refactor(ml): retire the Camie tagger + allowlist bulk-apply (#1189)

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

tests/test_tasks_ml.py

@@ -1,15 +1,12 @@
-"""tag_and_embed / backfill task tests. Models aren't in CI, so we test
-the pure helpers (_aggregate_video_predictions, _is_video) as unit tests, and
-the DB-touching backfill query as an integration test with monkeypatched
-inference.
-"""
+"""tag_and_embed (embed-only) / backfill task tests. The pure _is_video helper
+is a unit test; the DB-touching backfill query is an integration test with
+monkeypatched dispatch."""
 
 from pathlib import Path
 
 import pytest
 
-from backend.app.services.ml.tagger import TagPrediction
-from backend.app.tasks.ml import _aggregate_video_predictions, _is_video
+from backend.app.tasks.ml import _is_video
 
 
 def test_is_video():
@@ -18,34 +15,6 @@ def test_is_video():
     assert _is_video(Path("a.jpg")) is False
 
 
-def _pred(name, conf, cat="general"):
-    return {name: TagPrediction(name, cat, conf)}
-
-
-def test_aggregate_video_keeps_corroborated_and_means():
-    # #747: 4 frames; "smile" in 3, "sword" in 1 (noise). min_frames=2.
-    per_frame = [
-        {"smile": TagPrediction("smile", "general", 0.6),
-         "sword": TagPrediction("sword", "general", 0.9)},
-        _pred("smile", 0.8),
-        _pred("smile", 0.7),
-        {},
-    ]
-    out = _aggregate_video_predictions(per_frame, min_frames=2)
-    assert "sword" not in out  # one-frame flicker dropped
-    assert abs(out["smile"]["confidence"] - (0.6 + 0.8 + 0.7) / 3) < 1e-9  # mean, not max
-
-
-def test_aggregate_video_clamps_min_frames_to_sample_count():
-    # Short video: 1 frame but min_frames=3 — clamp so it still tags.
-    out = _aggregate_video_predictions([_pred("solo", 0.8)], min_frames=3)
-    assert out["solo"]["confidence"] == 0.8
-
-
-def test_aggregate_video_empty():
-    assert _aggregate_video_predictions([], min_frames=3) == {}
-
-
 @pytest.mark.integration
 @pytest.mark.asyncio
 async def test_backfill_enqueues_missing(db, monkeypatch):
@@ -69,75 +38,3 @@ async def test_backfill_enqueues_missing(db, monkeypatch):
     count = ml_tasks.backfill()
     assert count >= 1
     assert img.id in calls
-
-
-@pytest.mark.integration
-@pytest.mark.asyncio
-async def test_apply_allowlist_applies_above_threshold(db):
-    from sqlalchemy import select
-
-    from backend.app.models import ImageRecord, TagAllowlist, TagKind
-    from backend.app.models.tag import image_tag
-    from backend.app.services.tag_service import TagService
-    from backend.app.tasks import ml as ml_tasks
-    from tests._prediction_helpers import seed_predictions
-
-    tag = await TagService(db).find_or_create("autohero", TagKind.character)
-    db.add(TagAllowlist(tag_id=tag.id, min_confidence=0.95))
-    img = ImageRecord(
-        path="/images/al.jpg", sha256="al" + "0" * 62, size_bytes=1,
-        mime="image/jpeg", width=1, height=1,
-        origin="imported_filesystem", integrity_status="unknown",
-    )
-    db.add(img)
-    await db.commit()
-    await seed_predictions(
-        db, img.id, {"autohero": {"category": "character", "confidence": 0.97}}
-    )
-    await db.commit()
-
-    n = ml_tasks.apply_allowlist_tags(tag_id=tag.id)
-    assert n >= 1
-    src = (
-        await db.execute(
-            select(image_tag.c.source)
-            .where(image_tag.c.image_record_id == img.id)
-            .where(image_tag.c.tag_id == tag.id)
-        )
-    ).scalar_one()
-    assert src == "ml_auto"
-
-
-@pytest.mark.integration
-@pytest.mark.asyncio
-async def test_apply_allowlist_skips_below_threshold(db):
-    from sqlalchemy import select
-
-    from backend.app.models import ImageRecord, TagAllowlist, TagKind
-    from backend.app.models.tag import image_tag
-    from backend.app.services.tag_service import TagService
-    from backend.app.tasks import ml as ml_tasks
-    from tests._prediction_helpers import seed_predictions
-
-    tag = await TagService(db).find_or_create("lowconf", TagKind.character)
-    db.add(TagAllowlist(tag_id=tag.id, min_confidence=0.95))
-    img = ImageRecord(
-        path="/images/lc.jpg", sha256="lc" + "0" * 62, size_bytes=1,
-        mime="image/jpeg", width=1, height=1,
-        origin="imported_filesystem", integrity_status="unknown",
-    )
-    db.add(img)
-    await db.commit()
-    await seed_predictions(
-        db, img.id, {"lowconf": {"category": "character", "confidence": 0.40}}
-    )
-    await db.commit()
-    ml_tasks.apply_allowlist_tags(tag_id=tag.id)
-    applied = (
-        await db.execute(
-            select(image_tag.c.tag_id)
-            .where(image_tag.c.image_record_id == img.id)
-            .where(image_tag.c.tag_id == tag.id)
-        )
-    ).scalar_one_or_none()
-    assert applied is None