test gradio

app.py

@@ -143,14 +143,12 @@ 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 =[]
-    long_prompt = {}
     for frame in frames:
         conditioning_frames.append(generate_image(frame['description'], reference_image, float(controlnet_conditioning_scale)))
         controlnet_frame_indices.append(frame['frame_index'])
-        long_prompt[frame['frame_index']] = frame['description']
 
     video = gif_pipe(
-        prompt=long_prompt,
+        prompt=prompt,
         negative_prompt="low quality, worst quality",
         num_inference_steps=25,
         conditioning_frames=conditioning_frames,