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>

tests/_prediction_helpers.py

@@ -0,0 +1,21 @@
+"""#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()