app.py

import gradio as gr
import os
import tempfile
import shutil
from typing import Optional, Tuple, Union
from huggingface_hub import InferenceClient, whoami
from pathlib import Path

# Initialize Hugging Face Inference Client with fal-ai provider
client = InferenceClient(
    provider="fal-ai",
    api_key=os.environ.get("HF_TOKEN"),
    bill_to="huggingface",
)

def verify_pro_status(token: Optional[Union[gr.OAuthToken, str]]) -> bool:
    """Verifies if the user is a Hugging Face PRO user or part of an enterprise org."""
    if not token:
        return False
    
    if isinstance(token, gr.OAuthToken):
        token_str = token.token
    elif isinstance(token, str):
        token_str = token
    else:
        return False
    
    try:
        user_info = whoami(token=token_str)
        return (
            user_info.get("isPro", False) or
            any(org.get("isEnterprise", False) for org in user_info.get("orgs", []))
        )
    except Exception as e:
        print(f"Could not verify user's PRO/Enterprise status: {e}")
        return False

def cleanup_temp_files():
    """Clean up old temporary video files to prevent storage overflow."""
    try:
        temp_dir = tempfile.gettempdir()
        # Clean up old .mp4 files in temp directory
        for file_path in Path(temp_dir).glob("*.mp4"):
            try:
                # Remove files older than 5 minutes
                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 generate_video(
    prompt: str,
    duration: int = 8,
    size: str = "1280x720",
    api_key: Optional[str] = None
) -> Tuple[Optional[str], str]:
    """Generate video using Sora-2 through Hugging Face Inference API with fal-ai provider."""
    cleanup_temp_files()
    try:
        if api_key:
            temp_client = InferenceClient(
                provider="fal-ai",
                api_key=api_key,
                bill_to="huggingface",
            )
        else:
            temp_client = client
            if not os.environ.get("HF_TOKEN") and not api_key:
                return None, "❌ Please set HF_TOKEN environment variable."
        
        video_bytes = temp_client.text_to_video(
            prompt,
            model="akhaliq/sora-2",
        )
        
        temp_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
        try:
            temp_file.write(video_bytes)
            temp_file.flush()
            video_path = temp_file.name
        finally:
            temp_file.close()
        
        return video_path, "✅ Video generated successfully!"
    except Exception as e:
        return None, f"❌ Error generating video: {str(e)}"

# --- NEW: image -> video support ---
def generate_video_from_image(
    image: Union[str, bytes],
    prompt: str,
    api_key: Optional[str] = None
) -> Tuple[Optional[str], str]:
    """Generate a video from a single input image + prompt using Sora-2 image-to-video."""
    cleanup_temp_files()
    if not prompt or prompt.strip() == "":
        return None, "❌ Please enter a prompt"
    try:
        if api_key:
            temp_client = InferenceClient(
                provider="fal-ai",
                api_key=api_key,
                bill_to="huggingface",
            )
        else:
            temp_client = client
            if not os.environ.get("HF_TOKEN") and not api_key:
                return None, "❌ Please set HF_TOKEN environment variable."

        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, "❌ Invalid image input. Please upload an image."

        video_bytes = temp_client.image_to_video(
            input_image,
            prompt=prompt,
            model="akhaliq/sora-2-image-to-video",
        )

        temp_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
        try:
            temp_file.write(video_bytes)
            temp_file.flush()
            video_path = temp_file.name
        finally:
            temp_file.close()

        return video_path, "✅ Video generated from image successfully!"
    except Exception as e:
        return None, f"❌ Error generating video from image: {str(e)}"

def generate_with_pro_auth(
    prompt: str, 
    oauth_token: Optional[gr.OAuthToken] = None
) -> Tuple[Optional[str], str]:
    """Wrapper function that checks if user is PRO before generating video."""
    if not verify_pro_status(oauth_token):
        raise gr.Error("Access Denied. This app is exclusively for Hugging Face PRO users.")
    
    if not prompt or prompt.strip() == "":
        return None, "❌ Please enter a prompt"
    
    return generate_video(
        prompt, 
        duration=8, 
        size="1280x720", 
        api_key=None
    )

# --- NEW: PRO-gated wrapper for image -> video ---
def generate_with_pro_auth_image(
    prompt: str,
    image_path: Optional[str] = None,
    oauth_token: Optional[gr.OAuthToken] = None
) -> Tuple[Optional[str], str]:
    """Checks PRO status then calls image->video generator."""
    if not verify_pro_status(oauth_token):
        raise gr.Error("Access Denied. This app is exclusively for Hugging Face PRO users.")
    if not image_path:
        return None, "❌ Please upload an image"
    return generate_video_from_image(image=image_path, prompt=prompt, api_key=None)

def simple_generate(prompt: str) -> Optional[str]:
    """Simplified wrapper for examples that only returns video."""
    if not prompt or prompt.strip() == "":
        return None
    video_path, _ = generate_video(prompt, duration=8, size="1280x720", api_key=None)
    return video_path

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}
    .pro-badge{
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        color: white;
        padding: 4px 12px;
        border-radius: 20px;
        font-size: 0.9em;
        font-weight: bold;
        display: inline-block;
        margin-left: 8px;
    }
    '''
    
    with gr.Blocks(title="Sora-2 Text-to-Video Generator", theme=gr.themes.Soft(), css=css) as demo:
        gr.HTML("""
            <div style="text-align: center; max-width: 800px; margin: 0 auto;">
                <h1 style="font-size: 2.5em; margin-bottom: 0.5em;">
                    🎬 Sora-2 Text-to-Video Generator
                    <span class="pro-badge">PRO</span>
                </h1>
                <p style="font-size: 1.1em; color: #666; margin-bottom: 20px;">Generate stunning videos using OpenAI's Sora-2 model</p>
                <p id="sub_title" style="font-size: 1em; margin-top: 20px; margin-bottom: 15px;">
                    <strong>Exclusive access for Hugging Face PRO users.</strong> 
                    <a href="http://huggingface.co/subscribe/pro?source=sora2_video" target="_blank" style="color: #667eea;">Subscribe to PRO →</a>
                </p>
                <p style="font-size: 0.9em; color: #999; margin-top: 15px;">
                    Built with <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" style="color: #667eea;">anycoder</a>
                </p>
            </div>
        """)
        
        gr.LoginButton()
        pro_message = gr.Markdown(visible=False)
        main_interface = gr.Column(visible=False)
        
        with main_interface:
            gr.HTML("""<div style="text-align: center; margin: 20px 0;">
                <p style="color: #28a745; font-weight: bold;">✨ Welcome PRO User! You have full access to Sora-2.</p>
            </div>""")
            
            # 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
                    )
                    generate_btn = gr.Button("🎥 Generate Video", variant="primary", size="lg")
                with gr.Column(scale=1):
                    video_output = gr.Video(label="Generated Video", height=400, interactive=False, show_download_button=True)
                    status_output = gr.Textbox(label="Status", interactive=False, visible=True)
            
            generate_btn.click(
                fn=generate_with_pro_auth,
                inputs=[prompt_input],
                outputs=[video_output, status_output],
                queue=False
            )

            # --- NEW: Image -> Video UI ---
            gr.HTML("""
                <div style="text-align: center; margin: 40px 0 10px;">
                    <h3 style="margin-bottom: 8px;">🖼️ ➜ 🎬 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):
                    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,
                    )
                    image_input = gr.Image(label="Upload an image", type="filepath")
                    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)
                    status_output_img = gr.Textbox(label="Status", interactive=False, visible=True)

            generate_img_btn.click(
                fn=generate_with_pro_auth_image,
                inputs=[img_prompt_input, image_input],
                outputs=[video_output_img, status_output_img],
                queue=False
            )
            
            gr.HTML("""<div style="text-align: center; margin-top: 40px; padding: 20px; border-top: 1px solid #e0e0e0;">
                <h3 style="color: #667eea;">Thank you for being a PRO user! 🤗</h3>
            </div>""")
        
        def control_access(profile: Optional[gr.OAuthProfile] = None, oauth_token: Optional[gr.OAuthToken] = None):
            if not profile:
                return gr.update(visible=False), gr.update(visible=False)
            if verify_pro_status(oauth_token):
                return gr.update(visible=True), gr.update(visible=False)
            else:
                message = "## ✨ Exclusive Access for PRO Users\n\nThis tool is available exclusively for Hugging Face **PRO** members."
                return gr.update(visible=False), gr.update(visible=True, value=message)
        
        demo.load(control_access, inputs=None, outputs=[main_interface, pro_message])
    
    return demo

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.launch(show_api=False, enable_monitoring=False, quiet=True, max_threads=10)