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>
- fetch() uses a retrying session (connection resets, 5xx, 429 Retry-After)
with backoff, so a transient drop mid-fetch no longer aborts the run.
- tqdm progress bars on all paginated fetches (driven by paging `total`).
- Flow.fetch_spotify(): drop stray `self` from the @staticmethod.
- gui.sh runs the venv's streamlit from the script dir; add requirements.txt.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
