0e64d439c4
Replace the JSON-file pipeline with a SQLite/SQLAlchemy store (Postgres via DATABASE_URL) and an incremental, ID-based matcher whose goal is "which Spotify/iTunes tracks am I missing locally". - src/db: models (Track/MbCache/FileState/IngestState) + engine/session. - src/ingest: upsert prefetched spotify/itunes/local (work_dir/latest-1) by (source, source_id); pulls ISRC from raw Spotify; preserves enrichment. - src/enrich: MusicBrainz recording-MBID resolver. Resolves via NORMALIZED text search (so the same song across sources lands on one MBID + one cached lookup), ISRC as exact fallback. Only touches unresolved tracks, caches positive AND negative results, rate-limited and resumable. - src/match: normalization + cross-source matcher with tiers ISRC > MBID > normalized text; reports present/missing and the match method. - main.py: CLI (ingest / enrich / match). Gui.py: missing-tracks viewer. - README + requirements (SQLAlchemy, musicbrainzngs); ignore *.db. Incremental everywhere: re-running updates only what changed; enrich continues where it stopped. Live Spotify/local backends are left intact. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
11 lines
199 B
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11 lines
199 B
Text
streamlit==1.58.0
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streamlit-aggrid==1.2.1.post2
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pandas==3.0.3
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Pillow==12.2.0
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mutagen==1.47.0
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rapidfuzz==3.14.5
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requests==2.34.2
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tqdm==4.68.2
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xmltodict==1.0.4
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SQLAlchemy==2.0.50
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musicbrainzngs==0.7.1
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