backend

app.py

@@ -216,10 +216,10 @@ def handle_video_upload(video):
             try:
                 print("🔧 Calling backend API for video upload...")
                 
-                # Call the unified API with upload_video function type
+                # Call the unified API with upload_video function type - fix: use handle_file wrapper
                 result = backend_client.predict(
                     "upload_video",  # function_type
-                    handle_file(video),           # video file
+                    handle_file(video),           # video file - wrapped with handle_file
                     "",              # original_image_state (not used for upload)
                     [],              # selected_points (not used for upload)
                     "positive_point", # point_type (not used for upload)
@@ -242,6 +242,11 @@ def handle_video_upload(video):
                     display_image = result.get("display_image", None)
                     selected_points = result.get("selected_points", [])
                     
+                    # Fix: Convert display_image from list back to numpy array if needed
+                    if isinstance(display_image, list):
+                        display_image = np.array(display_image, dtype=np.uint8)
+                        print(f"🔧 Converted display_image from list to numpy array: {display_image.shape}")
+                    
                     # Get video settings based on video name
                     video_name = get_video_name(video)
                     grid_size_val, vo_points_val, fps_val = get_video_settings(video_name)
@@ -337,6 +342,12 @@ def select_point(original_img: str, sel_pix: list, point_type: str, evt: gr.Sele
                 if isinstance(result, dict) and result.get("success"):
                     display_image = result.get("display_image", None)
                     new_sel_pix = result.get("selected_points", sel_pix)
+                    
+                    # Fix: Convert display_image from list back to numpy array if needed
+                    if isinstance(display_image, list):
+                        display_image = np.array(display_image, dtype=np.uint8)
+                        print(f"🔧 Converted display_image from list to numpy array: {display_image.shape}")
+                    
                     return display_image, new_sel_pix
                 else:
                     print("Backend processing failed, using local fallback")
@@ -384,8 +395,8 @@ def select_point(original_img: str, sel_pix: list, point_type: str, evt: gr.Sele
                 cv2.circle(display_image, (x, y), 8, color, -1)
                 cv2.circle(display_image, (x, y), 12, (255, 255, 255), 2)
                 
-                # Add point to selected points list
-                new_sel_pix = sel_pix.copy()
+                # Add point to selected points list - fix logic to match local version
+                new_sel_pix = sel_pix.copy() if sel_pix else []
                 new_sel_pix.append([x, y, point_type])
                 
                 return display_image, new_sel_pix
@@ -429,6 +440,12 @@ def reset_points(original_img: str, sel_pix):
                 if isinstance(result, dict) and result.get("success"):
                     display_image = result.get("display_image", None)
                     new_sel_pix = result.get("selected_points", [])
+                    
+                    # Fix: Convert display_image from list back to numpy array if needed
+                    if isinstance(display_image, list):
+                        display_image = np.array(display_image, dtype=np.uint8)
+                        print(f"🔧 Converted display_image from list to numpy array: {display_image.shape}")
+                    
                     return display_image, new_sel_pix
                 else:
                     print("Backend processing failed, using local fallback")