try to fix get_point

app.py

@@ -17,38 +17,32 @@ def preprocess_image(image):
 def get_point(point_type, tracking_points, trackings_input_label, first_frame_path, evt: gr.SelectData):
     print(f"You selected {evt.value} at {evt.index} from {evt.target}")
 
-    tracking_points.value.append(evt.index)
-    print(f"TRACKING POINT: {tracking_points.value}")
+    tracking_points.append(evt.index)
+    print(f"TRACKING POINT: {tracking_points}")
 
     if point_type == "include":
-        trackings_input_label.value.append(1)
+        trackings_input_label.append(1)
     elif point_type == "exclude":
-        trackings_input_label.value.append(0)
-    print(f"TRACKING INPUT LABEL: {trackings_input_label.value}")
+        trackings_input_label.append(0)
+    print(f"TRACKING INPUT LABEL: {trackings_input_label}")
     
-    # Open the image and get its dimensions
     transparent_background = Image.open(first_frame_path).convert('RGBA')
     w, h = transparent_background.size
+    radius = int(0.02 * min(w, h))
     
-    # Define the circle radius as a fraction of the smaller dimension
-    fraction = 0.02  # You can adjust this value as needed
-    radius = int(fraction * min(w, h))
-    
-    # Create a transparent layer to draw on
     transparent_layer = np.zeros((h, w, 4), dtype=np.uint8)
     
-    for index, track in enumerate(tracking_points.value):
-        if trackings_input_label.value[index] == 1:
+    for index, track in enumerate(tracking_points):
+        if trackings_input_label[index] == 1:
             cv2.circle(transparent_layer, track, radius, (0, 255, 0, 255), -1)
         else:
             cv2.circle(transparent_layer, track, radius, (255, 0, 0, 255), -1)
 
-    # Convert the transparent layer back to an image
     transparent_layer = Image.fromarray(transparent_layer, 'RGBA')
     selected_point_map = Image.alpha_composite(transparent_background, transparent_layer)
     
     return tracking_points, trackings_input_label, selected_point_map
-    
+
 # use bfloat16 for the entire notebook
 torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()