Update app.py

app.py

@@ -13,42 +13,50 @@ pipe = AnimateDiffPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
 pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
 pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")
 
-# Time estimate logic
+# Estimate time based on device
 def estimate_time(num_frames):
-    est_seconds = int(num_frames * 2.5)
-    return f"Estimated time: ~{est_seconds} seconds"
+    if torch.cuda.is_available():
+        time_per_frame = 2.5  # GPU
+    else:
+        time_per_frame = 12.0  # CPU
 
-# Simulated progress bar + generation
+    est_seconds = int(num_frames * time_per_frame)
+    return f"Estimated time: ~{est_seconds} seconds ({'GPU' if torch.cuda.is_available() else 'CPU'})"
+
+# Video generation function with simulated progress
 def generate_video(image, prompt, num_frames, email):
     status = "Generating..."
     progress = 0
 
     image = image.convert("RGB").resize((512, 512))
-    
-    # Simulate progress (before actual generation)
+
+    # Simulated progress before real generation
     for i in range(3):
         time.sleep(0.8)
         progress += 30
 
+    # Generate animation frames
     result = pipe(prompt=prompt, image=image, num_frames=num_frames, guidance_scale=7.5)
     frames = result.frames
 
+    # Save video
     video_path = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
     imageio.mimsave(video_path, frames, fps=8)
 
+    # Build message
     final_message = f"✅ Done! Generated {num_frames} frames."
     if email:
-        final_message += f" Notification would be sent to: {email} (simulated)"
-    
-    return video_path, final_message, gr.update(visible=True), gr.update(value=100)
+        final_message += f"\n📧 Notification would be sent to: {email} (simulated)"
+
+    return video_path, final_message, gr.update(value=100), gr.update(visible=True, value=video_path)
 
-# Gradio UI
+# UI layout
 with gr.Blocks() as demo:
     gr.Markdown("# 🌀 Image + Prompt to Video Generator")
-    
+
     with gr.Row():
-        image_input = gr.Image(type="pil", label="Upload Image")
-        prompt_input = gr.Textbox(label="Describe Motion (Prompt)")
+        image_input = gr.Image(type="pil", label="🖼️ Upload Image")
+        prompt_input = gr.Textbox(label="✏️ Describe Motion (Prompt)")
 
     with gr.Row():
         num_frames_slider = gr.Slider(8, 32, value=16, step=8, label="🎞️ Number of Frames")
@@ -56,7 +64,7 @@ with gr.Blocks() as demo:
 
     email_input = gr.Textbox(label="📧 Optional Email (Notify when done)")
     generate_btn = gr.Button("🎬 Generate Video")
-    
+
     with gr.Row():
         status_output = gr.Textbox(label="🔄 Status", interactive=False)
         progress_bar = gr.Slider(0, 100, value=0, label="📊 Progress", interactive=False)
@@ -70,7 +78,11 @@ with gr.Blocks() as demo:
     generate_btn.click(
         fn=generate_video,
         inputs=[image_input, prompt_input, num_frames_slider, email_input],
-        outputs=[video_output, status_output, download_button, progress_bar]
+        outputs=[video_output, status_output, progress_bar, download_button]
     )
 
+    # Optional warning if on CPU
+    if not torch.cuda.is_available():
+        gr.Markdown("⚠️ **Warning: Running on CPU. Generation will be slow!**")
+
 demo.launch()