vggt

app.py

@@ -117,29 +117,22 @@ def handle_video_upload(video):
             try:
                 print("🔧 Calling backend API for video upload...")
                 
-                # For gr.Blocks, we use predict method
+                # Use call_function method
                 if 0 in backend_api.fns:
-                    print("🔧 Using backend Blocks predict method")
+                    print("🔧 Using backend call_function method")
+                    result = backend_api.call_function(0, [video, [], 50, 756, 3])
                     
-                    # Check if predict is callable
-                    if callable(backend_api.predict):
-                        result = backend_api.predict(0, [video, [], 50, 756, 3])
-                        
-                        print(f"✅ Backend video upload API call successful!")
-                        print(f"🔧 Result type: {type(result)}")
-                        
-                        # Parse the result - expect a dict with success status
-                        if isinstance(result, dict) and result.get("success"):
-                            # For now, just extract the first frame locally
-                            display_image = extract_first_frame(video)
-                            original_image_state = json.dumps({"video_path": video, "frame": "backend_processing"})
-                            return original_image_state, display_image, [], 50, 756, 3
-                        else:
-                            print("Backend processing failed, using local fallback")
-                            # Fallback to local processing
-                            pass
+                    print(f"✅ Backend video upload API call successful!")
+                    print(f"🔧 Result type: {type(result)}")
+                    
+                    # Parse the result - expect a dict with success status
+                    if isinstance(result, dict) and result.get("success"):
+                        # For now, just extract the first frame locally
+                        display_image = extract_first_frame(video)
+                        original_image_state = json.dumps({"video_path": video, "frame": "backend_processing"})
+                        return original_image_state, display_image, [], 50, 756, 3
                     else:
-                        print("❌ Backend predict is not callable")
+                        print("Backend processing failed, using local fallback")
                         # Fallback to local processing
                         pass
                 else:
@@ -181,10 +174,10 @@ def select_point(original_img: str, sel_pix: list, point_type: str, evt: gr.Sele
             try:
                 print(f"🔧 Calling backend select point API: x={evt.index[0]}, y={evt.index[1]}, type={point_type}")
                 
-                # For gr.Blocks, we use predict method
+                # Use call_function method
                 if 1 in backend_api.fns:
-                    print("🔧 Using backend Blocks predict method for point selection")
-                    result = backend_api.predict(1, [original_img, sel_pix, point_type, evt.index[0], evt.index[1]])
+                    print("🔧 Using backend call_function method for point selection")
+                    result = backend_api.call_function(1, [original_img, sel_pix, point_type, evt.index[0], evt.index[1]])
                     
                     print(f"✅ Backend select point API call successful!")
                     print(f"🔧 Result type: {type(result)}")
@@ -267,10 +260,10 @@ def reset_points(original_img: str, sel_pix):
             try:
                 print("🔧 Calling backend reset points API")
                 
-                # For gr.Blocks, we use predict method
+                # Use call_function method
                 if 2 in backend_api.fns:
-                    print("🔧 Using backend Blocks predict method for reset")
-                    result = backend_api.predict(2, [original_img, sel_pix])
+                    print("🔧 Using backend call_function method for reset")
+                    result = backend_api.call_function(2, [original_img, sel_pix])
                     
                     print(f"✅ Backend reset points API call successful!")
                     print(f"🔧 Result type: {type(result)}")
@@ -327,43 +320,25 @@ def launch_viz(grid_size, vo_points, fps, original_image_state):
                 print(f"🔧 Original image state type: {type(original_image_state)}")
                 print(f"🔧 Original image state preview: {str(original_image_state)[:100]}...")
                 
-                # Debug: Check what predict actually is
-                print(f"🔧 Backend API predict type: {type(backend_api.predict)}")
-                print(f"🔧 Backend API predict: {backend_api.predict}")
-                
-                # For gr.Blocks, we use predict method
+                # Use call_function method
                 if 0 in backend_api.fns:
-                    print("🔧 Using backend Blocks predict method")
-                    # Check if predict is callable
-                    if callable(backend_api.predict):
-                        # For now, we'll use empty points since we're in local mode
-                        result = backend_api.predict(0, [None, [], grid_size, vo_points, fps])
-                        
-                        print(f"✅ Backend API call successful!")
-                        print(f"🔧 Result type: {type(result)}")
-                        print(f"🔧 Result: {result}")
-                        
-                        # Parse the result
-                        if isinstance(result, dict) and result.get("success"):
-                            viz_html = result.get("viz_html_path", "")
-                            track_video_path = result.get("track_video_path", "")
-                            return viz_html, track_video_path
-                        else:
-                            print("Backend processing failed, showing error message")
-                            # Fallback to error message
-                            pass
+                    print("🔧 Using backend call_function method")
+                    # For now, we'll use empty points since we're in local mode
+                    result = backend_api.call_function(0, [None, [], grid_size, vo_points, fps])
+                    
+                    print(f"✅ Backend API call successful!")
+                    print(f"🔧 Result type: {type(result)}")
+                    print(f"🔧 Result: {result}")
+                    
+                    # Parse the result
+                    if isinstance(result, dict) and result.get("success"):
+                        viz_html = result.get("viz_html_path", "")
+                        track_video_path = result.get("track_video_path", "")
+                        return viz_html, track_video_path
                     else:
-                        print("❌ Backend predict is not callable")
-                        print(f"🔧 Predict type: {type(backend_api.predict)}")
-                        # Try alternative approach
-                        if hasattr(backend_api, 'call_function'):
-                            print("🔧 Trying call_function method")
-                            result = backend_api.call_function(0, [None, [], grid_size, vo_points, fps])
-                            # Handle result...
-                        else:
-                            print("❌ No callable method found")
-                            # Fallback to error message
-                            pass
+                        print("Backend processing failed, showing error message")
+                        # Fallback to error message
+                        pass
                 else:
                     print("Backend function 0 not found")
                     # Fallback to error message
@@ -395,7 +370,6 @@ def launch_viz(grid_size, vo_points, fps, original_image_state):
                 <p style='color: #666; font-size: 12px; margin: 0;'>Backend URL: {BACKEND_SPACE_URL}</p>
                 <p style='color: #666; font-size: 12px; margin: 0;'>Backend Type: {type(backend_api) if backend_api else 'None'}</p>
                 <p style='color: #666; font-size: 12px; margin: 0;'>Available Functions: {list(backend_api.fns.keys()) if backend_api and hasattr(backend_api, 'fns') else 'None'}</p>
-                <p style='color: #666; font-size: 12px; margin: 0;'>Predict Type: {type(backend_api.predict) if backend_api and hasattr(backend_api, 'predict') else 'None'}</p>
             </div>
             <p style='color: #2d3436; font-weight: bold; margin-top: 15px;'>
                 Current Status: Backend unavailable - Running in limited mode