Update app.py

app.py

@@ -1,249 +1,17 @@
 import gradio as gr
-import os
-import tempfile
-import shutil
-from typing import Optional, Union
-from pathlib import Path
-from huggingface_hub import InferenceClient
 
-# -------------------------
-# Utilities
-# -------------------------
-
-def cleanup_temp_files():
-    try:
-        temp_dir = tempfile.gettempdir()
-        for file_path in Path(temp_dir).glob("*.mp4"):
-            try:
-                import time
-                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,
+# Create the Gradio interface
+with gr.Blocks(theme=gr.themes.Soft(), title="Sora 2") as demo:
+    gr.Markdown(
+        """
+        # 🚧 Under Maintenance
+        
+        Sora 2 app is currently under maintenance. Please check back later.
+        
+        We apologize for any inconvenience.
+        """,
     )
 
-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()
-
-# -------------------------
-# Inference wrappers (no env fallback; always require LoginButton)
-# -------------------------
-
-def generate_video(
-    prompt: str,
-    token: gr.OAuthToken | None,
-    duration: int = 8,          # kept for future use
-    size: str = "1280x720",     # kept for future use
-    *_                          # tolerate extra event payloads
-) -> Optional[str]:
-    if token is None or not getattr(token, "token", None):
-        raise gr.Error("Sign in with Hugging Face to continue. This app uses your inference provider credits.")
-    if not prompt or not prompt.strip():
-        return None
-
-    cleanup_temp_files()
-    try:
-        client = _client_from_token(token.token)
-        # Ensure model id matches what users can access. Change if you intend provider repo.
-        model_id = "akhaliq/sora-2"
-        try:
-            video_bytes = client.text_to_video(prompt, model=model_id)
-        except Exception as e:
-            # Provide a clearer message if this is an HTTP 403 from requests
-            import requests
-            if isinstance(e, requests.HTTPError) and getattr(e.response, "status_code", None) == 403:
-                raise gr.Error(
-                    "Access denied by provider (403). Make sure your HF account has credits/permission "
-                    f"for provider 'fal-ai' and model '{model_id}'."
-                )
-            raise
-        return _save_bytes_as_temp_mp4(video_bytes)
-    except gr.Error:
-        raise
-    except Exception:
-        raise gr.Error("Generation failed. If this keeps happening, check your provider quota or try again later.")
-
-def generate_video_from_image(
-    image: Union[str, bytes, None],
-    prompt: str,
-    token: gr.OAuthToken | None,
-    *_
-) -> Optional[str]:
-    if token is None or not getattr(token, "token", None):
-        raise gr.Error("Sign in with Hugging Face to continue. This app uses your inference provider credits.")
-    if not image or not prompt or not prompt.strip():
-        return None
-
-    cleanup_temp_files()
-    try:
-        # Load image bytes
-        if isinstance(image, str):
-            with open(image, "rb") as f:
-                input_image = f.read()
-        elif isinstance(image, (bytes, bytearray)):
-            input_image = image
-        else:
-            return None
-
-        client = _client_from_token(token.token)
-        model_id = "akhaliq/sora-2-image-to-video"
-        try:
-            video_bytes = client.image_to_video(
-                input_image,
-                prompt=prompt,
-                model=model_id,
-            )
-        except Exception as e:
-            import requests
-            if isinstance(e, requests.HTTPError) and getattr(e.response, "status_code", None) == 403:
-                raise gr.Error(
-                    "Access denied by provider (403). Make sure your HF account has credits/permission "
-                    f"for provider 'fal-ai' and model '{model_id}'."
-                )
-            raise
-        return _save_bytes_as_temp_mp4(video_bytes)
-    except gr.Error:
-        raise
-    except Exception:
-        raise gr.Error("Generation failed. If this keeps happening, check your provider quota or try again later.")
-
-# -------------------------
-# UI
-# -------------------------
-
-def create_ui():
-    css = '''
-    .logo-dark{display: none}
-    .dark .logo-dark{display: block !important}
-    .dark .logo-light{display: none}
-    #sub_title{margin-top: -20px !important}
-    .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;
-    }
-    '''
-
-    with gr.Blocks(title="Sora-2 (uses your provider credits)", theme=gr.themes.Soft(), css=css) as demo:
-        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=sora_2' 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>
-        """)
-
-        login_btn = gr.LoginButton("Sign in with Hugging Face")
-
-        # Text -> Video
-        with gr.Row():
-            with gr.Column(scale=1):
-                prompt_input = gr.Textbox(
-                    label="Enter your prompt",
-                    placeholder="Describe the video you want to create…",
-                    lines=4,
-                    elem_id="prompt-text-input"
-                )
-                generate_btn = gr.Button("🎥 Generate Video", variant="primary")
-            with gr.Column(scale=1):
-                video_output = gr.Video(
-                    label="Generated Video",
-                    height=400,
-                    interactive=False,
-                    show_download_button=True,
-                    elem_id="text-to-video"
-                )
-
-        # Order of inputs: prompt, token
-        generate_btn.click(
-            fn=generate_video,
-            inputs=[prompt_input, login_btn],
-            outputs=[video_output],
-        )
-
-        # Image -> Video
-        gr.HTML("""
-            <div style="text-align:center; margin: 34px 0 10px;">
-                <h3 style="margin-bottom:6px;">🖼️ ➜ 🎬 Image → Video (beta)</h3>
-                <p style="color:#666; margin:0;">Turn a single image into a short video with a guiding prompt.</p>
-            </div>
-        """)
-        with gr.Row():
-            with gr.Column(scale=1):
-                image_input = gr.Image(label="Upload an image", type="filepath")
-                img_prompt_input = gr.Textbox(
-                    label="Describe how the scene should evolve",
-                    placeholder="e.g., The cat starts to dance and spins playfully",
-                    lines=3,
-                    elem_id="img-prompt-text-input"
-                )
-                generate_img_btn = gr.Button("🎥 Generate from Image", variant="primary")
-            with gr.Column(scale=1):
-                video_output_img = gr.Video(
-                    label="Generated Video (from Image)",
-                    height=400,
-                    interactive=False,
-                    show_download_button=True,
-                    elem_id="image-to-video"
-                )
-
-        # Order of inputs: image, prompt, token
-        generate_img_btn.click(
-            fn=generate_video_from_image,
-            inputs=[image_input, img_prompt_input, login_btn],
-            outputs=[video_output_img],
-        )
-
-        # Examples: keep UI-only (no automatic inference to avoid accidental charges)
-        gr.Examples(
-            examples=[["A majestic golden eagle soaring through a vibrant sunset sky"]],
-            inputs=prompt_input
-        )
-
-    return demo
-
-# -------------------------
-# Entrypoint
-# -------------------------
-
+# Launch the app
 if __name__ == "__main__":
-    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}")
-
-    app = create_ui()
-    app.queue(status_update_rate="auto", api_open=False, default_concurrency_limit=None)
-    app.launch(show_api=False, enable_monitoring=False, quiet=True, ssr_mode=True)
+    demo.launch()