Update app.py

app.py

@@ -1,28 +1,26 @@
 import gradio as gr
-import torch
-from diffusers import WanPipeline, UniPCMultistepScheduler
-
-pipe = WanPipeline.from_pretrained(
-    "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
-    torch_dtype=torch.float16
-)
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-pipe = pipe.to("cuda")
+import time
+import shutil
 
 def generate_video(prompt, duration):
     """
-    Generates a video from a text prompt locally using Wan 2.2.
-    Duration is clamped between 1 and 8 seconds.
-    Returns the path to the saved MP4 file.
+    Simulates video generation for testing UI and API.
+    Returns a placeholder MP4 file.
     """
     duration = max(1, min(duration, 8)) 
-    video = pipe(prompt=prompt, height=512, width=512, duration_seconds=duration, guidance_scale=1.0).videos[0]
-    video_path = "output.mp4"
-    video.save(video_path)
-    return video_path
+    print(f"Generating video for prompt: '{prompt}' with duration {duration}s")
+    
+    time.sleep(1)
+    
+    placeholder_video = "placeholder.mp4"
+    output_path = "output.mp4"
+    shutil.copyfile(placeholder_video, output_path)
+    
+    return output_path
 
+# Gradio UI + API
 with gr.Blocks() as demo:
-    gr.Markdown("## Wan 2.2 Video Generator (Local)")
+    gr.Markdown("## Wan 2.2 Video Generator (Local Test Placeholder)")
     
     with gr.Row():
         prompt_input = gr.Textbox(label="Prompt", placeholder="Describe your scene")
@@ -32,5 +30,4 @@ with gr.Blocks() as demo:
     
     submit_btn.click(generate_video, inputs=[prompt_input, duration_input], outputs=output_video)
 
-
-demo.launch(api_name="generate-video")
+demo.launch(api_name="generate-video")