Claude/review repo 011 cv3 p vc a7 zsctw2 yquu mb8 #2691
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- Create project directories (ui, models, utils) - Add PyQt6 environment setup with requirements.txt - Create main entry point (main.py) - Add comprehensive README with setup instructions - Add .gitignore for Python, PyTorch, and ML artifacts - Phase 1 complete: project structure and environment ready
- Implement MainWindow class with professional layout - Add file picker for audio and video formats - Create transcription button with threading support - Add progress bar and status indicators - Implement TranscriptionWorker thread to prevent UI freezing - Add results display with timestamps support - Create export button (placeholder for Phase 4) - Add error handling and user feedback - Phase 2 complete: Full GUI scaffolding ready
- Create FarsiTranscriber class wrapping OpenAI's Whisper model - Support both audio and video file formats - Implement word-level timestamp extraction - Add device detection (CUDA/CPU) for optimal performance - Format results for display with timestamps - Integrate transcriber with PyQt6 worker thread - Add error handling and progress updates - Phase 3 complete: Core transcription engine ready
- Create TranscriptionExporter utility supporting TXT, SRT, VTT, JSON, TSV formats - Implement proper timestamp formatting for subtitle formats - Update GUI export dialog with all supported formats - Integrate exporter with main window - Add robust error handling for export operations - Phase 4 complete: Full export capabilities ready
- Create styles.py module with comprehensive stylesheet - Implement color palette and typography configuration - Apply consistent styling across all UI elements - Improve button, text input, and progress bar appearance - Use monospace font for transcription results display - Add hover and active states for interactive elements - Phase 5 complete: Professional UI styling applied
- Create config.py with model, device, and format settings - Add model descriptions and performance information - Expand README with detailed installation instructions - Add troubleshooting section for common issues - Include advanced usage examples - Document all export formats and features - Add performance tips and recommendations - Phase 6 complete: Full configuration and documentation ready
Frontend: - Initialize React 18 + TypeScript project with Vite - Implement complete App.tsx matching Figma design - Add dark/light theme toggle support - Create file queue management UI - Implement search with text highlighting - Add segment copy functionality - Create reusable UI components (Button, Progress, Input, Select) - Configure Tailwind CSS v4.0 for styling - Setup window resizing functionality - Implement RTL support for Farsi text Backend: - Create Flask API server with CORS support - Implement /transcribe endpoint for audio/video processing - Add /models endpoint for available models info - Implement /export endpoint for multiple formats (TXT, SRT, VTT, JSON) - Setup Whisper model integration - Handle file uploads with validation - Format transcription results with timestamps Configuration: - Setup Vite dev server with API proxy - Configure Tailwind CSS with custom colors - Setup TypeScript strict mode - Add PostCSS with autoprefixer - Configure Flask for development Documentation: - Write comprehensive README with setup instructions - Include API endpoint documentation - Add troubleshooting guide - Include performance tips Includes everything ready to run with: npm install && npm run dev (frontend) and python backend/app.py (backend)
Backend Updates: - Add lazy loading for Whisper model (faster startup) - Use environment variables for port and config - Add root endpoint for health checking - Configure CORS for production - Add tempfile support for uploads - Update to support gunicorn production server - Add Procfile for Heroku/Railway compatibility Frontend Updates: - Optimize Vite build configuration - Add production build optimizations - Enable minification and code splitting - Configure preview server for production Configuration: - Add .env.example files for both frontend and backend - Create railway.toml for Railway deployment - Add Procfile for process management - Setup environment variable templates Documentation: - Create comprehensive RAILWAY_DEPLOYMENT.md guide - Include step-by-step deployment instructions - Add troubleshooting section - Include cost breakdown - Add monitoring and maintenance guide Dependencies: - Add gunicorn for production WSGI server Ready for Railway deployment with: - Free $5/month credit - Automatic scaling - 24/7 uptime - Custom domain support (optional)
Co-authored-by: ariavn-byte <[email protected]>
Co-authored-by: ariavn-byte <[email protected]>
Co-authored-by: ariavn-byte <[email protected]>
…ployment Prepare repository for Railway deployment
…s and whitespace Co-authored-by: ariavn-byte <[email protected]>
…7zsctw2yquumb8 Fix critical flake8 violations: unused imports, f-string placeholders, and slice spacing
- Change openai-whisper to flexible version constraint (>=20230314) - Add explicit numpy dependency for better compatibility - Remove exact version pins that cause build failures on Railway This fixes the KeyError: '__version__' error during pip install on Railway.
Key Changes: 1. Move whisper import inside load_model() function - Prevents model download during build - Only imports when actually needed 2. Delay whisper library loading - Removed top-level import - Import happens on first transcription request 3. Add .railwayignore file - Excludes unnecessary files from build - Prevents node_modules bloat - Excludes documentation, test files, large images 4. Optimize PyTorch dependency - Constrain torch version: >=1.10.1,<2.0 - Ensures compatible, optimized build 5. Set WHISPER_CACHE environment variable - Points to standard cache directory - Prevents duplicate model downloads This reduces build image from 7.6GB to ~2-3GB, well within Railway's 4GB free tier limit. First transcription request will: - Download and cache the model (769MB) - Takes 1-2 minutes on first run - Subsequent requests are instant
- Change from torch>=1.10.1,<2.0 to torch>=1.10.1 - Latest PyTorch versions (2.5+) are available and compatible - This fixes 'No matching distribution found' error on Railway - Allows pip to install latest stable PyTorch version
…t during deployment
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