bvandeusen
485387ff0b
Heads + CCIP are the tag source and head auto-apply is the earned propagation.
The Camie tagger ran only to feed the allowlist bulk-apply (its ImagePrediction
rows had no other consumer), and the allowlist was a SECOND, un-earned auto-apply
path firing in parallel with heads on every accept — exactly the un-earned spray
the v2 pivot replaced. Retire both.
Behavior change: accepting a suggestion now applies the tag to THAT image only
(source='ml_accepted', a head-training positive) — it no longer allowlists +
fans the tag across the library via Camie. Propagation is heads' earned
auto-apply. (Loses instant cold-start propagation for booru-vocab tags; that was
un-earned and bypassed the precision gate.)
- tag_and_embed is now EMBED-ONLY (no Camie load/infer, no ImagePrediction
writes); backfill enqueues it for images with no embedding.
- Removed: services/ml/tagger.py, apply_allowlist_tags + helpers + daily beat +
every enqueue caller (accept/alias/merge/per-image), api/allowlist.py +
blueprint, ImagePrediction + TagAllowlist models/tables (migration 0067),
AllowlistTable.vue + allowlist store, the accept coverage-projection payload.
- AllowlistService gutted to accept/dismiss/undismiss/reject (the rejection store
the rail still needs); accept returns nothing, API returns {accepted, tag_id}.
- tag merge no longer repoints/triggers the allowlist; _keep_as_alias now keys on
ML-applied image_tag sources (incl. head_auto) instead of the allowlist.
- UI: MLBackfillCard relabelled to embedding-only; accept toast simplified;
MaintenancePanel drops the allowlist tile.
Left for a follow-up hygiene pass (now-inert, harmless): the dead settings
columns (tagger_store_floor, tagger_model_version, suggestion_threshold_*,
video_min_tag_frames), image_record.tagger_model_version, MLThresholdSliders
trim, and the Camie model download in download_models.py.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
41 lines
1.1 KiB
Python
41 lines
1.1 KiB
Python
"""tag_and_embed (embed-only) / backfill task tests. The pure _is_video helper
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is a unit test; the DB-touching backfill query is an integration test with
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monkeypatched dispatch."""
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from pathlib import Path
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import pytest
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from backend.app.tasks.ml import _is_video
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def test_is_video():
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assert _is_video(Path("a.mp4")) is True
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assert _is_video(Path("a.MKV")) is True
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assert _is_video(Path("a.jpg")) is False
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@pytest.mark.integration
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@pytest.mark.asyncio
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async def test_backfill_enqueues_missing(db, monkeypatch):
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from backend.app.models import ImageRecord
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from backend.app.tasks import ml as ml_tasks
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calls = []
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monkeypatch.setattr(
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ml_tasks.tag_and_embed, "delay", lambda image_id: calls.append(image_id)
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)
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img = ImageRecord(
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path="/images/n.jpg", sha256="n" * 64, size_bytes=1,
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mime="image/jpeg", width=1, height=1,
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origin="imported_filesystem", integrity_status="unknown",
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siglip_embedding=None,
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)
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db.add(img)
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await db.commit()
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count = ml_tasks.backfill()
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assert count >= 1
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assert img.id in calls
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