feat(ml): image_prediction table + backfill + dual-write (#768 step 1)

Normalize tagger predictions out of the image_record.tagger_predictions JSON
blob into a queryable per-prediction table. Step 1 of the cutover (expand):
additive + low-risk — reads still use the JSON, this just adds the table and
keeps it populated.

- ImagePrediction(image_record_id, raw_name, category, score) — stores the
  RAW tagger vocab name (not tag_id) so read-time alias→canonical resolution
  is unchanged. Indexed for per-image reads + by (raw_name, score).
- Migration 0045: create table + set-based backfill from the JSON via
  json_each (fast post-#764-prune). The old column stays (vestigial) and is
  dropped in a later follow-up — DROP needs an ACCESS EXCLUSIVE lock on the
  hot image_record table, so it waits for a quiesced-worker window.
- tag_and_embed dual-writes the rows (delete-then-insert, idempotent);
  tagger_store_floor already applied in infer().

Next: switch suggestion + allowlist reads to the table, then drop the JSON
write. Plan-task #768.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

backend/app/models/__init__.py

@@ -7,6 +7,7 @@ from .backup_run import BackupRun
 from .base import Base
 from .credential import Credential
 from .download_event import DownloadEvent
+from .image_prediction import ImagePrediction
 from .image_provenance import ImageProvenance
 from .image_record import ImageRecord
 from .import_batch import ImportBatch
@@ -45,6 +46,7 @@ __all__ = [
     "SeriesPage",
     "SeriesSuggestion",
     "ImageRecord",
+    "ImagePrediction",
     "ImageProvenance",
     "Tag",
     "TagKind",

backend/app/models/image_prediction.py

@@ -0,0 +1,37 @@
+"""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/tasks/ml.py

@@ -10,11 +10,11 @@ import logging
 from pathlib import Path
 
 from celery.exceptions import SoftTimeLimitExceeded
-from sqlalchemy import select
+from sqlalchemy import delete, select
 from sqlalchemy.exc import DBAPIError, OperationalError
 
 from ..celery_app import celery
-from ..models import ImageRecord, MLSettings
+from ..models import ImagePrediction, ImageRecord, MLSettings
 from ._sync_engine import sync_session_factory as _sync_session_factory
 
 log = logging.getLogger(__name__)
@@ -162,6 +162,23 @@ def tag_and_embed(self, image_id: int) -> dict:
             record.siglip_embedding = embedding.tolist()
             record.siglip_model_version = settings.embedder_model_version
             session.add(record)
+            # Write the normalized image_prediction rows (#768). Delete-then-
+            # insert keeps a re-tag idempotent. tagger_store_floor was already
+            # applied in tagger.infer, so preds is the >=floor set. (Transitional
+            # dual-write alongside the JSON column until the read cutover lands.)
+            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(