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

alembic/versions/0067_retire_camie_allowlist.py

@@ -0,0 +1,66 @@
+"""retire the Camie tagger + allowlist bulk-apply (#1189)
+
+The v2 pivot made heads + CCIP the tag source and head auto-apply the earned
+propagation. The Camie tagger ran only to feed the allowlist bulk-apply (its
+predictions had no other consumer), and the allowlist was a second, un-earned
+auto-apply path parallel to heads. Both are retired — drop their storage.
+
+(image_prediction = Camie's per-image predictions; tag_allowlist = the bulk-
+apply allowlist. Nothing references INTO these tables, so the drop is clean.)
+
+Revision ID: 0067
+Revises: 0066
+Create Date: 2026-06-30
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0067"
+down_revision: Union[str, None] = "0066"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.drop_table("image_prediction")
+    op.drop_table("tag_allowlist")
+
+
+def downgrade() -> None:
+    op.create_table(
+        "tag_allowlist",
+        sa.Column("tag_id", sa.Integer(), nullable=False),
+        sa.Column(
+            "min_confidence", sa.Float(), nullable=False, server_default="0.9"
+        ),
+        sa.Column(
+            "created_at", sa.DateTime(timezone=True),
+            server_default=sa.func.now(), nullable=False,
+        ),
+        sa.ForeignKeyConstraint(["tag_id"], ["tag.id"], ondelete="CASCADE"),
+        sa.PrimaryKeyConstraint("tag_id"),
+        sa.CheckConstraint(
+            "min_confidence >= 0 AND min_confidence <= 1",
+            name="ck_tag_allowlist_confidence_range",
+        ),
+    )
+    op.create_table(
+        "image_prediction",
+        sa.Column("id", sa.Integer(), primary_key=True),
+        sa.Column("image_record_id", sa.Integer(), nullable=False),
+        sa.Column("raw_name", sa.String(length=255), nullable=False),
+        sa.Column("category", sa.String(length=32), nullable=False),
+        sa.Column("score", sa.Float(), nullable=False),
+        sa.ForeignKeyConstraint(
+            ["image_record_id"], ["image_record.id"], ondelete="CASCADE"
+        ),
+    )
+    op.create_index(
+        "ix_image_prediction_image", "image_prediction", ["image_record_id"]
+    )
+    op.create_index(
+        "ix_image_prediction_name_score", "image_prediction",
+        ["raw_name", "score"],
+    )

backend/app/api/__init__.py

@@ -16,7 +16,6 @@ api_bp.add_url_rule("/health", view_func=health.get_health, methods=["GET"])
 def all_blueprints() -> list[Blueprint]:
     from .admin import admin_bp
     from .aliases import aliases_bp
-    from .allowlist import allowlist_bp
     from .artist import artist_bp
     from .artists import artists_bp
     from .attachments import attachments_bp
@@ -58,7 +57,6 @@ def all_blueprints() -> list[Blueprint]:
         cleanup_bp,
         import_admin_bp,
         suggestions_bp,
-        allowlist_bp,
         aliases_bp,
         tag_eval_bp,
         heads_bp,

backend/app/api/allowlist.py

@@ -1,84 +0,0 @@
-"""Allowlist API: list, adjust threshold, remove."""
-
-from quart import Blueprint, jsonify, request
-
-from ..extensions import get_session
-from ..models import TagAllowlist
-from ..services.ml.allowlist import AllowlistService
-
-allowlist_bp = Blueprint("allowlist", __name__, url_prefix="/api")
-
-
-@allowlist_bp.route("/allowlist", methods=["GET"])
-async def list_allowlist():
-    async with get_session() as session:
-        rows = await AllowlistService(session).list_all()
-    return jsonify(
-        [
-            {
-                "tag_id": r.tag_id,
-                "tag_name": r.tag_name,
-                "tag_kind": r.tag_kind,
-                "min_confidence": r.min_confidence,
-                "applied_count": r.applied_count,
-                "coverage_count": r.coverage_count,
-            }
-            for r in rows
-        ]
-    )
-
-
-@allowlist_bp.route("/tags/<int:tag_id>/allowlist/coverage", methods=["GET"])
-async def coverage(tag_id: int):
-    """Live "at threshold T, a sweep would cover ~N images" projection for the
-    allowlist tuning dashboard. Defaults to the tag's stored threshold."""
-    raw = request.args.get("threshold")
-    async with get_session() as session:
-        svc = AllowlistService(session)
-        if raw is not None:
-            try:
-                threshold = float(raw)
-            except ValueError:
-                return jsonify({"error": "threshold must be a float"}), 400
-            if not (0 < threshold <= 1):
-                return jsonify({"error": "threshold must be in (0, 1]"}), 400
-        else:
-            row = await session.get(TagAllowlist, tag_id)
-            if row is None:
-                return jsonify({"error": "not on allowlist"}), 404
-            threshold = row.min_confidence
-        count = await svc.coverage(tag_id, threshold)
-    return jsonify({"count": count, "threshold": threshold})
-
-
-@allowlist_bp.route("/tags/<int:tag_id>/allowlist", methods=["GET"])
-async def get_one(tag_id: int):
-    async with get_session() as session:
-        row = await session.get(TagAllowlist, tag_id)
-    if row is None:
-        return jsonify({"error": "not on allowlist"}), 404
-    return jsonify(
-        {"min_confidence": row.min_confidence, "added_at": row.added_at.isoformat()}
-    )
-
-
-@allowlist_bp.route("/tags/<int:tag_id>/allowlist", methods=["PATCH"])
-async def patch_threshold(tag_id: int):
-    body = await request.get_json()
-    if not body or "min_confidence" not in body:
-        return jsonify({"error": "min_confidence required"}), 400
-    mc = float(body["min_confidence"])
-    if not (0 < mc <= 1):
-        return jsonify({"error": "min_confidence must be in (0, 1]"}), 400
-    async with get_session() as session:
-        await AllowlistService(session).update_threshold(tag_id, mc)
-        await session.commit()
-    return "", 204
-
-
-@allowlist_bp.route("/tags/<int:tag_id>/allowlist", methods=["DELETE"])
-async def remove(tag_id: int):
-    async with get_session() as session:
-        await AllowlistService(session).remove(tag_id)
-        await session.commit()
-    return "", 204

backend/app/api/suggestions.py

@@ -3,31 +3,12 @@
 from quart import Blueprint, jsonify, request
 
 from ..extensions import get_session
-from ..models import Tag, TagAllowlist
 from ..services.ml.allowlist import AllowlistService
 from ..services.ml.suggestions import SuggestionService
 
 suggestions_bp = Blueprint("suggestions", __name__, url_prefix="/api")
 
 
-async def _accept_payload(session, svc, newly_added: bool, tag_id: int) -> dict:
-    """Shape the accept/alias response. When accepting newly allowlists a tag,
-    include the coverage PROJECTION (at the tag's threshold) so the UI can show
-    a non-blocking "auto-applying to ~N images" toast — the actual apply runs
-    async via apply_allowlist_tags, so this is an estimate, not a post-hoc
-    count (#7)."""
-    payload = {"allowlisted": newly_added}
-    if newly_added:
-        tag = await session.get(Tag, tag_id)
-        row = await session.get(TagAllowlist, tag_id)
-        payload["tag_id"] = tag_id
-        payload["tag_name"] = tag.name if tag is not None else None
-        payload["projected_count"] = await svc.coverage(
-            tag_id, row.min_confidence if row is not None else 0.90,
-        )
-    return payload
-
-
 @suggestions_bp.route("/images/<int:image_id>/suggestions", methods=["GET"])
 async def get_suggestions(image_id: int):
     # ?min=<float> overrides the configured per-category thresholds so the typed
@@ -83,15 +64,9 @@ async def accept_suggestion(image_id: int):
         return jsonify({"error": "tag_id required"}), 400
     tag_id = body["tag_id"]
     async with get_session() as session:
-        svc = AllowlistService(session)
+        await AllowlistService(session).accept(image_id, tag_id)
-        newly_added = await svc.accept(image_id, tag_id)
-        payload = await _accept_payload(session, svc, newly_added, tag_id)
         await session.commit()
-    if newly_added:
+    return jsonify({"accepted": True, "tag_id": tag_id})
-        from ..tasks.ml import apply_allowlist_tags
-
-        apply_allowlist_tags.delay(tag_id=tag_id)
-    return jsonify(payload)
 
 
 @suggestions_bp.route(
@@ -104,22 +79,14 @@ async def alias_suggestion(image_id: int):
         return jsonify({"error": f"required: {sorted(required)}"}), 400
     canonical_tag_id = body["canonical_tag_id"]
     async with get_session() as session:
-        svc = AllowlistService(session)
+        await AllowlistService(session).add_alias_and_accept(
-        newly_added = await svc.add_alias_and_accept(
             image_id,
             body["alias_string"],
             body["alias_category"],
             canonical_tag_id,
         )
-        payload = await _accept_payload(
-            session, svc, newly_added, canonical_tag_id,
-        )
         await session.commit()
-    if newly_added:
+    return jsonify({"accepted": True, "tag_id": canonical_tag_id})
-        from ..tasks.ml import apply_allowlist_tags
-
-        apply_allowlist_tags.delay(tag_id=canonical_tag_id)
-    return jsonify(payload)
 
 
 @suggestions_bp.route(

backend/app/api/tags.py

@@ -1,13 +1,12 @@
 """Tags API: autocomplete, create, list/add/remove for an image."""
 
 from quart import Blueprint, jsonify, request
-from sqlalchemy import exists, select
+from sqlalchemy import select
 from sqlalchemy.dialects.postgresql import insert as pg_insert
 from sqlalchemy.exc import IntegrityError
 
 from ..extensions import get_session
 from ..models import Tag, TagKind, TagPositiveConfirmation
-from ..models.tag_allowlist import TagAllowlist
 from ..services.bulk_tag_service import BulkTagService
 from ..services.ml.aliases import AliasService
 from ..services.series_match_service import SeriesMatchService
@@ -297,13 +296,6 @@ async def merge_tag(source_id: int):
             status = 404 if "not found" in msg else 400
             return jsonify({"error": msg}), status
         await session.commit()
-        target_allowlisted = await session.scalar(
-            select(exists().where(TagAllowlist.tag_id == result.target_id))
-        )
-    if target_allowlisted:
-        from ..tasks.ml import apply_allowlist_tags
-
-        apply_allowlist_tags.delay(tag_id=result.target_id)
     return jsonify(
         {
             "target": {

backend/app/celery_app.py

@@ -101,10 +101,6 @@ def make_celery() -> Celery:
                 "task": "backend.app.tasks.ml.backfill",
                 "schedule": 86400.0,
             },
-            "apply-allowlist-sweep-daily": {
-                "task": "backend.app.tasks.ml.apply_allowlist_tags",
-                "schedule": 86400.0,
-            },
             "train-heads-nightly": {
                 "task": "backend.app.tasks.ml.scheduled_train_heads",
                 "schedule": 86400.0,  # passive cadence; manual retrain stays available

backend/app/models/__init__.py

@@ -13,7 +13,6 @@ from .head_auto_apply_run import HeadAutoApplyRun
 from .head_metric import HeadMetric
 from .head_metrics_snapshot import HeadMetricsSnapshot
 from .head_training_run import HeadTrainingRun
-from .image_prediction import ImagePrediction
 from .image_provenance import ImageProvenance
 from .image_record import ImageRecord
 from .image_region import ImageRegion
@@ -34,7 +33,6 @@ from .subscribestar_failed_media import SubscribeStarFailedMedia
 from .subscribestar_seen_media import SubscribeStarSeenMedia
 from .tag import Tag, TagKind, image_tag
 from .tag_alias import TagAlias
-from .tag_allowlist import TagAllowlist
 from .tag_eval_run import TagEvalRun
 from .tag_head import TagHead
 from .tag_positive_confirmation import TagPositiveConfirmation
@@ -59,7 +57,6 @@ __all__ = [
     "SeriesPage",
     "SeriesSuggestion",
     "ImageRecord",
-    "ImagePrediction",
     "ImageProvenance",
     "ImageRegion",
     "Tag",
@@ -78,7 +75,6 @@ __all__ = [
     "HeadMetricsSnapshot",
     "HeadTrainingRun",
     "TagAlias",
-    "TagAllowlist",
     "TagEvalRun",
     "TagHead",
     "TagPositiveConfirmation",

backend/app/models/image_prediction.py

@@ -1,37 +0,0 @@
-"""ImagePrediction — one row per (image, tagger vocab prediction).
-
-Replaces the image_record.tagger_predictions JSON blob (#768). Storing the
-raw Camie/booru vocab name (not a tag_id) preserves the suggestion read
-path's semantics: raw_name → canonical Tag resolution happens at read time
-via the alias map, and accepting a prediction can CREATE the Tag. The store
-floor (ml_settings.tagger_store_floor) is applied at WRITE time, so only
-predictions >= the floor land here.
-"""
-
-from sqlalchemy import Float, ForeignKey, Index, String, UniqueConstraint
-from sqlalchemy.orm import Mapped, mapped_column
-
-from .base import Base
-
-
-class ImagePrediction(Base):
-    __tablename__ = "image_prediction"
-    __table_args__ = (
-        UniqueConstraint(
-            "image_record_id", "raw_name", name="image_raw_name",
-        ),
-        # Per-image read (suggestion build) and the "images with tag X above
-        # Y" query the JSON blob never allowed.
-        Index("ix_image_prediction_image", "image_record_id"),
-        Index("ix_image_prediction_name_score", "raw_name", "score"),
-    )
-
-    id: Mapped[int] = mapped_column(primary_key=True)
-    image_record_id: Mapped[int] = mapped_column(
-        ForeignKey("image_record.id", ondelete="CASCADE"), nullable=False,
-    )
-    # The raw tagger vocab key (booru form) — NOT a tag_id. Resolved to a
-    # canonical Tag at read time, exactly as the old JSON keys were.
-    raw_name: Mapped[str] = mapped_column(String(255), nullable=False)
-    category: Mapped[str] = mapped_column(String(64), nullable=False)
-    score: Mapped[float] = mapped_column(Float, nullable=False)

backend/app/models/tag_allowlist.py

@@ -1,32 +0,0 @@
-"""TagAllowlist — tags the operator opted-in to auto-apply via maintenance."""
-
-from datetime import datetime
-
-from sqlalchemy import CheckConstraint, DateTime, Float, ForeignKey, func
-from sqlalchemy.orm import Mapped, mapped_column
-
-from .base import Base
-
-
-class TagAllowlist(Base):
-    __tablename__ = "tag_allowlist"
-    # Bare name — Base.metadata's naming convention prepends ck_<table>_,
-    # producing the final ck_tag_allowlist_confidence_range (matches migration 0003).
-    __table_args__ = (
-        CheckConstraint(
-            "min_confidence > 0 AND min_confidence <= 1",
-            name="confidence_range",
-        ),
-    )
-
-    tag_id: Mapped[int] = mapped_column(
-        ForeignKey("tag.id", ondelete="CASCADE"), primary_key=True
-    )
-    # Default auto-apply threshold for a newly-accepted tag. 0.90 (lowered from
-    # 0.95 on operator evidence 2026-06-07: 0.95 was too strict and skipped
-    # confident-enough applications). Per-tag value is still tunable in the
-    # allowlist table; existing rows keep whatever they were stored with.
-    min_confidence: Mapped[float] = mapped_column(Float, nullable=False, default=0.90)
-    added_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=True), nullable=False, server_default=func.now()
-    )

backend/app/services/importer.py

@@ -1479,16 +1479,6 @@ class Importer:
         existing.siglip_embedding = None
         existing.siglip_model_version = None
         existing.centroid_scores = None
-        # #768: predictions also live in the normalized image_prediction table
-        # now — clear them so a re-imported file re-derives a fresh set.
-        from sqlalchemy import delete as _delete
-
-        from ..models import ImagePrediction as _ImagePrediction
-        self.session.execute(
-            _delete(_ImagePrediction).where(
-                _ImagePrediction.image_record_id == existing.id
-            )
-        )
         # created_at intentionally preserved; updated_at auto-bumps.
         self.session.flush()
         self.session.commit()

backend/app/services/ml/allowlist.py

@@ -1,36 +1,20 @@
-"""Allowlist semantics: accepting a suggestion adds the canonical tag to
+"""Suggestion actions: accept applies the canonical tag to an image (which
-image_tag AND to tag_allowlist; per-image removal/dismiss writes a rejection.
+feeds head training); dismiss / reject record a per-image rejection.
+
+(The Camie allowlist bulk-apply was retired #1189 — heads + CCIP are the tag
+source, and head auto-apply is the earned propagation. Accept no longer
+allowlists or fans a tag out across the library.)
 """
 
-from collections.abc import Sequence
+from sqlalchemy import delete
-from dataclasses import dataclass
-
-from sqlalchemy import and_, delete, distinct, func, or_, select
 from sqlalchemy.dialects.postgresql import insert
 from sqlalchemy.ext.asyncio import AsyncSession
 
-from ...models import (
+from ...models import TagSuggestionRejection
-    ImagePrediction,
-    MLSettings,
-    Tag,
-    TagAlias,
-    TagAllowlist,
-    TagSuggestionRejection,
-)
 from ...models.tag import image_tag
 from .aliases import AliasService
 
 
-@dataclass(frozen=True)
-class AllowlistRow:
-    tag_id: int
-    tag_name: str
-    tag_kind: str
-    min_confidence: float
-    applied_count: int      # image_tag rows currently carrying this tag
-    coverage_count: int     # images a sweep WOULD cover at min_confidence
-
-
 class AllowlistService:
     def __init__(self, session: AsyncSession):
         self.session = session
@@ -39,21 +23,11 @@ class AllowlistService:
     async def _apply_image_tag(self, image_id: int, tag_id: int, source: str):
         stmt = insert(image_tag).values(
             image_record_id=image_id, tag_id=tag_id, source=source
-        )
+        ).on_conflict_do_nothing(
-        stmt = stmt.on_conflict_do_nothing(
             index_elements=["image_record_id", "tag_id"]
         )
         await self.session.execute(stmt)
 
-    async def _add_to_allowlist(self, tag_id: int) -> bool:
-        """Returns True if newly added (caller should kick off retro-apply)."""
-        exists = await self.session.get(TagAllowlist, tag_id)
-        if exists is not None:
-            return False
-        self.session.add(TagAllowlist(tag_id=tag_id))
-        await self.session.flush()
-        return True
-
     async def _clear_rejection(self, image_id: int, tag_id: int):
         await self.session.execute(
             delete(TagSuggestionRejection)
@@ -61,12 +35,11 @@ class AllowlistService:
             .where(TagSuggestionRejection.tag_id == tag_id)
         )
 
-    async def accept(self, image_id: int, tag_id: int) -> bool:
+    async def accept(self, image_id: int, tag_id: int) -> None:
-        """Accept a suggestion. Returns True if the tag was newly added to
+        """Apply the accepted tag to this image (source='ml_accepted', a head
-        the allowlist (the API layer enqueues apply_allowlist_tags then)."""
+        training positive) and clear any prior rejection."""
         await self._apply_image_tag(image_id, tag_id, source="ml_accepted")
         await self._clear_rejection(image_id, tag_id)
-        return await self._add_to_allowlist(tag_id)
 
     async def add_alias_and_accept(
         self,
@@ -74,17 +47,16 @@ class AllowlistService:
         alias_string: str,
         alias_category: str,
         canonical_tag_id: int,
-    ) -> bool:
+    ) -> None:
         await self.aliases.create(
             alias_string, alias_category, canonical_tag_id
         )
-        return await self.accept(image_id, canonical_tag_id)
+        await self.accept(image_id, canonical_tag_id)
 
     async def dismiss(self, image_id: int, tag_id: int) -> None:
         stmt = insert(TagSuggestionRejection).values(
             image_record_id=image_id, tag_id=tag_id
-        )
+        ).on_conflict_do_nothing(
-        stmt = stmt.on_conflict_do_nothing(
             index_elements=["image_record_id", "tag_id"]
         )
         await self.session.execute(stmt)
@@ -96,118 +68,11 @@ class AllowlistService:
         await self._clear_rejection(image_id, tag_id)
 
     async def reject_applied_tag(self, image_id: int, tag_id: int) -> None:
-        """Operator removed an applied tag from an image. Remove the
+        """Operator removed an applied tag from an image. Remove the image_tag
-        image_tag row AND record a rejection so the allowlist won't
+        row AND record a rejection so head auto-apply won't re-apply it."""
-        re-apply it on the next maintenance sweep."""
         await self.session.execute(
             image_tag.delete()
             .where(image_tag.c.image_record_id == image_id)
             .where(image_tag.c.tag_id == tag_id)
         )
         await self.dismiss(image_id, tag_id)
-
-    async def _store_floor(self) -> float:
-        return (
-            await self.session.execute(
-                select(MLSettings.tagger_store_floor).where(MLSettings.id == 1)
-            )
-        ).scalar_one()
-
-    async def update_threshold(
-        self, tag_id: int, min_confidence: float
-    ) -> None:
-        row = await self.session.get(TagAllowlist, tag_id)
-        if row is not None:
-            # An allowlist tag can't auto-apply more permissively than the
-            # ingest store floor — predictions below tagger_store_floor aren't
-            # stored, so a lower min_confidence would behave identically to the
-            # floor. Clamp so the stored threshold matches actual behavior
-            # (#764).
-            floor = await self._store_floor()
-            row.min_confidence = max(min_confidence, floor)
-
-    async def remove(self, tag_id: int) -> None:
-        await self.session.execute(
-            delete(TagAllowlist).where(TagAllowlist.tag_id == tag_id)
-        )
-
-    async def _coverage_match(self, tag: Tag):
-        """The predicate over image_prediction rows that resolve to `tag`,
-        mirroring tasks.ml._confidence_for_tag's resolution: a prediction whose
-        raw_name equals the tag name (any category), OR an alias maps
-        (raw_name, category) -> this tag. Returns a SQLAlchemy boolean clause.
-        """
-        alias_rows = (
-            await self.session.execute(
-                select(TagAlias.alias_string, TagAlias.alias_category).where(
-                    TagAlias.canonical_tag_id == tag.id
-                )
-            )
-        ).all()
-        name_clause = ImagePrediction.raw_name == tag.name
-        alias_clauses = [
-            and_(
-                ImagePrediction.raw_name == a,
-                ImagePrediction.category == c,
-            )
-            for a, c in alias_rows
-        ]
-        return or_(name_clause, *alias_clauses) if alias_clauses else name_clause
-
-    async def coverage(self, tag_id: int, threshold: float) -> int:
-        """How many distinct images a sweep WOULD cover for this tag at
-        `threshold`: images with a resolving prediction scoring >= threshold.
-        The gross candidate pool (NOT minus already-applied/rejected) — it's
-        the tuning signal for "lower the threshold and ~N more images qualify".
-        """
-        tag = await self.session.get(Tag, tag_id)
-        if tag is None:
-            return 0
-        match = await self._coverage_match(tag)
-        stmt = select(
-            func.count(distinct(ImagePrediction.image_record_id))
-        ).where(ImagePrediction.score >= threshold, match)
-        return (await self.session.execute(stmt)).scalar_one()
-
-    async def list_all(self) -> Sequence[AllowlistRow]:
-        stmt = (
-            select(
-                TagAllowlist.tag_id,
-                Tag.name,
-                Tag.kind,
-                TagAllowlist.min_confidence,
-            )
-            .join(Tag, Tag.id == TagAllowlist.tag_id)
-            .order_by(Tag.name.asc())
-        )
-        rows = (await self.session.execute(stmt)).all()
-        tag_ids = [r[0] for r in rows]
-
-        # Applied counts in ONE grouped query (vs N per-row counts).
-        applied: dict[int, int] = {}
-        if tag_ids:
-            applied = dict(
-                (
-                    await self.session.execute(
-                        select(image_tag.c.tag_id, func.count())
-                        .where(image_tag.c.tag_id.in_(tag_ids))
-                        .group_by(image_tag.c.tag_id)
-                    )
-                ).all()
-            )
-
-        result = []
-        for r in rows:
-            # Coverage is per-tag (alias set differs); allowlist is small.
-            cov = await self.coverage(r[0], r[3])
-            result.append(
-                AllowlistRow(
-                    tag_id=r[0],
-                    tag_name=r[1],
-                    tag_kind=r[2].value if hasattr(r[2], "value") else str(r[2]),
-                    min_confidence=r[3],
-                    applied_count=applied.get(r[0], 0),
-                    coverage_count=cov,
-                )
-            )
-        return result

backend/app/services/ml/tagger.py

@@ -1,210 +0,0 @@
-"""Camie-tagger-v2 ONNX wrapper (CPU).
-
-Single-image at a time. Loaded lazily inside the ml-worker process; NOT
-thread-safe — the ml queue worker runs --concurrency=1 per process (scale ML by
-running multiple worker replicas, not threads).
-
-v2 layout reference: HuggingFace Camais03/camie-tagger-v2 root has
-camie-tagger-v2.onnx (789 MB) + camie-tagger-v2-metadata.json (7.77 MB)
-+ config.json. Tags ship as nested JSON, not CSV. Preprocessing and
-output handling follow the published onnx_inference.py reference:
-ImageNet normalize, NCHW layout, sigmoid on refined logits (output[1]).
-"""
-
-import json
-import os
-from dataclasses import dataclass
-from pathlib import Path
-
-import numpy as np
-from PIL import Image, ImageFile
-
-# Cap inference threads (see Tagger.load) so each ml-worker replica is a bounded
-# core consumer on a shared node — keep N_replicas × this within the cores
-# allotted to ML so replicas don't oversubscribe the box / starve the DB.
-_INTRA_OP_THREADS = 4
-
-# onnxruntime lives in requirements-ml.txt only — it is NOT installed in the
-# lean web image or in CI. Imported lazily inside Tagger.load() so this module
-# imports fine without it (the suggestion service imports SURFACED_CATEGORIES
-# from here in the web container, and CI collects the pure-logic tests).
-
-# Tolerate minutely-truncated source images (same rationale as IR's wd14.py:
-# a few missing bytes at the JPEG EOI shouldn't block tagging the whole image).
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-
-MODEL_NAME = os.environ.get("CAMIE_MODEL_NAME", "camie-tagger-v2")
-_MODEL_DIR = Path(os.environ.get("ML_MODEL_DIR", "/models")) / "camie"
-_MODEL_FILE = f"{MODEL_NAME}.onnx"
-_METADATA_FILE = f"{MODEL_NAME}-metadata.json"
-
-# Ingest floor below which predictions aren't stored (keeps the JSON compact).
-# DEFAULT/fallback only — the live value is DB-backed
-# (ml_settings.tagger_store_floor) and passed into infer() per call by the ml
-# task. 0.70: the suggestion path already filters there and the centroid path
-# covers lower-confidence preferred tags, so the sub-0.70 tail is redundant
-# (it had bloated image_record's TOAST to ~100 GB; plan-task #764).
-DEFAULT_STORE_FLOOR = 0.70
-
-# The categories FC-2b surfaces in the UI. Others (meta/rating/year) are
-# still stored but the suggestion service filters them out.
-# 'artist' retired in FC-2d-vii-c — artist identity is acquisition-derived
-# (image_record.artist_id), never ML-inferred. 'copyright' retired
-# 2026-06-01 — operator doesn't use the copyright tag-kind; fandom is
-# this app's franchise/series concept (per TagsView.vue's doc comment).
-# Raw predictions for both categories still get stored at STORE_FLOOR but
-# don't surface in suggestions.
-SURFACED_CATEGORIES = {"character", "general"}
-
-# ImageNet preprocessing constants (per Camie v2 onnx_inference.py).
-_IMAGENET_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32)
-_IMAGENET_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32)
-# Square-pad color ≈ ImageNet mean × 255 (matches reference inference).
-_PAD_COLOR = (124, 116, 104)
-
-
-@dataclass(frozen=True)
-class TagPrediction:
-    name: str
-    category: str
-    confidence: float
-
-
-class Tagger:
-    def __init__(self, model_dir: Path | None = None):
-        self._model_dir = model_dir or _MODEL_DIR
-        self._session = None  # onnxruntime.InferenceSession once load()ed
-        self._tag_names: list[str] | None = None
-        self._tag_categories: list[str] | None = None
-        self._input_name: str | None = None
-        self._input_size: int = 512
-
-    def load(self) -> None:
-        if self._session is not None:
-            return
-        model_path = self._model_dir / _MODEL_FILE
-        meta_path = self._model_dir / _METADATA_FILE
-        if not model_path.is_file():
-            raise RuntimeError(
-                f"Camie {_MODEL_FILE} missing at {model_path}. "
-                f"Populate /models via the ml-worker downloader."
-            )
-        if not meta_path.is_file():
-            raise RuntimeError(
-                f"Camie {_METADATA_FILE} missing at {meta_path}. "
-                f"Populate /models via the ml-worker downloader."
-            )
-
-        with open(meta_path) as f:
-            metadata = json.load(f)
-
-        # Per Camie v2 onnx_inference.py: idx_to_tag is keyed by str(idx);
-        # tag_to_category maps tag_name -> category. Project to two parallel
-        # lists indexed by output position for O(1) lookup in the hot path.
-        ds = metadata["dataset_info"]
-        idx_to_tag = ds["tag_mapping"]["idx_to_tag"]
-        tag_to_category = ds["tag_mapping"]["tag_to_category"]
-        total = ds["total_tags"]
-        names: list[str] = []
-        cats: list[str] = []
-        for i in range(total):
-            name = idx_to_tag.get(str(i), f"unknown-{i}")
-            names.append(name)
-            cats.append(tag_to_category.get(name, "general"))
-
-        # Input size from metadata; fall back to 512 (the v2 default).
-        self._input_size = int(
-            metadata.get("model_info", {}).get("img_size", 512)
-        )
-
-        # Lazy import — kept after the file-existence checks so the
-        # missing-model RuntimeError still fires first in environments
-        # without onnxruntime (CI / lean web image).
-        import onnxruntime as ort
-
-        # Cap the intra-op thread pool. ONNX Runtime otherwise sizes it to ALL
-        # host cores, so on a shared node each ml-worker replica would grab every
-        # core and oversubscribe (and starve the co-located DB/web). Bounding it
-        # makes each replica a predictable core consumer — run N replicas where
-        # N × _INTRA_OP_THREADS stays within the cores you allot to ML.
-        opts = ort.SessionOptions()
-        opts.intra_op_num_threads = _INTRA_OP_THREADS
-        session = ort.InferenceSession(
-            str(model_path), sess_options=opts, providers=["CPUExecutionProvider"],
-        )
-        self._input_name = session.get_inputs()[0].name
-        # Assign sentinels last so a partial load isn't observable.
-        self._tag_names = names
-        self._tag_categories = cats
-        self._session = session
-
-    def _preprocess(self, image_path: Path) -> np.ndarray:
-        img = Image.open(image_path)
-        # Composite RGBA onto neutral so transparency doesn't bias the model.
-        if img.mode == "RGBA":
-            bg = Image.new("RGBA", img.size, (255, 255, 255, 255))
-            bg.paste(img, mask=img.split()[3])
-            img = bg.convert("RGB")
-        elif img.mode != "RGB":
-            img = img.convert("RGB")
-
-        # Pad to square with ImageNet-mean color, then bicubic resize.
-        w, h = img.size
-        side = max(w, h)
-        square = Image.new("RGB", (side, side), _PAD_COLOR)
-        square.paste(img, ((side - w) // 2, (side - h) // 2))
-        square = square.resize(
-            (self._input_size, self._input_size), Image.BICUBIC
-        )
-
-        arr = np.array(square, dtype=np.float32) / 255.0   # HWC, [0,1]
-        arr = (arr - _IMAGENET_MEAN) / _IMAGENET_STD        # ImageNet normalize
-        arr = arr.transpose(2, 0, 1)                        # HWC -> CHW
-        return arr[np.newaxis, :, :, :]                     # NCHW
-
-    def infer(
-        self, image_path: Path, *, store_floor: float = DEFAULT_STORE_FLOOR,
-    ) -> dict[str, TagPrediction]:
-        """Run Camie v2 on one image. Returns {name: TagPrediction} with
-        confidence >= store_floor (across all categories — the suggestion
-        service does category filtering later). store_floor is the DB-backed
-        ml_settings.tagger_store_floor, passed in by the ml task.
-
-        v2 emits multiple outputs; we use the refined predictions
-        (output[1] per onnx_inference.py). Sigmoid is applied to raw
-        logits to produce [0,1] confidence scores.
-        """
-        self.load()
-        x = self._preprocess(image_path)
-        outputs = self._session.run(None, {self._input_name: x})
-        # Refined predictions if present (v2 emits initial + refined),
-        # fall back to initial for single-output forks.
-        logits = outputs[1] if len(outputs) > 1 else outputs[0]
-        # Squeeze batch dim, apply sigmoid.
-        probs = 1.0 / (1.0 + np.exp(-logits[0]))
-        results: dict[str, TagPrediction] = {}
-        names = self._tag_names
-        cats = self._tag_categories
-        for idx, score in enumerate(probs):
-            conf = float(score)
-            if conf < store_floor:
-                continue
-            if idx >= len(names):
-                # Output longer than metadata declared — shouldn't happen but
-                # don't crash the import pipeline if v2 metadata desynchronizes.
-                continue
-            results[names[idx]] = TagPrediction(
-                name=names[idx], category=cats[idx], confidence=conf
-            )
-        return results
-
-
-_default_tagger: Tagger | None = None
-
-
-def get_tagger() -> Tagger:
-    """Process-level singleton so the ONNX session loads once per worker."""
-    global _default_tagger
-    if _default_tagger is None:
-        _default_tagger = Tagger()
-    return _default_tagger

backend/app/services/tag_service.py

@@ -10,7 +10,6 @@ from sqlalchemy.dialects.postgresql import insert as pg_insert
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from ..models import HeadMetric, Tag, TagHead, TagKind, image_tag
-from ..models.tag_allowlist import TagAllowlist
 from .db_helpers import get_or_create
 from .tag_query import fandom_join_alias, tag_columns
 
@@ -303,28 +302,22 @@ class TagService:
 
     async def _keep_as_alias(self, tag_id: int) -> bool:
         """A merged-away tag's old name must survive as an alias iff the ML
-        pipeline has ever applied it OR could re-emit it (allowlisted) —
+        pipeline has ever applied it (manual accept or head auto-apply) — so a
-        otherwise the proactive apply_allowlist_tags worker would silently
+        re-application or an alias remap resolves the canonical name. Purely-
-        regenerate it. Purely-manual, ML-unknown tags are deleted outright (no
+        manual, ML-unknown tags are deleted outright (no DB bloat)."""
-        DB bloat)."""
         is_machine = await self.session.scalar(
             select(
                 exists().where(
                     and_(
                         image_tag.c.tag_id == tag_id,
                         image_tag.c.source.in_(
-                            ("ml_auto", "ml_accepted", "auto")
+                            ("ml_auto", "ml_accepted", "head_auto", "auto")
                         ),
                     )
                 )
             )
         )
-        if is_machine:
+        return bool(is_machine)
-            return True
-        allowlisted = await self.session.scalar(
-            select(exists().where(TagAllowlist.tag_id == tag_id))
-        )
-        return bool(allowlisted)
 
     async def rename(self, tag_id: int, new_name: str) -> Tag:
         """Rename a tag. Raises TagMergeConflict if the new name collides
@@ -564,7 +557,6 @@ class TagService:
 
         merged_count = await self._repoint_image_tags(source_id, target_id)
         await self._repoint_rejections(source_id, target_id)
-        await self._repoint_allowlist(source_id, target_id)
         await self._repoint_aliases(source_id, target_id)
         await self._repoint_fandom_children(
             source_id, target_id, source_kind
@@ -630,23 +622,6 @@ class TagService:
             .values(tag_id=tgt)
         )
 
-    async def _repoint_allowlist(self, src: int, tgt: int) -> None:
-        tgt_has = await self.session.scalar(
-            select(exists().where(TagAllowlist.tag_id == tgt))
-        )
-        if tgt_has:
-            await self.session.execute(
-                text("DELETE FROM tag_allowlist WHERE tag_id = :src"),
-                {"src": src},
-            )
-        else:
-            await self.session.execute(
-                update(TagAllowlist)
-                .where(TagAllowlist.tag_id == src)
-                .values(tag_id=tgt)
-            )
-
-
     async def _repoint_aliases(self, src: int, tgt: int) -> None:
         from ..models.tag_alias import TagAlias
 

backend/app/tasks/ml.py

@@ -1,20 +1,19 @@
-"""ML Celery tasks: per-image inference, backfill discovery, head training,
+"""ML Celery tasks: per-image embedding, backfill discovery, head training,
-allowlist auto-apply, model self-heal.
+model self-heal.
 
-All run on the ml-worker (queue 'ml') except apply_allowlist_tags sweeps which
+All run on the ml-worker (queue 'ml'). Sync sessions (Celery workers are sync
-are 'maintenance' lane. Sync sessions (Celery workers are sync processes), same
+processes), same pattern as FC-2a tasks.
-pattern as FC-2a tasks.
 """
 
 import logging
 from pathlib import Path
 
 from celery.exceptions import SoftTimeLimitExceeded
-from sqlalchemy import delete, select
+from sqlalchemy import select
 from sqlalchemy.exc import DBAPIError, OperationalError
 
 from ..celery_app import celery
-from ..models import ImagePrediction, ImageRecord, MLSettings
+from ..models import ImageRecord, MLSettings
 from ._sync_engine import sync_session_factory as _sync_session_factory
 
 log = logging.getLogger(__name__)
@@ -46,19 +45,16 @@ def _is_video(path: Path) -> bool:
     time_limit=1200,       # 20 min hard
 )
 def tag_and_embed(self, image_id: int) -> dict:
-    """Run Camie + SigLIP on one image; store predictions + embedding;
+    """Compute + store one image's SigLIP embedding.
-    then enqueue per-image allowlist application.
 
     Video (#747): sample frames at a fixed cadence (ml_settings
-    video_frame_interval_seconds, capped at video_max_frames), keep a tag only if
+    video_frame_interval_seconds, capped at video_max_frames) and mean-pool the
-    it appears in >= video_min_tag_frames frames and average its confidence over
+    per-frame SigLIP embeddings. On no-frames returns status='no_frames' (not an
-    those frames (mean-pool, not max — kills one-frame noise); mean-pool the
+    error). (Camie tagging was retired #1189 — heads + CCIP are the tag source.)
-    SigLIP embeddings. On no-frames returns status='no_frames' (not an error).
     """
     import time
 
     from ..services.ml.embedder import get_embedder
-    from ..services.ml.tagger import get_tagger
 
     # Phase + file context, so a timeout/crash names WHICH file and WHERE it
     # died instead of a bare SoftTimeLimitExceeded() (operator-flagged 2026-06-08:
@@ -94,15 +90,13 @@ def tag_and_embed(self, image_id: int) -> dict:
                 return {"status": "file_missing", "image_id": image_id}
 
             phase = "load_models"
-            tagger = get_tagger()
             embedder = get_embedder(settings.embedder_model_name)
 
             if is_vid:
                 # Layer-3 isolation: ffprobe (a separate process) validates
-                # the container before we burn ~20 GPU ops sampling frames
+                # the container before we burn GPU ops sampling frames from it.
-                # from it. A corrupt video that would crash the frame
+                # A corrupt video that would crash the frame decoder is rejected
-                # decoder is rejected cleanly here instead of taking down
+                # cleanly here instead of taking down the ml-worker.
-                # the ml-worker. Operator-flagged 2026-05-28.
                 phase = "video_probe"
                 from ..utils import safe_probe
                 vprobe = safe_probe.probe_video(src)
@@ -115,48 +109,23 @@ def tag_and_embed(self, image_id: int) -> dict:
                         "reason": vprobe.reason,
                     }
                 phase = "video_sample_frames"
-                t0 = time.monotonic()
                 frames = _sample_video_frames(
                     src,
                     interval=settings.video_frame_interval_seconds,
                     max_frames=settings.video_max_frames,
                 )
-                log.info(
-                    "tag_and_embed sampled %d frame(s) in %.1fs: %s",
-                    len(frames), time.monotonic() - t0, ctx,
-                )
                 if not frames:
                     return {"status": "no_frames", "image_id": image_id}
-                phase = "video_infer"
+                phase = "video_embed"
                 import numpy as np
 
-                preds = _aggregate_video_predictions(
+                # Mean-pool the per-frame SigLIP embeddings into one vector.
-                    [tagger.infer(f, store_floor=settings.tagger_store_floor)
-                     for f in frames],
-                    min_frames=settings.video_min_tag_frames,
-                )
                 embedding = np.mean(
                     [embedder.infer(f) for f in frames], axis=0
                 ).astype("float32")
-                log.info(
-                    "tag_and_embed video aggregated %d tag(s) from %d frame(s) "
-                    "(min_frames=%d): %s",
-                    len(preds), len(frames), settings.video_min_tag_frames, ctx,
-                )
                 for f in frames:
                     f.unlink(missing_ok=True)
             else:
-                phase = "tag"
-                t0 = time.monotonic()
-                raw = tagger.infer(src, store_floor=settings.tagger_store_floor)
-                log.info(
-                    "tag_and_embed tagged in %.1fs (%d tags): %s",
-                    time.monotonic() - t0, len(raw), ctx,
-                )
-                preds = {
-                    name: {"category": p.category, "confidence": p.confidence}
-                    for name, p in raw.items()
-                }
                 phase = "embed"
                 t0 = time.monotonic()
                 embedding = embedder.infer(src)
@@ -166,28 +135,9 @@ def tag_and_embed(self, image_id: int) -> dict:
                 )
 
             phase = "persist"
-            record.tagger_model_version = settings.tagger_model_version
             record.siglip_embedding = embedding.tolist()
             record.siglip_model_version = settings.embedder_model_version
             session.add(record)
-            # Write the normalized image_prediction rows (#768) — the sole home
-            # for predictions now (image_record.tagger_predictions was dropped in
-            # migration 0046). Delete-then-insert keeps a re-tag idempotent;
-            # tagger_store_floor was already applied in tagger.infer, so preds is
-            # the >=floor set.
-            session.execute(
-                delete(ImagePrediction).where(
-                    ImagePrediction.image_record_id == image_id
-                )
-            )
-            session.add_all([
-                ImagePrediction(
-                    image_record_id=image_id, raw_name=name,
-                    category=p.get("category", "general"),
-                    score=float(p.get("confidence", 0.0)),
-                )
-                for name, p in preds.items()
-            ])
             session.commit()
     except SoftTimeLimitExceeded:
         log.error(
@@ -210,11 +160,8 @@ def tag_and_embed(self, image_id: int) -> dict:
         )
         raise
 
-    log.info(
+    log.info("tag_and_embed ok in %.1fs: %s", _elapsed(), ctx)
-        "tag_and_embed ok in %.1fs (%d tags): %s", _elapsed(), len(preds), ctx
+    return {"status": "ok", "image_id": image_id}
-    )
-    apply_allowlist_tags.delay(image_id=image_id)
-    return {"status": "ok", "image_id": image_id, "tags": len(preds)}
 
 
 def _sample_video_frames(
@@ -273,68 +220,24 @@ def _sample_video_frames(
     return out
 
 
-def _aggregate_video_predictions(per_frame: list[dict], *, min_frames: int) -> dict:
-    """Aggregate per-frame {name: TagPrediction} into one prediction set (#747).
-
-    A tag is kept only if it appears (≥ the tagger store floor, already applied)
-    in at least `min_frames` of the sampled frames — because sampling is at a
-    fixed cadence, that means it was on screen for roughly min_frames×interval
-    seconds, so a single-frame flicker / scene-transition artifact is dropped
-    while a genuine scene-local tag in a long video survives. Confidence is the
-    MEAN over the frames where the tag appears (not max — max re-inflated the
-    one-frame noise this whole change exists to remove).
-
-    `min_frames` is clamped to the number of frames actually sampled so a very
-    short video (1–2 frames) still tags instead of dropping everything.
-    """
-    n = len(per_frame)
-    if n == 0:
-        return {}
-    threshold = max(1, min(min_frames, n))
-    agg: dict[str, dict] = {}
-    for frame_preds in per_frame:
-        for name, p in frame_preds.items():
-            cur = agg.get(name)
-            if cur is None:
-                agg[name] = {"category": p.category, "sum": p.confidence, "count": 1}
-            else:
-                cur["sum"] += p.confidence
-                cur["count"] += 1
-    return {
-        name: {"category": v["category"], "confidence": v["sum"] / v["count"]}
-        for name, v in agg.items()
-        if v["count"] >= threshold
-    }
-
-
 @celery.task(name="backend.app.tasks.ml.backfill", bind=True)
 def backfill(self) -> int:
-    """Enqueue tag_and_embed for images missing predictions/embeddings for
+    """Enqueue tag_and_embed (embed-only) for images with no SigLIP embedding.
-    the current model versions. Keyset pagination by id ASC (restart-safe).
+    Keyset pagination by id ASC (restart-safe).
+
+    NB: a siglip MODEL-VERSION mismatch (an operator model swap, #1190) is NOT
+    re-embedded here — the CPU ml-worker can't churn the library at 384/512px;
+    the GPU agent owns version re-embeds via the 'embed' job.
     """
     SessionLocal = _sync_session_factory()
     enqueued = 0
     last_id = 0
     with SessionLocal() as session:
-        settings = session.execute(
-            select(MLSettings).where(MLSettings.id == 1)
-        ).scalar_one()
         while True:
             rows = session.execute(
                 select(ImageRecord.id)
                 .where(ImageRecord.id > last_id)
-                .where(
+                .where(ImageRecord.siglip_embedding.is_(None))
-                    (ImageRecord.tagger_model_version.is_(None))
-                    | (
-                        ImageRecord.tagger_model_version
-                        != settings.tagger_model_version
-                    )
-                    | (ImageRecord.siglip_embedding.is_(None))
-                    # NB: a siglip MODEL-VERSION mismatch (an operator model swap,
-                    # #1190) is intentionally NOT re-embedded here — the CPU
-                    # ml-worker can't churn the whole library at 384/512px. The
-                    # GPU agent owns version re-embeds via the 'embed' job.
-                )
                 .order_by(ImageRecord.id.asc())
                 .limit(500)
             ).scalars().all()
@@ -347,146 +250,6 @@ def backfill(self) -> int:
     return enqueued
 
 
-@celery.task(
-    name="backend.app.tasks.ml.apply_allowlist_tags",
-    bind=True,
-    # Audit 2026-06-02 — the full-sweep mode (neither tag_id nor image_id)
-    # is O(images × allowlist) and legitimately runs >5 min on large
-    # libraries. Cap matches the maintenance queue's recovery threshold.
-    soft_time_limit=1800, time_limit=2100,
-)
-def apply_allowlist_tags(self, tag_id: int | None = None,
-                         image_id: int | None = None) -> int:
-    """Retroactively apply allowlisted tags.
-
-    Modes:
-    - tag_id only      : scan all images for this tag.
-    - image_id only    : scan all allowlisted tags for this image.
-    - both             : just the (image, tag) pair.
-    - neither          : full sweep (daily beat).
-
-    Skips: already-applied, rejected (tag_suggestion_rejection), or
-    confidence below the tag's allowlist min_confidence. Applied with
-    source='ml_auto'.
-    """
-    from sqlalchemy import and_
-    from sqlalchemy import select as sa_select
-    from sqlalchemy.dialects.postgresql import insert as pg_insert
-
-    from ..models import TagAllowlist, TagSuggestionRejection
-    from ..models.tag import image_tag
-
-    SessionLocal = _sync_session_factory()
-    applied = 0
-    with SessionLocal() as session:
-        allow_rows = session.execute(
-            sa_select(TagAllowlist.tag_id, TagAllowlist.min_confidence)
-            if tag_id is None
-            else sa_select(
-                TagAllowlist.tag_id, TagAllowlist.min_confidence
-            ).where(TagAllowlist.tag_id == tag_id)
-        ).all()
-        allow = {r[0]: r[1] for r in allow_rows}
-        if not allow:
-            return 0
-
-        # Images that have any predictions (#768: from image_prediction, not
-        # the old JSON column), optionally narrowed to one image.
-        img_ids_query = sa_select(ImagePrediction.image_record_id).distinct()
-        if image_id is not None:
-            img_ids_query = img_ids_query.where(
-                ImagePrediction.image_record_id == image_id
-            )
-
-        for (img_id,) in session.execute(img_ids_query).all():
-            preds = _load_predictions_sync(session, img_id)
-            for a_tag_id, min_conf in allow.items():
-                exists = session.execute(
-                    sa_select(image_tag.c.tag_id).where(
-                        and_(
-                            image_tag.c.image_record_id == img_id,
-                            image_tag.c.tag_id == a_tag_id,
-                        )
-                    )
-                ).scalar_one_or_none()
-                if exists is not None:
-                    continue
-                rej = session.get(
-                    TagSuggestionRejection, (img_id, a_tag_id)
-                )
-                if rej is not None:
-                    continue
-                from ..models import Tag
-
-                tag = session.get(Tag, a_tag_id)
-                if tag is None:
-                    continue
-                conf = _confidence_for_tag(session, tag, preds)
-                if conf is None or conf < min_conf:
-                    continue
-                stmt = pg_insert(image_tag).values(
-                    image_record_id=img_id,
-                    tag_id=a_tag_id,
-                    source="ml_auto",
-                )
-                stmt = stmt.on_conflict_do_nothing(
-                    index_elements=["image_record_id", "tag_id"]
-                )
-                session.execute(stmt)
-                applied += 1
-        session.commit()
-    return applied
-
-
-def _load_predictions_sync(session, image_id: int) -> dict:
-    """Predictions for one image from image_prediction (#768), in the
-    {raw_name: {category, confidence}} shape _confidence_for_tag consumes —
-    keeps the allowlist resolution logic unchanged."""
-    from sqlalchemy import select as sa_select
-
-    rows = session.execute(
-        sa_select(
-            ImagePrediction.raw_name,
-            ImagePrediction.category,
-            ImagePrediction.score,
-        ).where(ImagePrediction.image_record_id == image_id)
-    ).all()
-    return {
-        r.raw_name: {"category": r.category, "confidence": r.score}
-        for r in rows
-    }
-
-
-def _confidence_for_tag(session, tag, preds: dict) -> float | None:
-    """Highest confidence among predictions that resolve to `tag` —
-    either the prediction name equals the tag name, or an alias maps
-    (prediction name, category) -> tag.id.
-    """
-    from sqlalchemy import select as sa_select
-
-    from ..models import TagAlias
-
-    best: float | None = None
-    direct = preds.get(tag.name)
-    if direct is not None:
-        best = float(direct.get("confidence", 0.0))
-    alias_rows = session.execute(
-        sa_select(TagAlias.alias_string, TagAlias.alias_category).where(
-            TagAlias.canonical_tag_id == tag.id
-        )
-    ).all()
-    for alias_string, alias_category in alias_rows:
-        p = preds.get(alias_string)
-        if p is None:
-            continue
-        if p.get("category") != alias_category:
-            continue
-        c = float(p.get("confidence", 0.0))
-        if best is None or c > best:
-            best = c
-    return best
-
-
 @celery.task(
     name="backend.app.tasks.ml.tag_eval_run",
     bind=True,

frontend/src/components/settings/AllowlistTable.vue

@@ -1,120 +0,0 @@
-<template>
-  <MaintenanceTile
-    icon="mdi-playlist-check"
-    :title="`Allowlisted tags (${store.rows.length})`"
-    blurb="Tags auto-applied to images that score above their threshold. Tune the
-           threshold and see how many images it would cover."
-  >
-    <v-data-table-virtual
-      :headers="headers" :items="store.rows" :loading="store.loading"
-      height="360" density="compact" fixed-header
-      no-data-text="No tags on the allowlist yet — accept a suggestion to add one."
-    >
-      <template #item.applied_count="{ item }">
-        <span class="fc-num">{{ item.applied_count ?? '—' }}</span>
-      </template>
-
-      <template #item.min_confidence="{ item }">
-        <div class="fc-thr">
-          <v-text-field
-            :model-value="item.min_confidence" type="number"
-            density="compact" hide-details style="max-width: 100px;"
-            :min="floor" max="1" step="0.05"
-            :aria-label="`Auto-apply threshold for ${item.tag_name}`"
-            @update:model-value="(v) => onThreshold(item, v)"
-          />
-          <span
-            v-if="proj[item.tag_id]"
-            class="fc-thr__proj"
-            :class="{ 'fc-thr__proj--loading': proj[item.tag_id].loading }"
-            :title="`At ${proj[item.tag_id].threshold}, a sweep would cover this many images`"
-          >≈ {{ proj[item.tag_id].count }} at {{ proj[item.tag_id].threshold }}</span>
-        </div>
-      </template>
-
-      <template #item.coverage_count="{ item }">
-        <span class="fc-num" :title="`Images a sweep covers at ${item.min_confidence}`">
-          {{ item.coverage_count ?? '—' }}
-        </span>
-      </template>
-
-      <template #item.actions="{ item }">
-        <v-btn
-          icon="mdi-delete" size="x-small" variant="text" color="error"
-          :aria-label="`Remove ${item.tag_name} from the allowlist`"
-          @click="store.remove(item.tag_id)"
-        />
-      </template>
-    </v-data-table-virtual>
-    <p class="fc-muted text-caption mt-2">
-      <strong>Applied</strong> = images currently carrying the tag.
-      <strong>Covers</strong> = images a sweep would auto-apply it to at the
-      current threshold. Lower the threshold to cover more (less certain) images.
-    </p>
-  </MaintenanceTile>
-</template>
-
-<script setup>
-import { computed, onMounted, reactive } from 'vue'
-import { useAllowlistStore } from '../../stores/allowlist.js'
-import { useMLStore } from '../../stores/ml.js'
-import MaintenanceTile from '../common/MaintenanceTile.vue'
-
-const store = useAllowlistStore()
-const ml = useMLStore()
-// min_confidence can't be set below the tagger store floor — predictions
-// below it aren't stored, so a lower threshold would behave identically to
-// the floor. The backend clamps too (#764).
-const floor = computed(() => ml.settings?.tagger_store_floor ?? 0.70)
-const headers = [
-  { title: 'Tag', key: 'tag_name', sortable: true },
-  { title: 'Kind', key: 'tag_kind', sortable: true, width: 100 },
-  { title: 'Applied', key: 'applied_count', sortable: true, width: 90 },
-  { title: 'Min confidence', key: 'min_confidence', sortable: false, width: 220 },
-  { title: 'Covers', key: 'coverage_count', sortable: true, width: 90 },
-  { title: '', key: 'actions', sortable: false, width: 56 }
-]
-
-// Per-row live projection while the operator drags a threshold:
-//   proj[tagId] = { threshold, count, loading }
-const proj = reactive({})
-
-onMounted(() => {
-  store.load()
-  if (!ml.settings) ml.loadSettings()
-})
-
-const debounces = {}
-function onThreshold(item, value) {
-  const tagId = item.tag_id
-  const v = Math.max(parseFloat(value), floor.value)
-  if (!(v > 0 && v <= 1)) return
-  const shown = Number(v.toFixed(2))
-  // Optimistic live projection box (loading until the count returns).
-  proj[tagId] = { threshold: shown, count: proj[tagId]?.count ?? '…', loading: true }
-  if (debounces[tagId]) clearTimeout(debounces[tagId])
-  debounces[tagId] = setTimeout(async () => {
-    try {
-      const { count } = await store.coverage(tagId, v)
-      proj[tagId] = { threshold: shown, count, loading: false }
-    } catch {
-      delete proj[tagId]  // drop the projection rather than show a wrong number
-    }
-    // Commit the new threshold (also refreshes the row's stored coverage_count).
-    store.updateThreshold(tagId, v)
-  }, 500)
-}
-</script>
-
-<style scoped>
-.fc-num { font-variant-numeric: tabular-nums; }
-.fc-thr { display: flex; align-items: center; gap: 10px; }
-.fc-thr__proj {
-  font-size: 12px;
-  font-variant-numeric: tabular-nums;
-  color: rgb(var(--v-theme-accent));
-  white-space: nowrap;
-}
-.fc-thr__proj--loading { color: rgb(var(--v-theme-on-surface-variant)); }
-.fc-muted { color: rgb(var(--v-theme-on-surface-variant)); }
-</style>

frontend/src/components/settings/MLBackfillCard.vue

@@ -2,12 +2,13 @@
   <MaintenanceTile
     icon="mdi-refresh"
     title="ML backfill"
-    blurb="Re-run tagging + embeddings on images missing them."
+    blurb="Compute SigLIP embeddings on images missing them."
     :open="busy"
   >
       <p class="text-body-2 mb-3">
-        Re-run Camie + SigLIP on images missing predictions or embeddings
+        Compute the SigLIP embedding for any image that doesn't have one yet
-        for the current model versions. Safe to re-run.
+        (CPU). Safe to re-run. To re-embed under a NEW model, use the GPU
+        agent's "Re-embed library" instead.
       </p>
       <v-btn color="primary" rounded="pill" :loading="busy" @click="run">
         <v-icon start>mdi-refresh</v-icon> Run backfill now

frontend/src/components/settings/MaintenancePanel.vue

@@ -1,8 +1,8 @@
 <template>
   <div class="fc-maint">
     <p class="fc-muted text-body-2 mb-5">
-      One-off backfills, tagging config and storage tools. The ML backfill runs
+      One-off backfills, tagging config and storage tools. Heads train nightly
-      nightly; the allowlist auto-applies accepted tags. Click a tile to open it.
+      and auto-apply earned tags. Click a tile to open it.
     </p>
 
     <section class="fc-section">
@@ -26,7 +26,6 @@
         <MLThresholdSliders />
         <HeadsCard />
         <GpuAgentCard />
-        <AllowlistTable />
         <AliasTable />
         <TagEvalCard />
       </div>
@@ -53,7 +52,6 @@ import DbMaintenanceCard from './DbMaintenanceCard.vue'
 import MLThresholdSliders from './MLThresholdSliders.vue'
 import HeadsCard from './HeadsCard.vue'
 import GpuAgentCard from './GpuAgentCard.vue'
-import AllowlistTable from './AllowlistTable.vue'
 import AliasTable from './AliasTable.vue'
 import TagEvalCard from './TagEvalCard.vue'
 import BackupCard from './BackupCard.vue'

frontend/src/stores/allowlist.js

@@ -1,44 +0,0 @@
-import { defineStore } from 'pinia'
-import { ref } from 'vue'
-import { useApi } from '../composables/useApi.js'
-
-export const useAllowlistStore = defineStore('allowlist', () => {
-  const api = useApi()
-  const rows = ref([])
-  const loading = ref(false)
-
-  async function load() {
-    loading.value = true
-    try { rows.value = await api.get('/api/allowlist') }
-    finally { loading.value = false }
-  }
-
-  async function updateThreshold(tagId, minConfidence) {
-    await api.patch(`/api/tags/${tagId}/allowlist`, {
-      body: { min_confidence: minConfidence }
-    })
-    const r = rows.value.find(x => x.tag_id === tagId)
-    if (r) {
-      r.min_confidence = minConfidence
-      // The committed threshold changed the covered pool — refresh the row's
-      // coverage so the table stays truthful after a save.
-      try { r.coverage_count = (await coverage(tagId, minConfidence)).count }
-      catch { /* leave the stale count rather than blank it */ }
-    }
-  }
-
-  // Live "at threshold T, a sweep would cover ~N images" projection for the
-  // tuning dashboard. Returns { count, threshold }.
-  async function coverage(tagId, threshold) {
-    return api.get(`/api/tags/${tagId}/allowlist/coverage`, {
-      params: { threshold }
-    })
-  }
-
-  async function remove(tagId) {
-    await api.delete(`/api/tags/${tagId}/allowlist`)
-    rows.value = rows.value.filter(x => x.tag_id !== tagId)
-  }
-
-  return { rows, loading, load, updateThreshold, coverage, remove }
-})

frontend/src/stores/suggestions.js

@@ -113,7 +113,7 @@ export const useSuggestionsStore = defineStore('suggestions', () => {
       })
       tagId = created.id
     }
-    const res = await api.post(`/api/images/${imageId}/suggestions/accept`, {
+    await api.post(`/api/images/${imageId}/suggestions/accept`, {
       body: { tag_id: tagId }
     })
     // Only drop from THIS image's category list — if the user navigated,
@@ -121,23 +121,14 @@ export const useSuggestionsStore = defineStore('suggestions', () => {
     if (currentImageId === imageId) {
       _dropEverywhere(suggestion)
     }
-    _acceptToast('Tagged', suggestion.display_name, res)
+    _acceptToast('Tagged', suggestion.display_name)
   }
 
-  // One non-blocking toast for accept/alias. When the accept newly allowlisted
+  // One non-blocking toast for accept/alias. The accepted tag is applied to this
-  // the tag, surface the coverage PROJECTION (#7) so the operator sees the
+  // image and feeds head training; head auto-apply handles propagation (earned),
-  // auto-apply reach without a blocking pre-accept preview — the apply itself
+  // so there's no instant fan-out to project.
-  // runs async, hence "~N".
+  function _acceptToast(verb, displayName) {
-  function _acceptToast(verb, displayName, res) {
+    toast({ text: `${verb}: ${displayName}`, type: 'success' })
-    if (res?.allowlisted) {
-      const n = res.projected_count
-      toast({
-        text: `${verb}: ${displayName} — allowlisted, auto-applying to ~${n} image${n === 1 ? '' : 's'}`,
-        type: 'success'
-      })
-    } else {
-      toast({ text: `${verb}: ${displayName}`, type: 'success' })
-    }
   }
 
   async function aliasAccept(suggestion, canonicalTagId) {
@@ -149,7 +140,7 @@ export const useSuggestionsStore = defineStore('suggestions', () => {
     // reappearing unaliased. raw_name is null only for centroid hits, which
     // can't be aliased (the UI hides the action for them).
     const aliasString = suggestion.raw_name ?? suggestion.display_name
-    const res = await api.post(`/api/images/${imageId}/suggestions/alias`, {
+    await api.post(`/api/images/${imageId}/suggestions/alias`, {
       body: {
         alias_string: aliasString,
         alias_category: suggestion.category,
@@ -159,7 +150,7 @@ export const useSuggestionsStore = defineStore('suggestions', () => {
     if (currentImageId === imageId) {
       _dropEverywhere(suggestion)
     }
-    _acceptToast('Aliased & tagged', suggestion.display_name, res)
+    _acceptToast('Aliased & tagged', suggestion.display_name)
   }
 
   // Remove the alias behind an aliased suggestion (the raw prediction reverts to

tests/_prediction_helpers.py

@@ -1,21 +0,0 @@
-"""#768 test helper: seed image_prediction rows.
-
-Read-path tests used to seed ImageRecord(tagger_predictions={...}); predictions
-now live in the normalized image_prediction table, so seed there instead.
-"""
-from backend.app.models import ImagePrediction
-
-
-async def seed_predictions(session, image_id: int, predictions: dict) -> None:
-    """Insert image_prediction rows from a {raw_name: {category, confidence}}
-    dict (the old JSON shape). Caller commits/flushes as needed; this flushes."""
-    session.add_all([
-        ImagePrediction(
-            image_record_id=image_id,
-            raw_name=name,
-            category=p.get("category", "general"),
-            score=float(p.get("confidence", 0.0)),
-        )
-        for name, p in predictions.items()
-    ])
-    await session.flush()

tests/test_api_allowlist.py

@@ -1,88 +0,0 @@
-import pytest
-
-from backend.app.models import ImagePrediction, ImageRecord, TagAllowlist, TagKind
-from backend.app.services.tag_service import TagService
-
-pytestmark = pytest.mark.integration
-
-
-@pytest.mark.asyncio
-async def test_list_and_patch_and_delete(client, db):
-    tag = await TagService(db).find_or_create("AL", TagKind.character)
-    db.add(TagAllowlist(tag_id=tag.id, min_confidence=0.95))
-    await db.commit()
-
-    resp = await client.get("/api/allowlist")
-    assert resp.status_code == 200
-    assert any(r["tag_id"] == tag.id for r in await resp.get_json())
-
-    resp = await client.patch(
-        f"/api/tags/{tag.id}/allowlist", json={"min_confidence": 0.80}
-    )
-    assert resp.status_code == 204
-
-    resp = await client.get(f"/api/tags/{tag.id}/allowlist")
-    assert (await resp.get_json())["min_confidence"] == pytest.approx(0.80)
-
-    resp = await client.delete(f"/api/tags/{tag.id}/allowlist")
-    assert resp.status_code == 204
-    resp = await client.get(f"/api/tags/{tag.id}/allowlist")
-    assert resp.status_code == 404
-
-
-@pytest.mark.asyncio
-async def test_patch_rejects_out_of_range(client, db):
-    tag = await TagService(db).find_or_create("AL2", TagKind.character)
-    db.add(TagAllowlist(tag_id=tag.id))
-    await db.commit()
-    resp = await client.patch(
-        f"/api/tags/{tag.id}/allowlist", json={"min_confidence": 1.5}
-    )
-    assert resp.status_code == 400
-
-
-@pytest.mark.asyncio
-async def test_coverage_endpoint(client, db):
-    tag = await TagService(db).find_or_create("Cover", TagKind.general)
-    db.add(TagAllowlist(tag_id=tag.id, min_confidence=0.90))
-    for i, score in enumerate((0.95, 0.60)):
-        img = ImageRecord(
-            path=f"/images/cov{i}.jpg", sha256=f"cv{i:062d}", size_bytes=1,
-            mime="image/jpeg", width=1, height=1,
-            origin="imported_filesystem", integrity_status="unknown",
-        )
-        db.add(img)
-        await db.flush()
-        db.add(ImagePrediction(
-            image_record_id=img.id, raw_name="Cover",
-            category="general", score=score,
-        ))
-    await db.commit()
-
-    # Explicit threshold.
-    resp = await client.get(
-        f"/api/tags/{tag.id}/allowlist/coverage?threshold=0.90"
-    )
-    assert resp.status_code == 200
-    assert (await resp.get_json())["count"] == 1
-    # Lower what-if threshold widens coverage.
-    resp = await client.get(
-        f"/api/tags/{tag.id}/allowlist/coverage?threshold=0.50"
-    )
-    assert (await resp.get_json())["count"] == 2
-    # No threshold → uses the stored min_confidence (0.90).
-    resp = await client.get(f"/api/tags/{tag.id}/allowlist/coverage")
-    body = await resp.get_json()
-    assert body["count"] == 1
-    assert body["threshold"] == pytest.approx(0.90)
-
-
-@pytest.mark.asyncio
-async def test_coverage_rejects_bad_threshold(client, db):
-    tag = await TagService(db).find_or_create("Cover2", TagKind.general)
-    db.add(TagAllowlist(tag_id=tag.id))
-    await db.commit()
-    resp = await client.get(
-        f"/api/tags/{tag.id}/allowlist/coverage?threshold=2.0"
-    )
-    assert resp.status_code == 400

tests/test_api_suggestions.py

@@ -15,9 +15,7 @@ def eager():
     celery.conf.task_always_eager = False
 
 
-async def _img(db, preds, sha="s" * 64):
+async def _img(db, sha="s" * 64):
-    from tests._prediction_helpers import seed_predictions
-
     img = ImageRecord(
         path=f"/images/{sha}.jpg", sha256=sha, size_bytes=1,
         mime="image/jpeg", width=1, height=1,
@@ -25,8 +23,6 @@ async def _img(db, preds, sha="s" * 64):
     )
     db.add(img)
     await db.commit()
-    await seed_predictions(db, img.id, preds)
-    await db.commit()
     return img
 
 
@@ -60,7 +56,7 @@ async def test_get_suggestions(client, db):
 
 @pytest.mark.asyncio
 async def test_accept_requires_tag_id(client, db):
-    img = await _img(db, {})
+    img = await _img(db)
     resp = await client.post(
         f"/api/images/{img.id}/suggestions/accept", json={}
     )
@@ -68,43 +64,31 @@ async def test_accept_requires_tag_id(client, db):
 
 
 @pytest.mark.asyncio
-async def test_accept_then_applied(client, db):
+async def test_accept_applies_tag_to_image(client, db):
-    img = await _img(db, {})
+    # Camie/allowlist retired (#1189): accept applies the tag to THIS image
+    # (source='ml_accepted', a head-training positive) — no bulk allowlist
+    # fan-out anymore.
+    from backend.app.models.tag import image_tag
+
+    img = await _img(db)
     tag = await TagService(db).find_or_create("AcceptMe", TagKind.character)
     await db.commit()
     resp = await client.post(
         f"/api/images/{img.id}/suggestions/accept", json={"tag_id": tag.id}
     )
     assert resp.status_code == 200
-    body = await resp.get_json()
+    assert (await resp.get_json())["accepted"] is True
-    # #7b: a fresh accept newly-allowlists → projection payload for the toast.
+    src = (await db.execute(
-    assert body["allowlisted"] is True
+        select(image_tag.c.source)
-    assert body["tag_id"] == tag.id
+        .where(image_tag.c.image_record_id == img.id)
-    assert body["tag_name"] == "AcceptMe"
+        .where(image_tag.c.tag_id == tag.id)
-    assert "projected_count" in body
+    )).scalar_one()
-
+    assert src == "ml_accepted"
-
-@pytest.mark.asyncio
-async def test_accept_already_allowlisted_reports_not_new(client, db):
-    img1 = await _img(db, {}, sha="c" * 64)
-    img2 = await _img(db, {}, sha="d" * 64)
-    tag = await TagService(db).find_or_create("Twice", TagKind.character)
-    await db.commit()
-    first = await client.post(
-        f"/api/images/{img1.id}/suggestions/accept", json={"tag_id": tag.id}
-    )
-    assert (await first.get_json())["allowlisted"] is True
-    second = await client.post(
-        f"/api/images/{img2.id}/suggestions/accept", json={"tag_id": tag.id}
-    )
-    body = await second.get_json()
-    assert body["allowlisted"] is False   # already on the allowlist
-    assert "projected_count" not in body
 
 
 @pytest.mark.asyncio
 async def test_dismiss(client, db):
-    img = await _img(db, {})
+    img = await _img(db)
     tag = await TagService(db).find_or_create("DismissMe", TagKind.general)
     await db.commit()
     resp = await client.post(
@@ -115,7 +99,7 @@ async def test_dismiss(client, db):
 
 @pytest.mark.asyncio
 async def test_undismiss_reverses_rejection(client, db):
-    img = await _img(db, {})
+    img = await _img(db)
     tag = await TagService(db).find_or_create("UndismissMe", TagKind.general)
     await db.commit()
     await client.post(
@@ -134,7 +118,7 @@ async def test_undismiss_reverses_rejection(client, db):
 
 @pytest.mark.asyncio
 async def test_alias_requires_fields(client, db):
-    img = await _img(db, {})
+    img = await _img(db)
     resp = await client.post(
         f"/api/images/{img.id}/suggestions/alias", json={"alias_string": "x"}
     )

tests/test_api_tag_merge.py

@@ -68,15 +68,7 @@ async def test_rename_collision_returns_rich_409(client):
 
 
 @pytest.mark.asyncio
-async def test_merge_endpoint_moves_and_deletes(client, monkeypatch):
+async def test_merge_endpoint_moves_and_deletes(client):
-    calls = []
-    from backend.app.tasks import ml as ml_tasks
-
-    monkeypatch.setattr(
-        ml_tasks.apply_allowlist_tags,
-        "delay",
-        lambda **kw: calls.append(kw),
-    )
     tgt = await _mk(client, "Keep", "general")
     src = await _mk(client, "Gone", "general")
     resp = await client.post(
@@ -92,36 +84,6 @@ async def test_merge_endpoint_moves_and_deletes(client, monkeypatch):
     assert r2.status_code == 200
 
 
-@pytest.mark.asyncio
-async def test_merge_enqueues_backfill_when_target_allowlisted(
-    client, monkeypatch
-):
-    calls = []
-    from backend.app.tasks import ml as ml_tasks
-
-    monkeypatch.setattr(
-        ml_tasks.apply_allowlist_tags,
-        "delay",
-        lambda **kw: calls.append(kw),
-    )
-    tgt = await _mk(client, "AllowTgt", "general")
-    src = await _mk(client, "AllowSrc", "general")
-    # No public route adds a tag to the allowlist (it happens via
-    # accept-suggestion); set the row directly through the app session.
-    from backend.app.extensions import get_session
-    from backend.app.models.tag_allowlist import TagAllowlist
-
-    async with get_session() as s:
-        s.add(TagAllowlist(tag_id=tgt))
-        await s.commit()
-
-    resp = await client.post(
-        f"/api/tags/{src}/merge", json={"target_id": tgt}
-    )
-    assert resp.status_code == 200
-    assert calls == [{"tag_id": tgt}]
-
-
 @pytest.mark.asyncio
 async def test_merge_self_is_400(client):
     t = await _mk(client, "Selfie", "general")

tests/test_image_prediction.py

@@ -1,57 +0,0 @@
-"""#768: image_prediction table — model + constraints round-trip."""
-import pytest
-from sqlalchemy import select
-from sqlalchemy.exc import IntegrityError
-
-from backend.app.models import ImagePrediction, ImageRecord
-
-pytestmark = pytest.mark.integration
-
-
-async def _make_image(db, path="/img/p0.jpg", sha="0"):
-    rec = ImageRecord(
-        path=path, sha256=sha.ljust(64, "0")[:64], size_bytes=10,
-        mime="image/jpeg", origin="imported_filesystem",
-    )
-    db.add(rec)
-    await db.flush()
-    return rec
-
-
-@pytest.mark.asyncio
-async def test_image_prediction_round_trip(db):
-    rec = await _make_image(db)
-    db.add_all([
-        ImagePrediction(
-            image_record_id=rec.id, raw_name="blue_eyes",
-            category="general", score=0.92,
-        ),
-        ImagePrediction(
-            image_record_id=rec.id, raw_name="hatsune_miku",
-            category="character", score=0.88,
-        ),
-    ])
-    await db.commit()
-
-    rows = (await db.execute(
-        select(ImagePrediction.raw_name, ImagePrediction.score)
-        .where(ImagePrediction.image_record_id == rec.id)
-        .order_by(ImagePrediction.score.desc())
-    )).all()
-    assert [r.raw_name for r in rows] == ["blue_eyes", "hatsune_miku"]
-
-
-@pytest.mark.asyncio
-async def test_image_prediction_unique_per_image_name(db):
-    rec = await _make_image(db, path="/img/p1.jpg", sha="1")
-    db.add(ImagePrediction(
-        image_record_id=rec.id, raw_name="dup",
-        category="general", score=0.9,
-    ))
-    await db.commit()
-    db.add(ImagePrediction(
-        image_record_id=rec.id, raw_name="dup",
-        category="general", score=0.7,
-    ))
-    with pytest.raises(IntegrityError):
-        await db.commit()

tests/test_migration_0003.py

@@ -5,14 +5,12 @@ from backend.app.models import (
     ImageRecord,
     MLSettings,
     TagAlias,
-    TagAllowlist,
     TagSuggestionRejection,
 )
 
 
 def test_new_tables_registered():
     expected = {
-        "tag_allowlist",
         "tag_suggestion_rejection",
         "tag_alias",
         "ml_settings",
@@ -40,11 +38,6 @@ def test_ml_settings_singleton_constraint():
     assert "ck_ml_settings_singleton" in names
 
 
-def test_tag_allowlist_confidence_check():
-    names = {c.name for c in TagAllowlist.__table__.constraints}
-    assert "ck_tag_allowlist_confidence_range" in names
-
-
 def test_tag_suggestion_rejection_pk():
     pk_cols = {c.name for c in TagSuggestionRejection.__table__.primary_key.columns}
     assert pk_cols == {"image_record_id", "tag_id"}

tests/test_ml_allowlist.py

@@ -1,182 +0,0 @@
-import pytest
-from sqlalchemy import select
-
-from backend.app.models import (
-    ImagePrediction,
-    TagAlias,
-    TagAllowlist,
-    TagKind,
-    TagSuggestionRejection,
-)
-from backend.app.models.tag import image_tag
-from backend.app.services.ml.allowlist import AllowlistService
-from backend.app.services.tag_service import TagService
-
-pytestmark = pytest.mark.integration
-
-
-async def _make_image(db, sha: str = "x" * 64):
-    from backend.app.models import ImageRecord
-    img = ImageRecord(
-        # Full sha in the path — the first 8 chars collide for sequential
-        # shas like c{i:063d}, and path is UNIQUE (uq_image_record_path).
-        path=f"/images/{sha}.jpg", sha256=sha, size_bytes=1,
-        mime="image/jpeg", width=1, height=1,
-        origin="imported_filesystem", integrity_status="unknown",
-    )
-    db.add(img)
-    await db.flush()
-    return img
-
-
-async def _add_pred(db, image_id, raw_name, score, category="general"):
-    db.add(ImagePrediction(
-        image_record_id=image_id, raw_name=raw_name,
-        category=category, score=score,
-    ))
-    await db.flush()
-
-
-@pytest.mark.asyncio
-async def test_accept_applies_and_allowlists(db):
-    img = await _make_image(db)
-    tag = await TagService(db).find_or_create("Hero", TagKind.character)
-    svc = AllowlistService(db)
-    newly_added = await svc.accept(img.id, tag.id)
-    assert newly_added is True
-
-    applied = (
-        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 applied == "ml_accepted"
-    assert await db.get(TagAllowlist, tag.id) is not None
-
-
-@pytest.mark.asyncio
-async def test_accept_idempotent_allowlist(db):
-    img = await _make_image(db)
-    tag = await TagService(db).find_or_create("Hero2", TagKind.character)
-    svc = AllowlistService(db)
-    assert await svc.accept(img.id, tag.id) is True
-    assert await svc.accept(img.id, tag.id) is False
-
-
-@pytest.mark.asyncio
-async def test_reject_applied_tag_records_rejection(db):
-    img = await _make_image(db)
-    tag = await TagService(db).find_or_create("Removeme", TagKind.general)
-    svc = AllowlistService(db)
-    await svc.accept(img.id, tag.id)
-    await svc.reject_applied_tag(img.id, tag.id)
-
-    still_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 still_applied is None
-    rej = await db.get(TagSuggestionRejection, (img.id, tag.id))
-    assert rej is not None
-
-
-@pytest.mark.asyncio
-async def test_dismiss_records_rejection(db):
-    img = await _make_image(db)
-    tag = await TagService(db).find_or_create("Dismissme", TagKind.general)
-    await AllowlistService(db).dismiss(img.id, tag.id)
-    assert await db.get(TagSuggestionRejection, (img.id, tag.id)) is not None
-
-
-@pytest.mark.asyncio
-async def test_add_alias_and_accept(db):
-    img = await _make_image(db)
-    canonical = await TagService(db).find_or_create(
-        "Canonical Char", TagKind.character
-    )
-    svc = AllowlistService(db)
-    await svc.add_alias_and_accept(
-        img.id, "model_char_name", "character", canonical.id
-    )
-    from backend.app.services.ml.aliases import AliasService
-    resolved = await AliasService(db).resolve("model_char_name", "character")
-    assert resolved.id == canonical.id
-    assert await db.get(TagAllowlist, canonical.id) is not None
-
-
-@pytest.mark.asyncio
-async def test_update_threshold_and_remove(db):
-    tag = await TagService(db).find_or_create("Thr", TagKind.general)
-    svc = AllowlistService(db)
-    img = await _make_image(db)
-    await svc.accept(img.id, tag.id)
-    await svc.update_threshold(tag.id, 0.80)
-    row = await db.get(TagAllowlist, tag.id)
-    assert abs(row.min_confidence - 0.80) < 1e-6
-    await svc.remove(tag.id)
-    assert await db.get(TagAllowlist, tag.id) is None
-
-
-@pytest.mark.asyncio
-async def test_coverage_by_threshold_direct_name(db):
-    tag = await TagService(db).find_or_create("Cov", TagKind.general)
-    svc = AllowlistService(db)
-    for i, score in enumerate((0.95, 0.80, 0.60)):
-        img = await _make_image(db, sha=f"c{i:063d}")
-        await _add_pred(db, img.id, "Cov", score)
-    assert await svc.coverage(tag.id, 0.90) == 1
-    assert await svc.coverage(tag.id, 0.70) == 2
-    assert await svc.coverage(tag.id, 0.50) == 3
-
-
-@pytest.mark.asyncio
-async def test_coverage_via_alias_respects_category(db):
-    tag = await TagService(db).find_or_create("Aliased", TagKind.character)
-    db.add(TagAlias(
-        alias_string="model_key", alias_category="character",
-        canonical_tag_id=tag.id,
-    ))
-    await db.flush()
-    svc = AllowlistService(db)
-    hit = await _make_image(db, sha=f"a{0:063d}")
-    await _add_pred(db, hit.id, "model_key", 0.92, category="character")
-    # Same alias string but wrong category must NOT resolve to the tag.
-    miss = await _make_image(db, sha=f"a{1:063d}")
-    await _add_pred(db, miss.id, "model_key", 0.99, category="general")
-    assert await svc.coverage(tag.id, 0.90) == 1
-
-
-@pytest.mark.asyncio
-async def test_list_all_reports_applied_and_coverage(db):
-    tag = await TagService(db).find_or_create("Both", TagKind.general)
-    svc = AllowlistService(db)
-    applied_img = await _make_image(db, sha=f"b{0:063d}")
-    await svc.accept(applied_img.id, tag.id)  # applies + allowlists
-    await _add_pred(db, applied_img.id, "Both", 0.95)
-    # A second image only has a qualifying prediction (covered, not applied).
-    cov_img = await _make_image(db, sha=f"b{1:063d}")
-    await _add_pred(db, cov_img.id, "Both", 0.95)
-
-    rows = await svc.list_all()
-    row = next(r for r in rows if r.tag_id == tag.id)
-    assert row.applied_count == 1                 # only the accepted image
-    assert row.coverage_count == 2                # both have a ≥threshold pred
-
-
-@pytest.mark.asyncio
-async def test_update_threshold_clamped_to_store_floor(db):
-    # A min_confidence below the store floor (default 0.70) is clamped up —
-    # predictions below the floor aren't stored, so a lower threshold can't
-    # apply more permissively than the floor (#764).
-    tag = await TagService(db).find_or_create("Lowthr", TagKind.general)
-    svc = AllowlistService(db)
-    img = await _make_image(db)
-    await svc.accept(img.id, tag.id)
-    await svc.update_threshold(tag.id, 0.30)
-    row = await db.get(TagAllowlist, tag.id)
-    assert abs(row.min_confidence - 0.70) < 1e-6

tests/test_ml_artist_retired.py

@@ -3,11 +3,6 @@ import pytest
 pytestmark = pytest.mark.integration
 
 
-def test_artist_not_surfaced():
-    from backend.app.services.ml.tagger import SURFACED_CATEGORIES
-    assert "artist" not in SURFACED_CATEGORIES
-
-
 def test_artist_not_head_eligible():
     # Tagging-v2: suggestions come from heads, and heads are only trained for
     # general/character concepts — so 'artist' (and any other kind) can't surface.

tests/test_ml_tagger.py

@@ -1,54 +0,0 @@
-"""Tagger unit tests. The ONNX model isn't available in CI (it's a 1GB
-download into /models), so these test the pure-logic surface:
-DEFAULT_STORE_FLOOR constant, SURFACED_CATEGORIES set, TagPrediction
-dataclass, and the load()-missing-file error path. Full inference is
-exercised by the local integration suite against a real /models volume.
-"""
-
-import pytest
-
-from backend.app.services.ml.tagger import (
-    DEFAULT_STORE_FLOOR,
-    SURFACED_CATEGORIES,
-    Tagger,
-    TagPrediction,
-    get_tagger,
-)
-
-
-def test_surfaced_categories():
-    # FC-2d-vii-c: 'artist' retired — artist identity is acquisition-
-    # derived (image_record.artist_id), never ML-inferred.
-    # 2026-06-01: 'copyright' retired — fandom serves as the franchise/
-    # copyright concept; operator doesn't use a separate copyright kind.
-    assert SURFACED_CATEGORIES == {"character", "general"}
-    assert "artist" not in SURFACED_CATEGORIES
-    assert "copyright" not in SURFACED_CATEGORIES
-
-
-def test_default_store_floor():
-    # Raised 0.05 → 0.70 (plan-task #764): the suggestion path filters at
-    # 0.70 and the centroid path covers lower-confidence preferred tags, so
-    # storing the sub-0.70 tail was redundant (100 GB of TOAST). The live
-    # value is DB-backed (ml_settings.tagger_store_floor); this is the default.
-    assert DEFAULT_STORE_FLOOR == 0.70
-
-
-def test_tag_prediction_dataclass():
-    p = TagPrediction(name="x", category="general", confidence=0.9)
-    assert p.name == "x"
-    assert p.category == "general"
-    assert p.confidence == 0.9
-
-
-def test_get_tagger_singleton():
-    assert get_tagger() is get_tagger()
-
-
-def test_load_raises_when_model_missing(tmp_path):
-    t = Tagger(model_dir=tmp_path / "nonexistent")
-    # Match the trailing "missing at <path>" rather than the specific
-    # filename, so a future model-version bump (camie-tagger-v3.onnx, etc.)
-    # doesn't bounce this test.
-    with pytest.raises(RuntimeError, match=r"\.onnx missing at "):
-        t.load()

tests/test_phash_dedup.py

@@ -11,7 +11,6 @@ from PIL import Image
 from sqlalchemy import func, select
 
 from backend.app.models import (
-    ImagePrediction,
     ImageProvenance,
     ImageRecord,
     ImportSettings,
@@ -119,11 +118,6 @@ def test_smaller_existing_is_superseded(importer, import_layout):
             image_record_id=eid, tag_id=tag.id, source="manual"
         )
     )
-    importer.session.add(
-        ImagePrediction(
-            image_record_id=eid, raw_name="x", category="general", score=0.9
-        )
-    )
     old.siglip_embedding = [0.0] * 1152
     old.integrity_status = "ok"
     importer.session.commit()
@@ -141,11 +135,6 @@ def test_smaller_existing_is_superseded(importer, import_layout):
     assert row.path != old_path
     assert row.phash is not None
     assert row.integrity_status == "unknown"
-    # #768: re-import clears the normalized predictions too
-    assert importer.session.execute(
-        select(func.count()).select_from(ImagePrediction)
-        .where(ImagePrediction.image_record_id == eid)
-    ).scalar_one() == 0
     assert row.siglip_embedding is None
     linked = importer.session.execute(
         select(image_tag.c.tag_id).where(

tests/test_tag_merge.py

@@ -2,7 +2,6 @@ import pytest
 from sqlalchemy import func, select
 
 from backend.app.models import Tag, TagKind, image_tag
-from backend.app.models.tag_allowlist import TagAllowlist
 from backend.app.services.tag_service import (
     MergeResult,
     TagMergeConflict,
@@ -110,18 +109,6 @@ async def test_will_alias_true_when_machine_sourced(db):
     assert ei.value.will_alias is True
 
 
-@pytest.mark.asyncio
-async def test_will_alias_true_when_allowlisted(db):
-    svc = TagService(db)
-    await svc.find_or_create("Canon2", TagKind.general)
-    source = await svc.find_or_create("Allowed", TagKind.general)
-    db.add(TagAllowlist(tag_id=source.id))
-    await db.flush()
-    with pytest.raises(TagMergeConflict) as ei:
-        await svc.rename(source.id, "Canon2")
-    assert ei.value.will_alias is True
-
-
 @pytest.mark.asyncio
 async def test_merge_rejects_self_merge(db):
     svc = TagService(db)
@@ -250,35 +237,6 @@ async def test_merge_dedups_suggestion_rejections(db):
     ).first() is None
 
 
-@pytest.mark.asyncio
-async def test_merge_allowlist_target_has_keeps_target_threshold(db):
-    svc = TagService(db)
-    a = await svc.find_or_create("SrcAL", TagKind.general)
-    b = await svc.find_or_create("TgtAL", TagKind.general)
-    db.add(TagAllowlist(tag_id=a.id, min_confidence=0.5))
-    db.add(TagAllowlist(tag_id=b.id, min_confidence=0.9))
-    await db.flush()
-    await svc.merge(a.id, b.id)
-    rows = (await db.execute(select(TagAllowlist))).scalars().all()
-    assert len(rows) == 1
-    assert rows[0].tag_id == b.id
-    assert rows[0].min_confidence == 0.9
-
-
-@pytest.mark.asyncio
-async def test_merge_allowlist_source_only_moves_to_target(db):
-    svc = TagService(db)
-    a = await svc.find_or_create("SrcAL2", TagKind.general)
-    b = await svc.find_or_create("TgtAL2", TagKind.general)
-    db.add(TagAllowlist(tag_id=a.id, min_confidence=0.42))
-    await db.flush()
-    await svc.merge(a.id, b.id)
-    rows = (await db.execute(select(TagAllowlist))).scalars().all()
-    assert len(rows) == 1
-    assert rows[0].tag_id == b.id
-    assert rows[0].min_confidence == 0.42
-
-
 @pytest.mark.asyncio
 async def test_merge_repoints_existing_aliases(db):
     from backend.app.models.tag_alias import TagAlias
@@ -372,7 +330,9 @@ async def test_alias_fallback_to_kind_when_no_predictions(db):
     svc = TagService(db)
     a = await svc.find_or_create("AllowNoPred", TagKind.character)
     b = await svc.find_or_create("CanonF", TagKind.character)
-    db.add(TagAllowlist(tag_id=a.id))
+    # Machine-known via a prior accept (source='ml_accepted') → kept as alias.
+    img = await _img(db)
+    await svc.add_to_image(img, a.id, source="ml_accepted")
     await db.flush()
     result = await svc.merge(a.id, b.id)
     assert result.alias_created is True

tests/test_tasks_ml.py

@@ -1,15 +1,12 @@
-"""tag_and_embed / backfill task tests. Models aren't in CI, so we test
+"""tag_and_embed (embed-only) / backfill task tests. The pure _is_video helper
-the pure helpers (_aggregate_video_predictions, _is_video) as unit tests, and
+is a unit test; the DB-touching backfill query is an integration test with
-the DB-touching backfill query as an integration test with monkeypatched
+monkeypatched dispatch."""
-inference.
-"""
 
 from pathlib import Path
 
 import pytest
 
-from backend.app.services.ml.tagger import TagPrediction
+from backend.app.tasks.ml import _is_video
-from backend.app.tasks.ml import _aggregate_video_predictions, _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