feat(suggestions): heads are the suggestion source — Camie + centroid removed (#114 C)

The rail's Suggestions now come from the trained per-concept heads. SuggestionService.for_image scores the image's frozen SigLIP embedding against
every head (heads.score_image) and surfaces concepts above each head's own
suggest threshold; the typed-dropdown's min=0 "show everything" mode maps to a
flat floor so any head-scored concept can still be picked. Already-applied tags
drop; rejected tags stay flagged + reversible (unchanged).

REMOVED from the suggestion path (rule 22, no fallback): the Camie
ImagePrediction candidate/alias/merge pipeline and the per-tag centroid
augmentation, plus the now-dead SuggestionService internals (_load_predictions,
_threshold_for, _settings, self.aliases, self.centroids). Head suggestions are
always canonical tags, so raw_name/via_alias are null/false and the rail's
alias kebab is inert by data (its removal + the Camie ingest-tagger rip are the
flagged follow-up). for_selection (bulk consensus) now aggregates head
suggestions unchanged.

Tests rewritten to the head path: test_ml_suggestions (surfaces/applied/
rejected-reversible/override/no-embedding/no-heads), test_suggestions_bulk
(consensus), test_api_suggestions (get + dropped the Camie-alias roundtrip),
and test_ml_artist_retired (artist not head-eligible via _HEAD_KINDS).

DEPLOY NOTE: after this lands, the rail is empty until you run Train heads
(Settings → Tagging → Concept heads) — deploy, train, then the rail populates.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa

backend/app/services/ml/heads.py

@@ -287,10 +287,14 @@ async def _current_heads(session: AsyncSession, embedding_version: str):
     return loaded
 
 
-async def score_image(session: AsyncSession, image_id: int) -> list[dict]:
+async def score_image(
+    session: AsyncSession, image_id: int, threshold_override: float | None = None,
+) -> list[dict]:
     """Suggestions for one image from the trained heads: [{tag_id, name,
-    category, score}], score >= each head's suggest_threshold, ranked. Empty if
-    the image has no embedding or no heads exist yet."""
+    category, score}], ranked. A concept surfaces when its score clears the
+    head's own suggest_threshold — or, when threshold_override is given (the
+    typed-dropdown "show everything" mode), that flat floor instead (0 → every
+    head). Empty if the image has no embedding or no heads exist yet."""
     import numpy as np
 
     img = await session.get(ImageRecord, image_id)
@@ -307,7 +311,8 @@ async def score_image(session: AsyncSession, image_id: int) -> list[dict]:
     probs = 1.0 / (1.0 + np.exp(-z))
     out = []
     for i, p in enumerate(probs):
-        if p >= heads["thr"][i]:
+        cut = threshold_override if threshold_override is not None else heads["thr"][i]
+        if p >= cut:
             m = heads["meta"][i]
             out.append({
                 "tag_id": m["tag_id"],