Deploy Gradio app with multiple files

app.py

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+import gradio as gr
+import os
+from huggingface_hub import InferenceClient
+from pathlib import Path
+import tempfile
+
+# Initialize the inference client
+client = InferenceClient(
+    provider="fal-ai",
+    api_key=os.environ.get("HF_TOKEN"),
+    bill_to="huggingface",
+)
+
+def generate_video(image, prompt, progress=gr.Progress()):
+    """
+    Generate a video from an image using the Ovi model.
+    
+    Args:
+        image: Input image (PIL Image or file path)
+        prompt: Text prompt describing the desired motion/animation
+        progress: Gradio progress tracker
+    
+    Returns:
+        Path to the generated video file
+    """
+    if image is None:
+        raise gr.Error("Please upload an image first!")
+    
+    if not prompt or prompt.strip() == "":
+        raise gr.Error("Please enter a prompt describing the desired motion!")
+    
+    try:
+        progress(0.2, desc="Processing image...")
+        
+        # Read the image file
+        if isinstance(image, str):
+            with open(image, "rb") as image_file:
+                input_image = image_file.read()
+        else:
+            # If image is a PIL Image, save it temporarily
+            temp_image = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
+            image.save(temp_image.name)
+            with open(temp_image.name, "rb") as image_file:
+                input_image = image_file.read()
+        
+        progress(0.4, desc="Generating video with AI...")
+        
+        # Generate video using the inference client
+        video = client.image_to_video(
+            input_image,
+            prompt=prompt,
+            model="chetwinlow1/Ovi",
+        )
+        
+        progress(0.9, desc="Finalizing video...")
+        
+        # Save the video to a temporary file
+        output_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
+        
+        # Check if video is bytes or a file path
+        if isinstance(video, bytes):
+            with open(output_path.name, "wb") as f:
+                f.write(video)
+        elif isinstance(video, str) and os.path.exists(video):
+            # If it's a path, copy it
+            import shutil
+            shutil.copy(video, output_path.name)
+        else:
+            # Try to write it directly
+            with open(output_path.name, "wb") as f:
+                f.write(video)
+        
+        progress(1.0, desc="Complete!")
+        
+        return output_path.name
+    
+    except Exception as e:
+        raise gr.Error(f"Error generating video: {str(e)}")
+
+# Create the Gradio interface
+with gr.Blocks(
+    theme=gr.themes.Soft(
+        primary_hue="blue",
+        secondary_hue="indigo",
+    ),
+    css="""
+        .header-link {
+            font-size: 0.9em;
+            color: #666;
+            text-decoration: none;
+            margin-bottom: 1em;
+            display: inline-block;
+        }
+        .header-link:hover {
+            color: #333;
+            text-decoration: underline;
+        }
+        .main-header {
+            text-align: center;
+            margin-bottom: 2em;
+        }
+        .info-box {
+            background-color: #f0f7ff;
+            border-left: 4px solid #4285f4;
+            padding: 1em;
+            margin: 1em 0;
+            border-radius: 4px;
+        }
+    """,
+    title="Image to Video Generator",
+) as demo:
+    
+    gr.HTML(
+        """
+        <div class="main-header">
+            <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" class="header-link">
+                Built with anycoder ✨
+            </a>
+        </div>
+        """
+    )
+    
+    gr.Markdown(
+        """
+        # 🎬 Image to Video Generator
+        
+        Transform your static images into dynamic videos using AI! Upload an image and describe the motion you want to see.
+        
+        Powered by the **Ovi** model via HuggingFace Inference API.
+        """
+    )
+    
+    gr.HTML(
+        """
+        <div class="info-box">
+            <strong>💡 Tips for best results:</strong>
+            <ul>
+                <li>Use clear, well-lit images with a single main subject</li>
+                <li>Write specific prompts describing the desired motion or action</li>
+                <li>Keep prompts concise and focused on movement</li>
+                <li>Processing may take 30-60 seconds depending on server load</li>
+            </ul>
+        </div>
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Image(
+                label="📸 Upload Image",
+                type="filepath",
+                sources=["upload", "clipboard"],
+                height=400,
+            )
+            
+            prompt_input = gr.Textbox(
+                label="✍️ Motion Prompt",
+                placeholder="Describe the motion or animation you want to see...",
+                lines=3,
+                value="The subject starts to move naturally",
+            )
+            
+            
+            
+            generate_btn = gr.Button(
+                "🎬 Generate Video",
+                variant="primary",
+                size="lg",
+            )
+            
+            clear_btn = gr.Button(
+                "🗑️ Clear",
+                variant="secondary",
+            )
+        
+        with gr.Column(scale=1):
+            video_output = gr.Video(
+                label="🎥 Generated Video",
+                height=400,
+                autoplay=True,
+            )
+            
+            gr.Markdown(
+                """
+                ### About the Model
+                
+                This app uses the **Ovi** model, which specializes in generating realistic video animations from static images.
+                The model can understand natural language prompts to create various types of motion and animation.
+                """
+            )
+    
+    # Event handlers
+    generate_btn.click(
+        fn=generate_video,
+        inputs=[image_input, prompt_input],
+        outputs=[video_output],
+        api_name="generate_video",
+    )
+    
+    clear_btn.click(
+        fn=lambda: (None, "The subject starts to move naturally", None),
+        inputs=None,
+        outputs=[image_input, prompt_input, video_output],
+    )
+    
+    gr.Markdown(
+        """
+        ---
+        
+        ### 🚀 How it works
+        
+        1. **Upload** your image - any photo or illustration
+        2. **Describe** the motion you want to see in the prompt
+        3. **Generate** and watch your image come to life!
+        
+        ### ⚠️ Notes
+        
+        - Video generation may take 30-60 seconds
+        - Requires a valid HuggingFace token with Inference API access
+        - Best results with clear, high-quality images
+        - The model works best with realistic subjects and natural motions
+        
+        ### 🔗 Resources
+        
+        - [Ovi Model Card](https://huggingface.co/chetwinlow1/Ovi)
+        - [HuggingFace Inference API](https://huggingface.co/docs/huggingface_hub/guides/inference)
+        """
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+huggingface_hub
+Pillow
+
+This complete image-to-video Gradio application includes:
+
+## Key Features:
+
+1. **Clean, Modern UI**:
+   - Two-column layout with image input and video output
+   - Progress tracking during generation
+   - Example prompts in an accordion
+   - Informative tips and instructions
+
+2. **Functionality**:
+   - Upload images via drag-and-drop or clipboard
+   - Custom motion prompts
+   - Video generation using the Ovi model
+   - Clear button to reset inputs
+   - Autoplay for generated videos
+
+3. **Error Handling**:
+   - Validates image and prompt inputs
+   - Graceful error messages using `gr.Error`
+   - Handles different image input types
+
+4. **User Experience**:
+   - Progress indicators during generation
+   - Example prompts for inspiration
+   - Tips for best results
+   - Detailed documentation in the UI
+
+5. **Professional Design**:
+   - Custom CSS styling
+   - "Built with anycoder" link in the header
+   - Responsive layout
+   - Info boxes with helpful tips
+
+The app uses the HuggingFace Inference Client with the Ovi model to transform static images into animated videos based on text prompts. Just set your `HF_TOKEN` environment variable and launch!