Update app.py

app.py

@@ -1,19 +1,439 @@
 import gradio as gr
+import os
+from huggingface_hub import InferenceClient
+import tempfile
+import shutil
+from pathlib import Path
+from typing import Optional, Union
+import time
+
+# -------------------------
+# Utilities
+# -------------------------
+
+def cleanup_temp_files():
+    try:
+        temp_dir = tempfile.gettempdir()
+        for file_path in Path(temp_dir).glob("*.mp4"):
+            try:
+                if file_path.stat().st_mtime < (time.time() - 300):
+                    file_path.unlink(missing_ok=True)
+            except Exception:
+                pass
+    except Exception as e:
+        print(f"Cleanup error: {e}")
+
+def _client_from_token(token: Optional[str]) -> InferenceClient:
+    if not token:
+        raise gr.Error("Please sign in first. This app requires your Hugging Face login.")
+    # IMPORTANT: do not set bill_to when using user OAuth tokens
+    return InferenceClient(
+        provider="fal-ai",
+        api_key=token,
+    )
+
+def _save_bytes_as_temp_mp4(data: bytes) -> str:
+    temp_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
+    try:
+        temp_file.write(data)
+        temp_file.flush()
+        return temp_file.name
+    finally:
+        temp_file.close()
+
+def text_to_video(prompt, token: gr.OAuthToken | None, duration=5, aspect_ratio="16:9", resolution="720p", *_):
+    """Generate video from text prompt"""
+    try:
+        if token is None or not getattr(token, "token", None):
+            return None, "❌ Sign in with Hugging Face to continue. This app uses your inference provider credits."
+        
+        if not prompt or prompt.strip() == "":
+            return None, "Please enter a text prompt"
+        
+        cleanup_temp_files()
+        
+        # Create client with user's token
+        client = _client_from_token(token.token)
+        
+        # Generate video from text
+        try:
+            video = client.text_to_video(
+                prompt,
+                model="akhaliq/veo3.1-fast",
+            )
+        except Exception as e:
+            import requests
+            if isinstance(e, requests.HTTPError) and getattr(e.response, "status_code", None) == 403:
+                return None, "❌ Access denied by provider (403). Make sure your HF account has credits/permission for provider 'fal-ai' and model 'akhaliq/veo3.1-fast'."
+            raise
+        
+        # Save the video to a temporary file
+        video_path = _save_bytes_as_temp_mp4(video)
+        
+        return video_path, f"✅ Video generated successfully from prompt: '{prompt[:50]}...'"
+    
+    except gr.Error as e:
+        return None, f"❌ {str(e)}"
+    except Exception as e:
+        return None, f"❌ Generation failed. If this keeps happening, check your provider quota or try again later."
+
+def image_to_video(image, prompt, token: gr.OAuthToken | None, duration=5, aspect_ratio="16:9", resolution="720p", *_):
+    """Generate video from image and prompt"""
+    try:
+        if token is None or not getattr(token, "token", None):
+            return None, "❌ Sign in with Hugging Face to continue. This app uses your inference provider credits."
+        
+        if image is None:
+            return None, "Please upload an image"
+        
+        if not prompt or prompt.strip() == "":
+            return None, "Please enter a prompt describing the motion"
+        
+        cleanup_temp_files()
+        
+        # Read the image file
+        if isinstance(image, str):
+            # If image is a file path
+            with open(image, "rb") as image_file:
+                input_image = image_file.read()
+        else:
+            # If image is already bytes or similar
+            import io
+            from PIL import Image as PILImage
+            
+            # Convert to bytes if necessary
+            if isinstance(image, PILImage.Image):
+                buffer = io.BytesIO()
+                image.save(buffer, format='PNG')
+                input_image = buffer.getvalue()
+            else:
+                # Assume it's a numpy array or similar
+                pil_image = PILImage.fromarray(image)
+                buffer = io.BytesIO()
+                pil_image.save(buffer, format='PNG')
+                input_image = buffer.getvalue()
+        
+        # Create client with user's token
+        client = _client_from_token(token.token)
+        
+        # Generate video from image
+        try:
+            video = client.image_to_video(
+                input_image,
+                prompt=prompt,
+                model="akhaliq/veo3.1-fast-image-to-video",
+            )
+        except Exception as e:
+            import requests
+            if isinstance(e, requests.HTTPError) and getattr(e.response, "status_code", None) == 403:
+                return None, "❌ Access denied by provider (403). Make sure your HF account has credits/permission for provider 'fal-ai' and model 'akhaliq/veo3.1-fast-image-to-video'."
+            raise
+        
+        # Save the video to a temporary file
+        video_path = _save_bytes_as_temp_mp4(video)
+        
+        return video_path, f"✅ Video generated successfully with motion: '{prompt[:50]}...'"
+    
+    except gr.Error as e:
+        return None, f"❌ {str(e)}"
+    except Exception as e:
+        return None, f"❌ Generation failed. If this keeps happening, check your provider quota or try again later."
+
+def clear_text_tab():
+    """Clear text-to-video tab"""
+    return "", None, ""
+
+def clear_image_tab():
+    """Clear image-to-video tab"""
+    return None, "", None, ""
+
+# Custom CSS for better styling
+custom_css = """
+.container {
+    max-width: 1200px;
+    margin: auto;
+}
+.header-link {
+    text-decoration: none;
+    color: #2196F3;
+    font-weight: bold;
+}
+.header-link:hover {
+    text-decoration: underline;
+}
+.status-box {
+    padding: 10px;
+    border-radius: 5px;
+    margin-top: 10px;
+}
+.notice {
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    color: white;
+    padding: 14px 16px;
+    border-radius: 12px;
+    margin: 18px auto 6px;
+    max-width: 860px;
+    text-align: center;
+    font-size: 0.98rem;
+}
+.mobile-link-container {
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    padding: 1.5em;
+    border-radius: 10px;
+    text-align: center;
+    margin: 1em 0;
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+}
+.mobile-link {
+    color: white !important;
+    font-size: 1.2em;
+    font-weight: bold;
+    text-decoration: none;
+    display: inline-block;
+    padding: 0.5em 1.5em;
+    background: rgba(255, 255, 255, 0.2);
+    border-radius: 25px;
+    transition: all 0.3s ease;
+}
+.mobile-link:hover {
+    background: rgba(255, 255, 255, 0.3);
+    transform: translateY(-2px);
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
+}
+.mobile-text {
+    color: white;
+    margin-bottom: 0.5em;
+    font-size: 1.1em;
+}
+"""
 
 # Create the Gradio interface
-with gr.Blocks(theme=gr.themes.Soft(), title="AI Video Generator") as demo:
+with gr.Blocks(css=custom_css, theme=gr.themes.Soft(), title="AI Video Generator (Paid)") as demo:
     gr.Markdown(
         """
-        # 🚧 Under Maintenance
-        
-        Veo 3.1 app is currently under maintenance. Please check back later.
+        # 🎬 AI Video Generator
+        ### Generate stunning videos from text or animate your images with AI
+        #### Powered by VEO 3.1 Fast Model via Hugging Face Inference API (provider: fal-ai)
+        """
+    )
+    
+     gr.HTML(
+        """
+        <div style="text-align:center; max-width:900px; margin:0 auto;">
+            <h1 style="font-size:2.2em; margin-bottom:6px;">🎬 Sora-2</h1>
+            <p style="color:#777; margin:0 0 8px;">Generate videos via the Hugging Face Inference API (provider: fal-ai)</p>
+            <div class="notice">
+                <b>Heads up:</b> This is a paid app that uses <b>your</b> inference provider credits when you run generations.
+                Free users get <b>$0.10 in included credits</b>. <b>PRO users</b> get <b>$2 in included credits</b> 
+                and can continue using beyond that (with billing). 
+                <a href='http://huggingface.co/subscribe/pro?source=veo3' target='_blank' style='color:#fff; text-decoration:underline; font-weight:bold;'>Subscribe to PRO</a> 
+                for more credits. Please sign in with your Hugging Face account to continue.
+            </div>
+            <p style="font-size: 0.9em; color: #999; margin-top: 10px;">
+                Built with <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" style="color:#fff; text-decoration:underline;">anycoder</a>
+            </p>
+        </div>
+        """
+     )
+    
+    # Add mobile link section
+    gr.HTML(
+        """
+        <div class="mobile-link-container">
+            <div class="mobile-text">📱 On mobile? Use the optimized version:</div>
+            <a href="https://akhaliq-veo3-1-fast.hf.space" target="_blank" class="mobile-link">
+                🚀 Open Mobile Version
+            </a>
+        </div>
+        """
+    )
+    
+    gr.HTML(
+        """
+        <p style="text-align: center; font-size: 0.9em; color: #999; margin-top: 10px;">
+            Built with <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" style="color:#667eea; text-decoration:underline;">anycoder</a>
+        </p>
+        """
+    )
+    
+    # Add login button - required for OAuth
+    login_btn = gr.LoginButton("Sign in with Hugging Face")
+    
+    with gr.Tabs() as tabs:
+        # Text-to-Video Tab
+        with gr.Tab("📝 Text to Video", id=0):
+            gr.Markdown("### Transform your text descriptions into dynamic videos")
+            
+            with gr.Row():
+                with gr.Column(scale=1):
+                    text_prompt = gr.Textbox(
+                        label="Text Prompt",
+                        placeholder="Describe the video you want to create... (e.g., 'A young man walking on the street during sunset')",
+                        lines=4,
+                        max_lines=6
+                    )
+                    
+                    with gr.Row():
+                        text_generate_btn = gr.Button("🎬 Generate Video", variant="primary", scale=2)
+                        text_clear_btn = gr.ClearButton(value="🗑️ Clear", scale=1)
+                    
+                    text_status = gr.Textbox(
+                        label="Status",
+                        interactive=False,
+                        visible=True,
+                        elem_classes=["status-box"]
+                    )
+                
+                with gr.Column(scale=1):
+                    text_video_output = gr.Video(
+                        label="Generated Video",
+                        autoplay=True,
+                        show_download_button=True,
+                        height=400
+                    )
+            
+            # Examples for text-to-video
+            gr.Examples(
+                examples=[
+                    ["A serene beach at sunset with gentle waves"],
+                    ["A bustling city street with neon lights at night"],
+                    ["A majestic eagle soaring through mountain peaks"],
+                    ["An astronaut floating in space near the International Space Station"],
+                    ["Cherry blossoms falling in slow motion in a Japanese garden"],
+                ],
+                inputs=text_prompt,
+                label="Example Prompts"
+            )
         
-        We apologize for any inconvenience.
-
-        check https://huggingface.co/spaces/akhaliq/veo3.1-fast/discussions/29 to latest updates
-        """,
+        # Image-to-Video Tab
+        with gr.Tab("🖼️ Image to Video", id=1):
+            gr.Markdown("### Bring your static images to life with motion")
+            
+            with gr.Row():
+                with gr.Column(scale=1):
+                    image_input = gr.Image(
+                        label="Upload Image",
+                        type="pil",
+                        height=300
+                    )
+                    
+                    image_prompt = gr.Textbox(
+                        label="Motion Prompt",
+                        placeholder="Describe how the image should move... (e.g., 'The cat starts to dance')",
+                        lines=3,
+                        max_lines=5
+                    )
+                    
+                    with gr.Row():
+                        image_generate_btn = gr.Button("🎬 Animate Image", variant="primary", scale=2)
+                        image_clear_btn = gr.ClearButton(value="🗑️ Clear", scale=1)
+                    
+                    image_status = gr.Textbox(
+                        label="Status",
+                        interactive=False,
+                        visible=True,
+                        elem_classes=["status-box"]
+                    )
+                
+                with gr.Column(scale=1):
+                    image_video_output = gr.Video(
+                        label="Generated Video",
+                        autoplay=True,
+                        show_download_button=True,
+                        height=400
+                    )
+            
+            # Examples for image-to-video
+            gr.Examples(
+                examples=[
+                    [None, "The person starts walking forward"],
+                    [None, "The animal begins to run"],
+                    [None, "Camera slowly zooms in while the subject smiles"],
+                    [None, "The flowers sway gently in the breeze"],
+                    [None, "The clouds move across the sky in time-lapse"],
+                ],
+                inputs=[image_input, image_prompt],
+                label="Example Motion Prompts"
+            )
+    
+    # How to Use section
+    with gr.Accordion("📖 How to Use", open=False):
+        gr.Markdown(
+            """
+            ### Text to Video:
+            1. Enter a detailed description of the video you want to create
+            2. Optionally adjust advanced settings (duration, aspect ratio, resolution)
+            3. Click "Generate Video" and wait for the AI to create your video
+            4. Download or preview your generated video
+            
+            ### Image to Video:
+            1. Upload an image you want to animate
+            2. Describe the motion or action you want to add to the image
+            3. Optionally adjust advanced settings
+            4. Click "Animate Image" to bring your image to life
+            5. Download or preview your animated video
+            
+            ### Tips for Better Results:
+            - Be specific and descriptive in your prompts
+            - For image-to-video, describe natural motions that fit the image
+            - Use high-quality input images for better results
+            - Experiment with different prompts to get the desired effect
+            
+            ### Mobile Users:
+            - For the best mobile experience, use the optimized version at: https://akhaliq-veo3-1-fast.hf.space
+            """
+        )
+    
+    # Event handlers
+    text_generate_btn.click(
+        fn=text_to_video,
+        inputs=[text_prompt, login_btn],
+        outputs=[text_video_output, text_status],
+        show_progress="full",
+        queue=False,
+        api_name=False,
+        show_api=False
+    )
+    
+    text_clear_btn.click(
+        fn=clear_text_tab,
+        inputs=[],
+        outputs=[text_prompt, text_video_output, text_status],
+        queue=False
+    )
+    
+    image_generate_btn.click(
+        fn=image_to_video,
+        inputs=[image_input, image_prompt, login_btn],
+        outputs=[image_video_output, image_status],
+        show_progress="full",
+        queue=False,
+        api_name=False,
+        show_api=False
+    )
+    
+    image_clear_btn.click(
+        fn=clear_image_tab,
+        inputs=[],
+        outputs=[image_input, image_prompt, image_video_output, image_status],
+        queue=False
     )
 
 # Launch the app
 if __name__ == "__main__":
-    demo.launch()
+    try:
+        cleanup_temp_files()
+        if os.path.exists("gradio_cached_examples"):
+            shutil.rmtree("gradio_cached_examples", ignore_errors=True)
+    except Exception as e:
+        print(f"Initial cleanup error: {e}")
+    
+    demo.queue(status_update_rate="auto", api_open=False, default_concurrency_limit=None)
+    demo.launch(
+        show_api=False,
+        share=False,
+        show_error=True,
+        enable_monitoring=False,
+        quiet=True,
+        ssr_mode=True
+    )