feat(ml): read suggestions + allowlist from image_prediction (#768 step 2)

Switch every prediction READER off the JSON column onto the normalized
image_prediction table. Parity by construction: each reader loads the same
{raw_name: {category, confidence}} dict it consumed before (via small
_load_predictions helpers), so all downstream threshold/alias/merge/consensus
logic is byte-identical — only the data source changed.

- suggestions.SuggestionService.for_image (and for_selection via it)
- ml.apply_allowlist_tags (iterates images that have prediction rows)
- importer re-import reset deletes the image's prediction rows
The tagger_predictions JSON column is still dual-written (step 1) so it stays
valid during transition; the backfill task's NULL check still works. Removing
the JSON write + DROP column + retiring the #764 prune is the cleanup
follow-up (needs a quiesced-worker window for the DROP lock).

Tests: shared tests/_prediction_helpers.seed_predictions seeds the table;
read-path tests (suggestions, bulk consensus, allowlist apply, API) seed there
instead of ImageRecord.tagger_predictions.

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

backend/app/services/ml/suggestions.py

@@ -8,6 +8,7 @@ from sqlalchemy import func, select
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from ...models import (
+    ImagePrediction,
     ImageRecord,
     MLSettings,
     Tag,
@@ -48,6 +49,25 @@ class SuggestionService:
             await self.session.execute(select(MLSettings).where(MLSettings.id == 1))
         ).scalar_one()
 
+    async def _load_predictions(self, image_id: int) -> dict:
+        """Predictions for one image from the normalized image_prediction
+        table (#768), in the {raw_name: {category, confidence}} shape the rest
+        of this service consumed from the old JSON column — so all downstream
+        threshold/alias/merge logic is unchanged."""
+        rows = (
+            await self.session.execute(
+                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 _threshold_for(
         self, s: MLSettings, category: str, override: float | None = None,
     ) -> float:
@@ -80,7 +100,7 @@ class SuggestionService:
             return SuggestionList()
 
         settings = await self._settings()
-        predictions: dict = img.tagger_predictions or {}
+        predictions: dict = await self._load_predictions(image_id)
 
         applied = set(
             (