DB-backed incremental redesign: find tracks missing locally

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>

requirements.txt

@@ -7,3 +7,5 @@ rapidfuzz==3.14.5
 requests==2.34.2
 tqdm==4.68.2
 xmltodict==1.0.4
+SQLAlchemy==2.0.50
+musicbrainzngs==0.7.1