123 lines
4.0 KiB
Markdown
123 lines
4.0 KiB
Markdown
# SongSense Backend - COMPLETE ✅
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## All 11 Backend Files Built Successfully
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### Core Infrastructure
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1. ✅ **src/lib/store.ts** - In-memory job store with Map-based CRUD operations
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2. ✅ **src/lib/processing/pipeline.ts** - Main orchestration pipeline for single songs and albums
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### Processing Modules
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3. ✅ **src/lib/processing/download.ts** - Audio download (yt-dlp, curl, local files)
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4. ✅ **src/lib/processing/convert.ts** - Audio conversion to 192kbps MP3 (ffmpeg)
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5. ✅ **src/lib/processing/transcribe.ts** - Lyrics transcription (OpenAI Whisper API)
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6. ✅ **src/lib/processing/analyze.ts** - Audio analysis & spectrogram generation (songsee CLI)
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7. ✅ **src/lib/processing/review.ts** - AI review generation (Claude Sonnet 4 API with vision)
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### API Routes (Next.js 15 App Router)
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8. ✅ **src/app/api/analyze/route.ts** - POST: Start analysis job
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9. ✅ **src/app/api/status/[jobId]/route.ts** - GET: Check job status & progress
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10. ✅ **src/app/api/review/[jobId]/route.ts** - GET: Retrieve completed review
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11. ✅ **src/app/api/upload/route.ts** - POST: Upload audio files
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## Features Implemented
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### Download Support
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- ✅ YouTube URLs (yt-dlp)
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- ✅ SoundCloud URLs (yt-dlp)
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- ✅ Direct file URLs (curl)
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- ✅ Local file paths
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- ✅ File uploads via multipart/form-data
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### Processing Pipeline
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- ✅ Download → Convert → Transcribe → Analyze → Review
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- ✅ Progress tracking (0-100%)
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- ✅ Status updates at each step
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- ✅ Error handling with graceful failures
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- ✅ Album support (sequential track processing)
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### Audio Analysis
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- ✅ Spectrogram generation
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- ✅ Mel spectrogram
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- ✅ Chroma visualization
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- ✅ Loudness analysis
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- ✅ Tempogram
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- ✅ Metadata extraction (duration, sample rate, channels)
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### AI Review Generation
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- ✅ Claude Sonnet 4 API integration
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- ✅ Vision API for spectrogram analysis
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- ✅ Structured JSON output
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- ✅ Production analysis
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- ✅ Lyric analysis
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- ✅ Vibe & emotional impact
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- ✅ 1-10 rating scale
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- ✅ Standout lines extraction
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- ✅ Instrumental track detection
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### API Endpoints
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```
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POST /api/analyze → Start analysis (URL or file upload)
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GET /api/status/:jobId → Get job status & progress
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GET /api/review/:jobId → Get completed review
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POST /api/upload → Upload file (returns path)
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```
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## Environment Variables Required
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```bash
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OPENAI_API_KEY=sk-... # For Whisper transcription
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ANTHROPIC_API_KEY=sk-... # For Claude review generation
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```
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## External Dependencies
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- `ffmpeg` - Audio conversion
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- `yt-dlp` - YouTube/SoundCloud downloads
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- `songsee` - Audio analysis (installed at /opt/homebrew/bin/songsee)
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- `curl` - Direct file downloads
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## Type Safety
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- ✅ All files import from shared `src/lib/types.ts`
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- ✅ TypeScript compilation passes with no errors
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- ✅ Proper type annotations throughout
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## Error Handling
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- ✅ Try/catch blocks in all async functions
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- ✅ Console logging for debugging
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- ✅ Error status updates in job store
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- ✅ Graceful degradation (e.g., instrumental detection on transcription failure)
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## Storage Strategy
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- In-memory Map for job storage (MVP - no database)
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- Temporary files in `/tmp/songsense/{jobId}/`
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- Uploads in `/tmp/songsense/uploads/`
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- Analysis images in `/tmp/songsense/{jobId}/analysis/`
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## Next Steps (for Frontend Builder)
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1. Build UI to submit URLs/files to `/api/analyze`
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2. Poll `/api/status/:jobId` for progress updates
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3. Display review from `/api/review/:jobId` when complete
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4. Show spectrograms and visualizations
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5. Implement SSE for real-time progress (optional enhancement)
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## Testing Commands
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```bash
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# Test TypeScript compilation
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npx tsc --noEmit
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# Start Next.js dev server
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npm run dev
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# Test API endpoints
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curl -X POST http://localhost:3000/api/analyze \
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-H "Content-Type: application/json" \
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-d '{"url": "https://www.youtube.com/watch?v=..."}'
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curl http://localhost:3000/api/status/{jobId}
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curl http://localhost:3000/api/review/{jobId}
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```
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---
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**Backend Status: READY FOR INTEGRATION** 🚀
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All backend files are built, tested, and ready for the frontend builder to create the UI layer.
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