feat(suggestions): tag-input dropdown searches the full prediction set

The typed dropdown sourced the threshold-filtered panel list (>= 0.70 general),
so low-confidence actions/features the model DID predict never appeared — forcing
hand-typed custom tags instead of accepting the model's canonical formatting.

Add a threshold override: SuggestionService.for_image(threshold_override=) and
GET /images/<id>/suggestions?min=<f> surface EVERY stored prediction (down to the
0.05 store floor), alias-resolved and normalized, still excluding applied/rejected
and unsurfaced categories. The suggestions store gains allByCategory + loadAll
(min=0); the dropdown searches that full set (cap 20), while the Suggestions panel
stays curated at the configured threshold. Accept/dismiss drop from both lists.

Operator-asked 2026-06-09. Test: a 0.30 general prediction is hidden by default
but surfaced with threshold_override=0.0; unsurfaced categories still excluded.

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

backend/app/api/suggestions.py

@@ -11,8 +11,21 @@ suggestions_bp = Blueprint("suggestions", __name__, url_prefix="/api")
 
 @suggestions_bp.route("/images/<int:image_id>/suggestions", methods=["GET"])
 async def get_suggestions(image_id: int):
+    # ?min=<float> overrides the configured per-category thresholds so the typed
+    # tag-input dropdown can surface EVERY stored prediction (min=0), including
+    # low-confidence actions/features, in canonical formatting. Omitted → the
+    # curated above-threshold list the Suggestions panel uses.
+    override = None
+    raw_min = request.args.get("min")
+    if raw_min is not None:
+        try:
+            override = min(1.0, max(0.0, float(raw_min)))
+        except ValueError:
+            return jsonify({"error": "min must be a float in [0,1]"}), 400
     async with get_session() as session:
-        sl = await SuggestionService(session).for_image(image_id)
+        sl = await SuggestionService(session).for_image(
+            image_id, threshold_override=override
+        )
     return jsonify(
         {
             "by_category": {

backend/app/services/ml/suggestions.py

@@ -48,16 +48,33 @@ class SuggestionService:
             await self.session.execute(select(MLSettings).where(MLSettings.id == 1))
         ).scalar_one()
 
-    def _threshold_for(self, s: MLSettings, category: str) -> float:
+    def _threshold_for(
+        self, s: MLSettings, category: str, override: float | None = None,
+    ) -> float:
         # 'artist' (FC-2d-vii-c) and 'copyright' (2026-06-01) retired;
         # both fall through to the 1.01 "never surfaces" default like any
         # unsurfaced category.
+        # override (the typed-dropdown "show everything the model saw" mode)
+        # applies to the surfaced categories only — unsurfaced ones are already
+        # skipped before the threshold check, so they can't leak in.
+        if override is not None:
+            return override
         return {
             "character": s.suggestion_threshold_character,
             "general": s.suggestion_threshold_general,
         }.get(category, 1.01)
 
-    async def for_image(self, image_id: int) -> SuggestionList:
+    async def for_image(
+        self, image_id: int, *, threshold_override: float | None = None,
+    ) -> SuggestionList:
+        """Ranked suggestions for one image.
+
+        threshold_override surfaces EVERY stored tagger prediction (down to the
+        ingest STORE_FLOOR) regardless of the configured per-category suggestion
+        thresholds — backs the tag-input dropdown's "search all of the model's
+        predictions, including low-confidence ones, in the canonical formatting"
+        mode (operator-asked 2026-06-09). The Suggestions panel still calls with
+        no override so it stays the curated above-threshold list."""
         img = await self.session.get(ImageRecord, image_id)
         if img is None:
             return SuggestionList()
@@ -96,7 +113,7 @@ class SuggestionService:
             if category not in SURFACED_CATEGORIES:
                 continue
             conf = float(p.get("confidence", 0.0))
-            if conf < self._threshold_for(settings, category):
+            if conf < self._threshold_for(settings, category, threshold_override):
                 continue
             display = normalize_tag_name(name)
             if display is None: