Update app.py

app.py

@@ -313,6 +313,7 @@ def propagate_to_all(video_in, checkpoint, stored_inference_state, stored_frame_
         vis_frame_stride = 15
     elif vis_frame_type == "render":
         vis_frame_stride = 1
+    
     plt.close("all")
     for out_frame_idx in range(0, len(frame_names), vis_frame_stride):
         plt.figure(figsize=(6, 4))
@@ -332,7 +333,7 @@ def propagate_to_all(video_in, checkpoint, stored_inference_state, stored_frame_
         jpeg_images.append(output_filename)
 
     torch.cuda.empty_cache()
-    
+    print(f"JPEG_IMAGES: {jpeg_images}")
 
     if vis_frame_type == "check":
         return gr.update(value=jpeg_images), gr.update(value=None), gr.update(choices=jpeg_images)
@@ -403,7 +404,7 @@ with gr.Blocks() as demo:
                     video_in = gr.Video(label="Video IN")
             
             with gr.Column():
-                working_frame = gr.Dropdown(label="working frame ID", choices=None, value=None)
+                working_frame = gr.Dropdown(label="working frame ID", choices=[], value=None)
                 output_result = gr.Image()
                 with gr.Row():
                     vis_frame_type = gr.Radio(label="Propagation level", choices=["check", "render"], value="check", scale=2)