test gradio

app.py

@@ -144,7 +144,7 @@ This way, each frame represents a distinct scene, and there’s no redundancy be
     conditioning_frames = []
     controlnet_frame_indices =[]
     for frame in frames:
-        conditioning_frames.append(generate_image(frame['description'], reference_image, controlnet_conditioning_scale))
+        conditioning_frames.append(generate_image(frame['description'], reference_image, float(controlnet_conditioning_scale)))
         controlnet_frame_indices.append(frame['frame_index'])
 
     video = gif_pipe(
@@ -153,7 +153,7 @@ This way, each frame represents a distinct scene, and there’s no redundancy be
         num_inference_steps=25,
         conditioning_frames=conditioning_frames,
         controlnet_frame_indices=controlnet_frame_indices,
-        controlnet_conditioning_scale=controlnet_conditioning_scale,
+        controlnet_conditioning_scale=float(controlnet_conditioning_scale),
     ).frames[0]
     export_to_gif(video, "output.gif")
 
@@ -178,7 +178,7 @@ This way, each frame represents a distinct scene, and there’s no redundancy be
 
 # Set up Gradio interface
 interface = gr.Interface(
-    fn=generate_gif,
+    fn=generate_frames,
     inputs=[
         gr.Textbox(label="Prompt"),
         # gr.Image( type= "filepath",label="Reference Image (Style)"),