Debug

app.py

@@ -448,17 +448,34 @@ def generate_auto_masks(image, request_json):
             print("    - Thresholds are still too strict")
             print("    - Image size is too small or too large")
         
+        # Optionally limit number of masks returned to keep JSON payload reasonable
+        max_masks = int(params.get("max_masks", 10))
+        if max_masks > 0 and len(masks) > max_masks:
+            # Sort by predicted IoU (descending) and keep top-K
+            print(f"Limiting masks from {len(masks)} to top {max_masks} by predicted_iou")
+            masks = sorted(
+                masks,
+                key=lambda m: float(m.get("predicted_iou", 0.0)),
+                reverse=True,
+            )[:max_masks]
+
+        print(f"Preparing {len(masks)} masks to return to client...")
+
         # Convert masks to JSON-serializable format
         masks_output = []
         for m in masks:
             mask_data = {
-                'segmentation': m['segmentation'].astype(np.uint8).tolist(),
-                'area': int(m['area']),
-                'bbox': [int(x) for x in m['bbox']],  # [x, y, width, height]
-                'predicted_iou': float(m['predicted_iou']),
-                'point_coords': [[int(p[0]), int(p[1])] for p in m['point_coords']] if m['point_coords'] is not None else [],
-                'stability_score': float(m['stability_score']),
-                'crop_box': [int(x) for x in m['crop_box']]  # [x, y, width, height]
+                "segmentation": m["segmentation"].astype(np.uint8).tolist(),
+                "area": int(m["area"]),
+                "bbox": [int(x) for x in m["bbox"]],  # [x, y, width, height]
+                "predicted_iou": float(m["predicted_iou"]),
+                "point_coords": [
+                    [int(p[0]), int(p[1])] for p in m["point_coords"]
+                ]
+                if m["point_coords"] is not None
+                else [],
+                "stability_score": float(m["stability_score"]),
+                "crop_box": [int(x) for x in m["crop_box"]],  # [x, y, width, height]
             }
             masks_output.append(mask_data)