deploy: sync code from GH commit d0a11cd

src/agrianalyze/api/app.py

@@ -196,6 +196,40 @@ def _is_plant_image(image_bgr: np.ndarray) -> dict:
     # - Farmer holding a leaf (hand + plant)
     # - Plant in an office/lab setting
     # - Field photo with people in background
+    # ══════════════════════════════════════════════════════════════
+    # PRIORITY 0 — HUMAN VETO. Always wins over any vegetation signal.
+    # Skin pixels overlap the "brown vegetation" hue range, so a selfie
+    # would otherwise pass D1b. We reject FIRST if a face is clearly
+    # present OR skin dominates without a real green leaf blob.
+    # ══════════════════════════════════════════════════════════════
+    if face_count > 0 and face_area_pct > 0.01 and not has_plant_region:
+        return {
+            "is_plant": False,
+            "reason": (
+                f"Human face detected ({face_count} face{'s' if face_count > 1 else ''}, "
+                f"covering {face_area_pct:.0%} of the image) without a visible crop leaf. "
+                "Please upload a close-up photo of a crop leaf, not a person."
+            ),
+            "face_count": face_count, "face_area_pct": face_area_pct,
+            "skin_ratio": skin_ratio, "green_ratio": green_ratio,
+            "brown_ratio": brown_ratio, "achromatic_ratio": achromatic_ratio,
+            "largest_green_blob_ratio": largest_green_blob_ratio,
+            "edge_density": edge_density,
+        }
+    if skin_ratio > 0.20 and largest_green_blob_ratio < 0.02:
+        return {
+            "is_plant": False,
+            "reason": (
+                f"Person/skin detected ({skin_ratio:.0%} skin pixels) without a crop leaf region. "
+                "Please upload a close-up photo of a crop leaf."
+            ),
+            "face_count": face_count, "face_area_pct": face_area_pct,
+            "skin_ratio": skin_ratio, "green_ratio": green_ratio,
+            "brown_ratio": brown_ratio, "achromatic_ratio": achromatic_ratio,
+            "largest_green_blob_ratio": largest_green_blob_ratio,
+            "edge_density": edge_density,
+        }
+
     if has_plant_region:
         is_plant = True
         reason = ""

src/agrianalyze/core/detector.py

@@ -92,8 +92,18 @@ def predict_with_uncertainty(
                 if not results or results[0].probs is None:
                     continue
                 probs = results[0].probs
-                top_idx = probs.top1
-                top_conf = float(probs.top1conf)
+                # In MC-Dropout (train mode) ultralytics may return un-normalized
+                # logits via .top1conf. Re-normalize via softmax over .data so
+                # confidences stay in [0, 1].
+                raw = probs.data
+                if hasattr(raw, "float"):
+                    raw = raw.float()
+                # softmax across the class dimension
+                soft = torch.softmax(raw, dim=-1)
+                top_idx = int(torch.argmax(soft).item())
+                top_conf = float(soft[top_idx].item())
+                # Hard clamp as a final safety net
+                top_conf = max(0.0, min(1.0, top_conf))
                 class_key = names[top_idx]
                 confidences.append(top_conf)
                 predictions.append(class_key)