Update app.py

app.py

@@ -4,51 +4,53 @@ from gradio_client import Client, handle_file
 def generate_video(input_image, prompt, negative_prompt, diffusion_step, height, width, scfg_scale, use_dctinit, dct_coefficients, noise_level, motion_bucket_id, seed):
     client = Client("maxin-cn/Cinemo")
     
-    try:
-        result = client.predict(
-            input_image=handle_file(input_image),
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            diffusion_step=diffusion_step,
-            height=height,
-            width=width,
-            scfg_scale=scfg_scale,
-            use_dctinit=use_dctinit,
-            dct_coefficients=dct_coefficients,
-            noise_level=noise_level,
-            motion_bucket_id=motion_bucket_id,
-            seed=seed,
-            api_name="/gen_video"
-        )
-
-        print("API Response:", result)
-        return result
-    except Exception as e:
-        return f"Error: {str(e)}"
+    result = client.predict(
+        input_image=handle_file(input_image),
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        diffusion_step=diffusion_step,
+        height=height,
+        width=width,
+        scfg_scale=scfg_scale,
+        use_dctinit=use_dctinit,
+        dct_coefficients=dct_coefficients,
+        noise_level=noise_level,
+        motion_bucket_id=motion_bucket_id,
+        seed=seed,
+        api_name="/gen_video"
+    )
+    print("API response" , result)
+    
+    video_path = result.get('video')  # Extract the video file path from the API response
+    
+    if video_path is None:
+        return "The API did not return a valid video. Please try again."
+    
+    return video_path  # Return the path to the video file
 
-# Define the Gradio interface
+# Gradio Interface
 with gr.Blocks() as demo:
     with gr.Row():
         input_image = gr.Image(label="Input Image", type="filepath")
-        with gr.Column():
-            prompt = gr.Textbox(label="Prompt", placeholder="Enter prompt here...")
-            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here...")
-
-    diffusion_step = gr.Slider(minimum=1, maximum=100, value=50, label="Diffusion Steps")
-    height = gr.Slider(minimum=128, maximum=1024, value=320, label="Height")
-    width = gr.Slider(minimum=128, maximum=1024, value=512, label="Width")
-    scfg_scale = gr.Slider(minimum=1.0, maximum=20.0, value=7.5, label="CFG Scale")
-    use_dctinit = gr.Checkbox(value=True, label="Enable DCTInit")
-    dct_coefficients = gr.Slider(minimum=0.0, maximum=1.0, value=0.23, label="DCT Coefficients")
-    noise_level = gr.Slider(minimum=0, maximum=1000, value=985, label="Noise Level")
-    motion_bucket_id = gr.Slider(minimum=1, maximum=100, value=10, label="Motion Intensity")
-    seed = gr.Slider(minimum=1, maximum=10000, value=100, label="Seed")
-
-    generate_btn = gr.Button("Generate Video")
-
-    output_video = gr.Video(label="Generated Video")
+    with gr.Row():
+        prompt = gr.Textbox(label="Prompt")
+        negative_prompt = gr.Textbox(label="Negative Prompt")
+    with gr.Row():
+        diffusion_step = gr.Slider(label="Sampling steps", minimum=1, maximum=100, value=50)
+        height = gr.Slider(label="Height", minimum=64, maximum=1024, value=320)
+        width = gr.Slider(label="Width", minimum=64, maximum=1024, value=512)
+        scfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7.5)
+    with gr.Row():
+        use_dctinit = gr.Checkbox(label="Enable DCTInit", value=True)
+        dct_coefficients = gr.Slider(label="DCT Coefficients", minimum=0.0, maximum=1.0, value=0.23)
+        noise_level = gr.Slider(label="Noise Level", minimum=0, maximum=1000, value=985)
+        motion_bucket_id = gr.Slider(label="Motion Intensity", minimum=0, maximum=20, value=10)
+        seed = gr.Number(label="Seed", value=100)
 
-    generate_btn.click(generate_video, inputs=[input_image, prompt, negative_prompt, diffusion_step, height, width, scfg_scale, use_dctinit, dct_coefficients, noise_level, motion_bucket_id, seed], outputs=output_video)
+    video_output = gr.Video(label="Generated Video")
+    
+    generate_button = gr.Button("Generate Video")
+    
+    generate_button.click(generate_video, inputs=[input_image, prompt, negative_prompt, diffusion_step, height, width, scfg_scale, use_dctinit, dct_coefficients, noise_level, motion_bucket_id, seed], outputs=video_output)
 
-# Launch the app with verbose error reporting
-demo.launch(show_error=True)
+demo.launch()