fix3: torch.load

app.py

@@ -17,6 +17,10 @@
 #               ultralytics   (for YOLOv8m-seg inference)
 # ─────────────────────────────────────────────────────────────────────────────
 
+# ── Force PyTorch weights_only=False globally (must be before any imports) ───
+import os as _os
+_os.environ["TORCH_FORCE_WEIGHTS_ONLY_LOAD"] = "0"
+
 # ── Python 3.13 audioop shim (removed from stdlib, needed by pydub/gradio) ───
 import sys, types
 if "audioop" not in sys.modules:
@@ -811,9 +815,9 @@ def run_yolo_seg(frame_rgb: np.ndarray, seg_conf: float) -> np.ndarray:
         n_written += int(binary.sum())
 
     detected_cls = [_YOLO_CLASS_NAMES[min(int(c) + 1, 25)] for c in cls_ids]
-    print(f"Segmentation Detections: {len(cls_ids)}  |  classes: {detected_cls}  |  "
+    print(f"[YOLO] Detections: {len(cls_ids)}  |  classes: {detected_cls}  |  "
           f"vessel px: {n_written} ({100 * n_written / (h * w):.1f}%)")
-    print(f"seg_map unique labels: {np.unique(seg_map)}")
+    print(f"[YOLO] seg_map unique labels: {np.unique(seg_map)}")
     return seg_map
 
 # ─────────────────────────────────────────────────────────────────────────────
@@ -1350,7 +1354,7 @@ def analyse(video_path, sten_conf, seg_conf, px_per_mm_override, progress=gr.Pro
             det["overlap"]=float(binary_mask[y1:y2,x1:x2].sum())/max(area,1)
 
         # Step 3b — YOLOv8m-seg 26-class segmentation
-        progress(0.65, desc="Running seg model")
+        progress(0.65, desc="Running YOLOv8m-seg…")
         seg_map = run_yolo_seg(frame_rgb, seg_conf)
         seg_overlay = render_nnunet_overlay(frame_rgb, seg_map)
 
@@ -1510,8 +1514,8 @@ with gr.Blocks(css=CSS, title="Angio AI") as demo:
                     )
                     seg_conf = gr.Slider(
                         minimum=0.05, maximum=0.95, value=0.25, step=0.05,
-                        label="Segmentation confidence threshold",
-                        info="Detections below this confidence are discarded by NMS",
+                        label="Segmentation confidence threshold  (YOLOv8m-seg)",
+                        info="Detections below this confidence are discarded by YOLO NMS",
                     )
                     px_per_mm = gr.Slider(
                         minimum=2.0, maximum=6.0, value=3.75, step=0.25,
@@ -1528,7 +1532,7 @@ with gr.Blocks(css=CSS, title="Angio AI") as demo:
   <ol style="font-size:13px;color:#1a2533;line-height:1.9;padding-left:16px">
     <li>Best frame extracted from video</li>
     <li>Stenosis detection — Mask2Former</li>
-    <li>Coronary segmentation — 26-classes</li>
+    <li>Coronary segmentation — 26-class</li>
     <li>Binary vessel mask — ResUNet</li>
     <li>FFR estimation — QFR v4</li>
     <li>SYNTAX score computation</li>