# SongSense Backend - COMPLETE ✅

## All 11 Backend Files Built Successfully

### Core Infrastructure
1. ✅ **src/lib/store.ts** - In-memory job store with Map-based CRUD operations
2. ✅ **src/lib/processing/pipeline.ts** - Main orchestration pipeline for single songs and albums

### Processing Modules
3. ✅ **src/lib/processing/download.ts** - Audio download (yt-dlp, curl, local files)
4. ✅ **src/lib/processing/convert.ts** - Audio conversion to 192kbps MP3 (ffmpeg)
5. ✅ **src/lib/processing/transcribe.ts** - Lyrics transcription (OpenAI Whisper API)
6. ✅ **src/lib/processing/analyze.ts** - Audio analysis & spectrogram generation (songsee CLI)
7. ✅ **src/lib/processing/review.ts** - AI review generation (Claude Sonnet 4 API with vision)

### API Routes (Next.js 15 App Router)
8. ✅ **src/app/api/analyze/route.ts** - POST: Start analysis job
9. ✅ **src/app/api/status/[jobId]/route.ts** - GET: Check job status & progress
10. ✅ **src/app/api/review/[jobId]/route.ts** - GET: Retrieve completed review
11. ✅ **src/app/api/upload/route.ts** - POST: Upload audio files

## Features Implemented

### Download Support
- ✅ YouTube URLs (yt-dlp)
- ✅ SoundCloud URLs (yt-dlp)
- ✅ Direct file URLs (curl)
- ✅ Local file paths
- ✅ File uploads via multipart/form-data

### Processing Pipeline
- ✅ Download → Convert → Transcribe → Analyze → Review
- ✅ Progress tracking (0-100%)
- ✅ Status updates at each step
- ✅ Error handling with graceful failures
- ✅ Album support (sequential track processing)

### Audio Analysis
- ✅ Spectrogram generation
- ✅ Mel spectrogram
- ✅ Chroma visualization
- ✅ Loudness analysis
- ✅ Tempogram
- ✅ Metadata extraction (duration, sample rate, channels)

### AI Review Generation
- ✅ Claude Sonnet 4 API integration
- ✅ Vision API for spectrogram analysis
- ✅ Structured JSON output
- ✅ Production analysis
- ✅ Lyric analysis
- ✅ Vibe & emotional impact
- ✅ 1-10 rating scale
- ✅ Standout lines extraction
- ✅ Instrumental track detection

### API Endpoints
```
POST   /api/analyze        → Start analysis (URL or file upload)
GET    /api/status/:jobId  → Get job status & progress
GET    /api/review/:jobId  → Get completed review
POST   /api/upload         → Upload file (returns path)
```

## Environment Variables Required
```bash
OPENAI_API_KEY=sk-...      # For Whisper transcription
ANTHROPIC_API_KEY=sk-...   # For Claude review generation
```

## External Dependencies
- `ffmpeg` - Audio conversion
- `yt-dlp` - YouTube/SoundCloud downloads
- `songsee` - Audio analysis (installed at /opt/homebrew/bin/songsee)
- `curl` - Direct file downloads

## Type Safety
- ✅ All files import from shared `src/lib/types.ts`
- ✅ TypeScript compilation passes with no errors
- ✅ Proper type annotations throughout

## Error Handling
- ✅ Try/catch blocks in all async functions
- ✅ Console logging for debugging
- ✅ Error status updates in job store
- ✅ Graceful degradation (e.g., instrumental detection on transcription failure)

## Storage Strategy
- In-memory Map for job storage (MVP - no database)
- Temporary files in `/tmp/songsense/{jobId}/`
- Uploads in `/tmp/songsense/uploads/`
- Analysis images in `/tmp/songsense/{jobId}/analysis/`

## Next Steps (for Frontend Builder)
1. Build UI to submit URLs/files to `/api/analyze`
2. Poll `/api/status/:jobId` for progress updates
3. Display review from `/api/review/:jobId` when complete
4. Show spectrograms and visualizations
5. Implement SSE for real-time progress (optional enhancement)

## Testing Commands
```bash
# Test TypeScript compilation
npx tsc --noEmit

# Start Next.js dev server
npm run dev

# Test API endpoints
curl -X POST http://localhost:3000/api/analyze \
  -H "Content-Type: application/json" \
  -d '{"url": "https://www.youtube.com/watch?v=..."}'

curl http://localhost:3000/api/status/{jobId}
curl http://localhost:3000/api/review/{jobId}
```

---

**Backend Status: READY FOR INTEGRATION** 🚀

All backend files are built, tested, and ready for the frontend builder to create the UI layer.