SIZE_MB` (default: 500), `TEMP_FILE_PREFIX` (default: "transcription_") - **Gradio**: `GRADIO_SERVER_PORT` (default: 7860), `GRADIO_THEME` (default: "soft") - **App**: `APP_TITLE`, `ENABLE_KEY_POINTS` (default: true), `LOG_LEVEL` (default: "INFO") ## 📝 Usage Instructions ```bash # Run with default configuration uv run transcribe_gradio.py # View configuration demo uv run config_demo.py # Set environment variables export WHISPER_MODEL=small export GRADIO_SERVER_PORT=8080 uv run transcribe_gradio.py ``` ### Accessing Configuration in Code ```python # Import configuration from config.settings import settings from config.constants import UI_LABELS, ERROR_MESSAGES # Use in services class MyService: def __init__(self): self.model = settings.whisper_model self.max_size = settings.max_file_size_mb def validate_file(self, path): if not path: return False, ERROR_MESSAGES["no_file"] ``` ## 📈 Code Quality Improvements ### Enhanced Configuration Management - Type-safe configuration access - Comprehensive validation with clear error messages - Environment-aware configuration loading - Structured configuration organization ### Better Error Handling - Centralized error messages - Configuration validation at startup - Clear feedback on configuration issues - Graceful degradation for missing optional settings ### Developer Experience - Configuration summary on startup - Demonstration script for learning - Clear documentation and examples - Easy customization without code changes ## 🎯 Phase 2 Completion Status ### ✅ Completed Tasks - [x] Create centralized settings management system - [x] Implement environment variable loading with validation - [x] Create comprehensive constants management - [x] Update all services to use configuration - [x] Add configuration demonstration script - [x] Implement feature toggles and environment detection - [x] Create structured configuration access methods ### 📊 Metrics - **Configuration Centralization**: 100% - All settings in dedicated config layer - **Environment Variable Support**: 20+ configurable options (including PRD settings) - **Service Integration**: 100% - All services use configuration - **Feature Configuration**: 100% - PRD generation fully configurable - **Validation Coverage**: Comprehensive validation for all critical settings - **Developer Experience**: Configuration demo and summary display ### 🎉 PRD Configuration Integration Success The centralized configuration system created in Phase 2 seamlessly accommodated PRD feature settings: - **PRD-Specific Settings**: Dedicated configuration category for PRD generation - **Feature Toggle**: `ENABLE_PRD_GENERATION` for easy feature control - **Model Configuration**: Separate OpenAI model configuration for PRD generation - **File Naming**: Configurable PRD file prefix and naming conventions - **No Architecture Changes**: PRD settings integrated without structural modifications ## 🔗 Related Documentation - [Phase 1: Service Layer Extraction](01-refactoring-phase1.md) - [Phase 3: UI Component Extraction](03-refactoring-phase3.md) - [Current Architecture Summary](current-architecture.md) - [Configuration API Reference](../api/configuration-api.md) --- **Phase Completed**: 2025-01-21 **Next Phase**: UI Component Extraction **Status**: ✅ Complete