typo

app.py

@@ -120,7 +120,11 @@ def reset(
     )
 
 
-def clear_points(session_input_points, session_input_labels, request: gr.Request,):
+def clear_points(
+    session_input_points,
+    session_input_labels,
+    request: gr.Request,
+):
     session_id = request.session_id
     predictor.to("cpu")
     session_input_points = []
@@ -237,9 +241,7 @@ def segment_with_points(
         print(f"TRACKING INPUT LABEL: {session_input_labels}")
 
         # Open the image and get its dimensions
-        transparent_background Image.fromarray(session_first_frame).convert(
-            "RGBA"
-        )
+        transparent_background = Image.fromarray(session_first_frame).convert("RGBA")
         w, h = transparent_background.size
 
         # Define the circle radius as a fraction of the smaller dimension
@@ -277,7 +279,12 @@ def segment_with_points(
         first_frame_output = Image.alpha_composite(transparent_background, mask_image)
 
         torch.cuda.empty_cache()
-        return selected_point_map, first_frame_output, session_input_points, session_input_labels
+        return (
+            selected_point_map,
+            first_frame_output,
+            session_input_points,
+            session_input_labels,
+        )
 
 
 def show_mask(mask, obj_id=None, random_color=False, convert_to_image=True):
@@ -308,7 +315,7 @@ def propagate_to_all(
         torch.backends.cudnn.allow_tf32 = True
     with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
         if (
-            len (session_input_points) == 0
+            len(session_input_points) == 0
             or video_in is None
             or global_inference_states[session_id] is None
         ):