Update Dockerfile

Dockerfile

@@ -1,50 +1,579 @@
-# BackgroundFX Pro v2.1.0 - Optimized Dockerfile for HuggingFace Spaces
-FROM python:3.10-slim
-
-# Set environment variables for optimization
-ENV PYTHONUNBUFFERED=1 \
-    PYTHONDONTWRITEBYTECODE=1 \
-    PIP_NO_CACHE_DIR=1 \
-    PIP_DISABLE_PIP_VERSION_CHECK=1 \
-    DEBIAN_FRONTEND=noninteractive \
-    PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512 \
-    CUDA_LAUNCH_BLOCKING=0 \
-    OMP_NUM_THREADS=4
-
-# Install system dependencies
-RUN apt-get update && apt-get install -y \
-    ffmpeg \
-    git \
-    wget \
-    curl \
-    build-essential \
-    && rm -rf /var/lib/apt/lists/*
-
-# Set working directory
-WORKDIR /app
-
-# Copy requirements first for better caching
-COPY requirements.txt .
-
-# Install Python dependencies
-RUN pip install --no-cache-dir -r requirements.txt
-
-# Copy application files
-COPY app.py .
-COPY startup.sh .
-
-# Make startup script executable
-RUN chmod +x startup.sh
-
-# Create necessary directories
-RUN mkdir -p /tmp/backgroundfx_pro/{uploads,outputs,temp,models}
-
-# Expose port
-EXPOSE 7860
-
-# Health check
-HEALTHCHECK --interval=30s --timeout=10s --start-period=5m --retries=3 \
-    CMD curl -f http://localhost:7860/health || exit 1
-
-# Use startup script
-CMD ["./startup.sh"]
+#!/usr/bin/env python3
+"""
+High-Quality Video Background Replacement
+Upload video → Choose professional background → Replace with cinema quality
+"""
+
+import os
+import sys
+import tempfile
+import cv2
+import numpy as np
+from pathlib import Path
+import gradio as gr
+import torch
+import requests
+from PIL import Image, ImageDraw
+import json
+
+# Suppress warnings and optimize for quality
+import warnings
+warnings.filterwarnings("ignore")
+os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:1024'
+os.environ['CUDA_LAUNCH_BLOCKING'] = '0'
+
+# Global variables for models
+sam2_predictor = None
+matanyone_model = None
+models_loaded = False
+
+# Professional background templates
+PROFESSIONAL_BACKGROUNDS = {
+    "office_modern": {
+        "name": "Modern Office",
+        "type": "gradient", 
+        "colors": ["#f8f9fa", "#e9ecef", "#dee2e6"],
+        "direction": "diagonal"
+    },
+    "office_executive": {
+        "name": "Executive Office",
+        "type": "gradient",
+        "colors": ["#2c3e50", "#34495e", "#5d6d7e"],
+        "direction": "vertical"
+    },
+    "studio_blue": {
+        "name": "Professional Blue",
+        "type": "gradient",
+        "colors": ["#1e3c72", "#2a5298", "#3498db"],
+        "direction": "radial"
+    },
+    "studio_green": {
+        "name": "Broadcast Green",
+        "type": "color",
+        "colors": ["#00b894"],
+        "chroma_key": True
+    },
+    "conference": {
+        "name": "Conference Room",
+        "type": "gradient",
+        "colors": ["#74b9ff", "#0984e3", "#6c5ce7"],
+        "direction": "horizontal"
+    },
+    "minimalist": {
+        "name": "Minimalist White",
+        "type": "gradient",
+        "colors": ["#ffffff", "#f1f2f6", "#ddd"],
+        "direction": "soft_radial"
+    },
+    "warm_gradient": {
+        "name": "Warm Sunset",
+        "type": "gradient",
+        "colors": ["#ff7675", "#fd79a8", "#fdcb6e"],
+        "direction": "diagonal"
+    },
+    "cool_gradient": {
+        "name": "Cool Ocean",
+        "type": "gradient", 
+        "colors": ["#74b9ff", "#0984e3", "#00cec9"],
+        "direction": "vertical"
+    },
+    "corporate": {
+        "name": "Corporate Navy",
+        "type": "gradient",
+        "colors": ["#2d3436", "#636e72", "#74b9ff"],
+        "direction": "radial"
+    },
+    "creative": {
+        "name": "Creative Purple",
+        "type": "gradient",
+        "colors": ["#6c5ce7", "#a29bfe", "#fd79a8"],
+        "direction": "diagonal"
+    }
+}
+
+def download_and_setup_models():
+    """Download and setup SAM2 and MatAnyone models with quality optimizations"""
+    global sam2_predictor, matanyone_model, models_loaded
+    
+    if models_loaded:
+        return "✅ High-quality models already loaded"
+    
+    try:
+        # Download SAM2 if needed
+        sam2_checkpoint = "/tmp/sam2_hiera_large.pt"
+        if not os.path.exists(sam2_checkpoint):
+            print("📥 Downloading SAM2 large model for maximum quality...")
+            url = "https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_large.pt"
+            response = requests.get(url, stream=True)
+            total_size = int(response.headers.get('content-length', 0))
+            downloaded = 0
+            
+            with open(sam2_checkpoint, 'wb') as f:
+                for chunk in response.iter_content(chunk_size=8192):
+                    f.write(chunk)
+                    downloaded += len(chunk)
+                    if total_size > 0:
+                        percent = (downloaded / total_size) * 100
+                        print(f"Download progress: {percent:.1f}%")
+        
+        # Setup SAM2 with quality settings
+        sys.path.append('/tmp/segment-anything-2')
+        from sam2.build_sam import build_sam2
+        from sam2.sam2_image_predictor import SAM2ImagePredictor
+        
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        print(f"🚀 Loading SAM2 on {device} for maximum quality...")
+        
+        sam2_model = build_sam2("sam2_hiera_large.yaml", sam2_checkpoint, device=device)
+        sam2_predictor = SAM2ImagePredictor(sam2_model)
+        
+        # Setup MatAnyone with quality optimizations
+        sys.path.append('/tmp/MatAnyone')
+        from inference import MatAnyoneInference
+        
+        print("🎨 Loading MatAnyone for cinema-quality matting...")
+        matanyone_model = MatAnyoneInference()
+        
+        models_loaded = True
+        gpu_info = f" (GPU: {torch.cuda.get_device_name(0)})" if torch.cuda.is_available() else " (CPU)"
+        return f"✅ High-quality models loaded successfully!{gpu_info}"
+        
+    except Exception as e:
+        return f"❌ Error loading models: {e}"
+
+def segment_person_hq(image):
+    """High-quality person segmentation using SAM2"""
+    # Set image with quality optimizations
+    sam2_predictor.set_image(image)
+    
+    h, w = image.shape[:2]
+    
+    # Use multiple points for better segmentation
+    points = np.array([
+        [w//2, h//2],           # Center
+        [w//2, h//3],           # Upper body
+        [w//2, 2*h//3],         # Lower body
+        [w//3, h//2],           # Left side
+        [2*w//3, h//2],         # Right side
+    ])
+    labels = np.array([1, 1, 1, 1, 1])  # All positive points
+    
+    # Predict with high quality settings
+    masks, scores, _ = sam2_predictor.predict(
+        point_coords=points,
+        point_labels=labels,
+        multimask_output=True
+    )
+    
+    # Select best mask and apply smoothing
+    best_mask = masks[np.argmax(scores)]
+    
+    # Smooth mask edges for better quality
+    kernel = np.ones((3,3), np.uint8)
+    best_mask = cv2.morphologyEx(best_mask.astype(np.uint8), cv2.MORPH_CLOSE, kernel)
+    best_mask = cv2.GaussianBlur(best_mask.astype(np.float32), (3,3), 1.0)
+    
+    return (best_mask * 255).astype(np.uint8)
+
+def refine_mask_hq(image, mask):
+    """Cinema-quality mask refinement using MatAnyone"""
+    # Apply edge-preserving filtering before MatAnyone
+    image_filtered = cv2.bilateralFilter(image, 9, 75, 75)
+    
+    # Use MatAnyone for professional matting
+    refined_mask = matanyone_model.infer(image_filtered, mask)
+    
+    # Post-process for smooth edges
+    refined_mask = cv2.medianBlur(refined_mask, 3)
+    
+    return refined_mask
+
+def create_professional_background(bg_config, width, height):
+    """Create professional background based on configuration"""
+    if bg_config["type"] == "color":
+        # Solid color
+        color_hex = bg_config["colors"][0].lstrip('#')
+        color_rgb = tuple(int(color_hex[i:i+2], 16) for i in (0, 2, 4))
+        color_bgr = color_rgb[::-1]  # Convert to BGR
+        background = np.full((height, width, 3), color_bgr, dtype=np.uint8)
+        
+    elif bg_config["type"] == "gradient":
+        background = create_gradient_background(bg_config, width, height)
+    
+    return background
+
+def create_gradient_background(bg_config, width, height):
+    """Create high-quality gradient backgrounds"""
+    colors = bg_config["colors"]
+    direction = bg_config.get("direction", "vertical")
+    
+    # Convert hex colors to RGB
+    rgb_colors = []
+    for color_hex in colors:
+        color_hex = color_hex.lstrip('#')
+        rgb = tuple(int(color_hex[i:i+2], 16) for i in (0, 2, 4))
+        rgb_colors.append(rgb)
+    
+    # Create PIL image for high-quality gradients
+    pil_img = Image.new('RGB', (width, height))
+    draw = ImageDraw.Draw(pil_img)
+    
+    if direction == "vertical":
+        # Vertical gradient
+        for y in range(height):
+            # Interpolate between colors
+            progress = y / height
+            if len(rgb_colors) == 2:
+                r = int(rgb_colors[0][0] + (rgb_colors[1][0] - rgb_colors[0][0]) * progress)
+                g = int(rgb_colors[0][1] + (rgb_colors[1][1] - rgb_colors[0][1]) * progress)
+                b = int(rgb_colors[0][2] + (rgb_colors[1][2] - rgb_colors[0][2]) * progress)
+            else:
+                # Multi-color gradient
+                segment = progress * (len(rgb_colors) - 1)
+                idx = int(segment)
+                local_progress = segment - idx
+                
+                if idx >= len(rgb_colors) - 1:
+                    r, g, b = rgb_colors[-1]
+                else:
+                    c1, c2 = rgb_colors[idx], rgb_colors[idx + 1]
+                    r = int(c1[0] + (c2[0] - c1[0]) * local_progress)
+                    g = int(c1[1] + (c2[1] - c1[1]) * local_progress)
+                    b = int(c1[2] + (c2[2] - c1[2]) * local_progress)
+            
+            draw.line([(0, y), (width, y)], fill=(r, g, b))
+    
+    elif direction == "horizontal":
+        # Horizontal gradient
+        for x in range(width):
+            progress = x / width
+            if len(rgb_colors) == 2:
+                r = int(rgb_colors[0][0] + (rgb_colors[1][0] - rgb_colors[0][0]) * progress)
+                g = int(rgb_colors[0][1] + (rgb_colors[1][1] - rgb_colors[0][1]) * progress)
+                b = int(rgb_colors[0][2] + (rgb_colors[1][2] - rgb_colors[0][2]) * progress)
+            else:
+                segment = progress * (len(rgb_colors) - 1)
+                idx = int(segment)
+                local_progress = segment - idx
+                
+                if idx >= len(rgb_colors) - 1:
+                    r, g, b = rgb_colors[-1]
+                else:
+                    c1, c2 = rgb_colors[idx], rgb_colors[idx + 1]
+                    r = int(c1[0] + (c2[0] - c1[0]) * local_progress)
+                    g = int(c1[1] + (c2[1] - c1[1]) * local_progress)
+                    b = int(c1[2] + (c2[2] - c1[2]) * local_progress)
+            
+            draw.line([(x, 0), (x, height)], fill=(r, g, b))
+    
+    elif direction == "diagonal":
+        # Diagonal gradient
+        for y in range(height):
+            for x in range(width):
+                progress = (x + y) / (width + height)
+                progress = min(1.0, progress)
+                
+                if len(rgb_colors) == 2:
+                    r = int(rgb_colors[0][0] + (rgb_colors[1][0] - rgb_colors[0][0]) * progress)
+                    g = int(rgb_colors[0][1] + (rgb_colors[1][1] - rgb_colors[0][1]) * progress)
+                    b = int(rgb_colors[0][2] + (rgb_colors[1][2] - rgb_colors[0][2]) * progress)
+                else:
+                    segment = progress * (len(rgb_colors) - 1)
+                    idx = int(segment)
+                    local_progress = segment - idx
+                    
+                    if idx >= len(rgb_colors) - 1:
+                        r, g, b = rgb_colors[-1]
+                    else:
+                        c1, c2 = rgb_colors[idx], rgb_colors[idx + 1]
+                        r = int(c1[0] + (c2[0] - c1[0]) * local_progress)
+                        g = int(c1[1] + (c2[1] - c1[1]) * local_progress)
+                        b = int(c1[2] + (c2[2] - c1[2]) * local_progress)
+                
+                pil_img.putpixel((x, y), (r, g, b))
+    
+    elif direction in ["radial", "soft_radial"]:
+        # Radial gradient
+        center_x, center_y = width // 2, height // 2
+        max_distance = np.sqrt(center_x**2 + center_y**2)
+        
+        for y in range(height):
+            for x in range(width):
+                distance = np.sqrt((x - center_x)**2 + (y - center_y)**2)
+                progress = distance / max_distance
+                progress = min(1.0, progress)
+                
+                if direction == "soft_radial":
+                    progress = progress**0.7  # Softer falloff
+                
+                if len(rgb_colors) == 2:
+                    r = int(rgb_colors[0][0] + (rgb_colors[1][0] - rgb_colors[0][0]) * progress)
+                    g = int(rgb_colors[0][1] + (rgb_colors[1][1] - rgb_colors[0][1]) * progress)
+                    b = int(rgb_colors[0][2] + (rgb_colors[1][2] - rgb_colors[0][2]) * progress)
+                else:
+                    segment = progress * (len(rgb_colors) - 1)
+                    idx = int(segment)
+                    local_progress = segment - idx
+                    
+                    if idx >= len(rgb_colors) - 1:
+                        r, g, b = rgb_colors[-1]
+                    else:
+                        c1, c2 = rgb_colors[idx], rgb_colors[idx + 1]
+                        r = int(c1[0] + (c2[0] - c1[0]) * local_progress)
+                        g = int(c1[1] + (c2[1] - c1[1]) * local_progress)
+                        b = int(c1[2] + (c2[2] - c1[2]) * local_progress)
+                
+                pil_img.putpixel((x, y), (r, g, b))
+    
+    # Convert PIL to OpenCV format
+    background = cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR)
+    return background
+
+def replace_background_hq(frame, mask, background):
+    """High-quality background replacement with edge feathering"""
+    # Resize background to match frame exactly
+    background = cv2.resize(background, (frame.shape[1], frame.shape[0]), interpolation=cv2.INTER_LANCZOS4)
+    
+    # Apply edge feathering for smooth transitions
+    mask_float = mask.astype(np.float32) / 255.0
+    
+    # Create feathered mask
+    feather_radius = 3
+    mask_feathered = cv2.GaussianBlur(mask_float, (feather_radius*2+1, feather_radius*2+1), feather_radius/3)
+    
+    # Expand mask to 3 channels
+    mask_3channel = np.stack([mask_feathered] * 3, axis=2)
+    
+    # High-quality compositing with gamma correction
+    frame_linear = np.power(frame.astype(np.float32) / 255.0, 2.2)
+    background_linear = np.power(background.astype(np.float32) / 255.0, 2.2)
+    
+    # Composite in linear space
+    result_linear = frame_linear * mask_3channel + background_linear * (1 - mask_3channel)
+    
+    # Convert back to sRGB
+    result = np.power(result_linear, 1/2.2) * 255.0
+    result = np.clip(result, 0, 255).astype(np.uint8)
+    
+    return result
+
+def process_video_hq(video_path, background_choice, custom_background_path, progress=gr.Progress()):
+    """High-quality video processing with professional backgrounds"""
+    if not models_loaded:
+        return None, "❌ Models not loaded. Click 'Load Models' first."
+    
+    try:
+        progress(0, desc="🎬 Initializing high-quality processing...")
+        
+        # Read video with quality settings
+        cap = cv2.VideoCapture(video_path)
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        
+        # Prepare background
+        if background_choice == "custom" and custom_background_path:
+            # Use uploaded image
+            background = cv2.imread(custom_background_path)
+            if background is None:
+                return None, "❌ Could not read custom background image"
+            background_name = "Custom Image"
+        else:
+            # Use professional background
+            if background_choice in PROFESSIONAL_BACKGROUNDS:
+                bg_config = PROFESSIONAL_BACKGROUNDS[background_choice]
+                background = create_professional_background(bg_config, frame_width, frame_height)
+                background_name = bg_config["name"]
+            else:
+                return None, "❌ Invalid background selection"
+        
+        # Setup high-quality output video
+        output_path = "/tmp/processed_video_hq.mp4"
+        # Use high-quality codec
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
+        
+        progress(0.1, desc=f"🎨 Using {background_name} background...")
+        
+        # Process each frame with quality optimizations
+        frame_count = 0
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            
+            # Update progress
+            progress_pct = 0.1 + (frame_count / total_frames) * 0.8
+            progress(progress_pct, desc=f"✨ Processing frame {frame_count + 1}/{total_frames} (High Quality)")
+            
+            # High-quality person segmentation
+            mask = segment_person_hq(frame)
+            
+            # Cinema-quality mask refinement
+            refined_mask = refine_mask_hq(frame, mask)
+            
+            # High-quality background replacement
+            result_frame = replace_background_hq(frame, refined_mask, background)
+            
+            # Write frame
+            out.write(result_frame)
+            frame_count += 1
+        
+        cap.release()
+        out.release()
+        
+        progress(0.9, desc="🎵 Adding high-quality audio...")
+        
+        # Add audio back with high quality settings
+        final_output = "/tmp/final_output_hq.mp4"
+        audio_cmd = f'ffmpeg -y -i {output_path} -i {video_path} -c:v libx264 -crf 18 -preset slow -c:a aac -b:a 192k -map 0:v:0 -map 1:a:0? -shortest {final_output}'
+        os.system(audio_cmd)
+        
+        # Save to MyAvatar/My Videos
+        myavatar_path = "/tmp/MyAvatar/My_Videos/"
+        os.makedirs(myavatar_path, exist_ok=True)
+        
+        import shutil
+        import time
+        saved_filename = f"hq_background_replaced_{int(time.time())}.mp4"
+        saved_path = os.path.join(myavatar_path, saved_filename)
+        shutil.copy2(final_output, saved_path)
+        
+        progress(1.0, desc="✅ High-quality processing complete!")
+        
+        return final_output, f"✅ High-Quality Success!\n🎬 Background: {background_name}\n📁 Saved: MyAvatar/My Videos/{saved_filename}\n🎯 Quality: Cinema-grade with SAM2 + MatAnyone"
+        
+    except Exception as e:
+        return None, f"❌ Error: {str(e)}"
+
+def get_model_status():
+    """Get current model loading status"""
+    if models_loaded:
+        gpu_info = f" (GPU: {torch.cuda.get_device_name(0)})" if torch.cuda.is_available() else " (CPU)"
+        return f"✅ High-quality models loaded{gpu_info}"
+    else:
+        return "⏳ Models not loaded. Click 'Load Models' for cinema-quality processing."
+
+def create_interface():
+    """Create enhanced Gradio interface with professional backgrounds"""
+    
+    # Create background choices
+    bg_choices = ["custom"] + list(PROFESSIONAL_BACKGROUNDS.keys())
+    bg_labels = ["📷 Custom Image"] + [f"🎨 {config['name']}" for config in PROFESSIONAL_BACKGROUNDS.values()]
+    bg_dropdown_choices = list(zip(bg_labels, bg_choices))
+    
+    with gr.Blocks(title="High-Quality Video Background Replacement", theme=gr.themes.Soft()) as demo:
+        gr.Markdown("# 🎬 Cinema-Quality Video Background Replacement")
+        gr.Markdown("**Professional background replacement using SAM2 + MatAnyone AI models**")
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown("### 📥 Input")
+                video_input = gr.Video(label="🎥 Upload Video (MP4, MOV, AVI)")
+                
+                gr.Markdown("### 🎨 Background Selection")
+                background_choice = gr.Dropdown(
+                    choices=bg_dropdown_choices,
+                    value="office_modern",
+                    label="Choose Background Type",
+                    info="Select professional background or upload custom image"
+                )
+                
+                custom_background = gr.Image(
+                    label="📷 Custom Background Image", 
+                    type="filepath",
+                    visible=False,
+                    info="Upload your own background image (will be resized to match video)"
+                )
+                
+                # Show/hide custom background based on selection
+                def toggle_custom_bg(choice):
+                    return gr.update(visible=(choice == "custom"))
+                
+                background_choice.change(
+                    fn=toggle_custom_bg,
+                    inputs=background_choice,
+                    outputs=custom_background
+                )
+                
+                with gr.Row():
+                    load_models_btn = gr.Button("🚀 Load High-Quality Models", variant="secondary", size="lg")
+                    process_btn = gr.Button("✨ Process with Cinema Quality", variant="primary", size="lg")
+                
+                status_text = gr.Textbox(
+                    label="🔧 System Status", 
+                    value=get_model_status(), 
+                    interactive=False,
+                    lines=2
+                )
+            
+            with gr.Column(scale=1):
+                gr.Markdown("### 📤 High-Quality Output")
+                video_output = gr.Video(label="🎬 Processed Video", height=400)
+                result_text = gr.Textbox(
+                    label="📊 Processing Results", 
+                    interactive=False,
+                    lines=4
+                )
+                
+                gr.Markdown("### 🎨 Professional Backgrounds Available")
+                bg_preview_html = "<div style='display: grid; grid-template-columns: repeat(2, 1fr); gap: 10px; padding: 10px;'>"
+                for key, config in PROFESSIONAL_BACKGROUNDS.items():
+                    colors_display = " → ".join(config["colors"][:2])
+                    bg_preview_html += f"""
+                    <div style='padding: 8px; border: 1px solid #ddd; border-radius: 8px; text-align: center; background: linear-gradient(45deg, {config["colors"][0]}, {config["colors"][-1]});'>
+                        <strong style='color: white; text-shadow: 1px 1px 2px rgba(0,0,0,0.7);'>{config["name"]}</strong>
+                    </div>
+                    """
+                bg_preview_html += "</div>"
+                gr.HTML(bg_preview_html)
+        
+        # Event handlers
+        load_models_btn.click(
+            fn=download_and_setup_models,
+            outputs=status_text
+        )
+        
+        process_btn.click(
+            fn=process_video_hq,
+            inputs=[video_input, background_choice, custom_background],
+            outputs=[video_output, result_text]
+        )
+        
+        # Info section
+        with gr.Accordion("ℹ️ Quality & Features", open=False):
+            gr.Markdown("""
+            ### 🏆 Cinema-Quality Features:
+            - **🤖 SAM2 Large Model**: Meta's most advanced segmentation
+            - **🎨 MatAnyone**: CVPR 2025 professional matting
+            - **✨ Edge Feathering**: Smooth, natural transitions  
+            - **🎬 Gamma Correction**: Professional color compositing
+            - **🎵 High-Quality Audio**: 192kbps AAC preservation
+            - **📺 H.264 Codec**: CRF 18 for broadcast quality
+            
+            ### 🎨 Professional Backgrounds:
+            - **Office Environments**: Modern, Executive styles
+            - **Studio Backdrops**: Broadcast-quality gradients
+            - **Creative Themes**: Artistic color combinations
+            - **Custom Images**: Upload your own backgrounds
+            
+            ### 💾 Output:
+            - Saved to: **MyAvatar/My Videos/**
+            - Format: **MP4 (H.264)**
+            - Quality: **Cinema-grade**
+            """)
+    
+    return demo
+
+if __name__ == "__main__":
+    print("🎬 Starting Cinema-Quality Video Background Replacement...")
+    
+    # Create and launch interface
+    demo = create_interface()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True,
+        show_error=True
+    )