test gradio

app.py

@@ -108,10 +108,10 @@ This way, each frame represents a distinct scene, and there’s no redundancy be
     frames = ask_gpt(massage_history,return_str=False)['frames']
     conditioning_frames = []
     controlnet_frame_indices =[]
+    frames = frames[0]
     for frame in frames:
         conditioning_frames.append(generate_image(frame['description'], reference_image, float(style_conditioning_scale)))
         controlnet_frame_indices.append(frame['frame_index'])
-        yield (conditioning_frames, "animation.gif")
 
     video = gif_pipe(
         prompt=prompt,
@@ -124,7 +124,7 @@ This way, each frame represents a distinct scene, and there’s no redundancy be
     ).frames[0]
     export_to_gif(video, "output.gif")
 
-    yield (conditioning_frames, "animation.gif")
+    return (conditioning_frames, "animation.gif")
 
 # Set up Gradio interface
 interface = gr.Interface(