feat(ml): argmax grounding in score_image → suggestions carry the winning crop (#133 step 1)

score_image now keeps the ARGMAX beside the max-over-bag: which bag row won each
head. The region query also selects bbox/kind/detector_version, a parallel
bag_meta maps each row → its region (None for the whole-image vector), and every
hit gains grounding {bbox,kind,detector} (null when the global vector won). Threaded
through SuggestionService (new Suggestion.grounding field) → /api/.../suggestions
payload. This is the data the #1206 hover-overlay draws. CCIP-only hits ground null
for now (figure grounding = step 2). Tests: winning crop grounds the tag with its
bbox+kind; whole-image win → grounding None.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM

backend/app/services/ml/suggestions.py

@@ -43,6 +43,11 @@ class Suggestion:
     # the rejection is VISIBLE and REVERSIBLE in the rail (misclick recovery,
     # operator-asked 2026-06-27) instead of silently vanishing or re-suggesting.
     rejected: bool = False
+    # grounding = the crop region that produced this suggestion (#1206):
+    # {bbox:[x,y,w,h] normalized, kind, detector}. None when the whole-image
+    # vector won (not localized) or for a CCIP-only hit (figure grounding TBD).
+    # Lets the rail highlight the exact region on hover.
+    grounding: dict | None = None
 
 
 @dataclass
@@ -103,6 +108,7 @@ class SuggestionService:
         for h in hits:
             merged[(h["category"], h["tag_id"])] = {
                 "name": h["name"], "score": h["score"], "source": "head",
+                "grounding": h.get("grounding"),
             }
         for c in ccip_hits:
             key = ("character", c["tag_id"])
@@ -128,6 +134,7 @@ class SuggestionService:
                     source=m["source"],
                     creates_new_tag=False,
                     rejected=tag_id in rejected,
+                    grounding=m.get("grounding"),
                 )
             )
         for cat in result.by_category: