feat(ml): Settings → Tagging 'Crop proposers' card (#134 step 3)

Exposes the detector config (per-proposer enable + weights + confidence, caps,
dedupe IoU) in Settings → Tagging, backed by MLSettings via /api/ml/settings.
ml_admin adds the detector fields to _EDITABLE + GET payload + validation (conf
0..1, caps >=1, IoU 0..1). New CropProposersCard.vue (mirrors HeadsCard) with
working defaults pre-filled, per-field live-save (no restart — the agent picks
changes up on its next lease), weights-format help, switch-revert on error.
Closes milestone #134: all three proposers are on out-of-the-box and tunable in
the UI. Test: detector defaults GET + patch round-trip + range validation.

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

backend/app/api/ml_admin.py

@@ -8,6 +8,26 @@ from ..models import MLSettings
 ml_admin_bp = Blueprint("ml_admin", __name__, url_prefix="/api/ml")
 
 
+# Crop-proposer / detector config (#134). Announced to the GPU agent in the lease
+# → tunable here with no restart. weights = ultralytics name | URL | hf_repo::file
+# (empty, or enabled off, skips that proposer).
+_DETECTOR_FIELDS = (
+    "detector_person_enabled",
+    "detector_person_weights",
+    "detector_person_conf",
+    "detector_anatomy_enabled",
+    "detector_anatomy_weights",
+    "detector_anatomy_conf",
+    "detector_panel_enabled",
+    "detector_panel_weights",
+    "detector_panel_conf",
+    "detector_max_figures",
+    "detector_max_components",
+    "detector_max_panels",
+    "detector_max_regions",
+    "detector_dedupe_iou",
+)
+
 _EDITABLE = (
     "cpu_embed_enabled",
     "video_frame_interval_seconds",
@@ -21,6 +41,7 @@ _EDITABLE = (
     "ccip_auto_apply_threshold",
     "embedder_model_name",
     "embedder_model_version",
+    *_DETECTOR_FIELDS,
 )
 
 
@@ -76,6 +97,7 @@ async def get_settings():
             "ccip_auto_apply_enabled": s.ccip_auto_apply_enabled,
             "ccip_auto_apply_threshold": s.ccip_auto_apply_threshold,
             "embedder_model_name": s.embedder_model_name,
+            **{f: getattr(s, f) for f in _DETECTOR_FIELDS},
         }
     )
 
@@ -133,6 +155,19 @@ def _validate(p: dict) -> str | None:
     for key in ("embedder_model_name", "embedder_model_version"):
         if not str(p[key]).strip():
             return f"{key} must not be empty"
+    # Crop proposers (#134). Weights may be empty (that proposer is just off);
+    # confidences are probabilities; caps are positive counts; IoU is [0,1].
+    for key in ("detector_person_conf", "detector_anatomy_conf", "detector_panel_conf"):
+        if not (0.0 <= float(p[key]) <= 1.0):
+            return f"{key} must be between 0 and 1"
+    for key in (
+        "detector_max_figures", "detector_max_components",
+        "detector_max_panels", "detector_max_regions",
+    ):
+        if int(p[key]) < 1:
+            return f"{key} must be >= 1"
+    if not (0.0 <= float(p["detector_dedupe_iou"]) <= 1.0):
+        return "detector_dedupe_iou must be between 0 and 1"
     return None