Feat/media analysis pipeline - #34
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Backend: - Add video_events table migration (e8a1b2c3d4f5) with indexes on (user_id, created_at), (video_id, event_type), (video_id, created_at) - Add Pydantic schema with VideoEventType enum validation - Add POST /api/events/video ingestion endpoint (202 Accepted) that batch-pushes events to Redis via RPUSH - Add Celery periodic task flush_video_events (every 60s) that LPOPs up to 500 events and bulk-inserts into Postgres, routing failures to video_events:failed dead-letter list Frontend: - Add useVideoEvents hook with client-side batching (flush every 5s or on unmount), per-session dedup via ref flags, and threshold detection (view/watch_25/50/75/complete/skip/like/share) - Integrate into VideoPlayerV2: view on first play, thresholds in handleTimeUpdate, skip on early unmount - Integrate into FlashCard: view on active, thresholds in handleTimeUpdate, skip/like/share on sidebar actions - Integrate into WatchView: like and share events
- Add media_analysis table migration with pgvector(384) embedding,
JSONB columns for frame_sample_paths, scene_cuts, beat_timestamps
- Create app/services/media_analysis.py with modular pipeline:
ffmpeg frame extraction (1fps), pyscenedetect ContentDetector,
librosa beat tracking, sentence-transformers caption embedding
- Add Celery task analyze_media (max_retries=2, 120s backoff)
that downloads HLS stream, runs pipeline, writes results to Postgres
- Fire-and-forget hook in _finalize_transcode_success triggers
analyze_media.delay() after transcode completes
- Add GET /api/videos/{video_id}/analysis endpoint
- Add requirements: pyscenedetect, librosa, sentence-transformers, pgvector
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