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/importer.py

@@ -1090,6 +1090,16 @@ 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()