<|begin_of_box|>import gradio as gr
import numpy as np
import cv2
import tempfile
import os
from PIL import Image
import time

# Simulated outpainting function (in a real application, this would use a proper model)
def outpaint_video_with_reference(input_video, reference_image, outpaint_ratio=0.5):
    """
    Simulate video outpainting to match reference image height.
    In a real implementation, this would use a diffusion model or similar.
    """
    try:
        # Create temporary files for processing
        with tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) as temp_video:
            temp_video_path = temp_video.name
        
        with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as temp_ref:
            temp_ref_path = temp_ref.name
        
        # Save uploaded files
        with open(input_video, 'wb') as f:
            f.write(open(input_video, 'rb').read())
        
        with open(reference_image, 'wb') as f:
            f.write(open(reference_image, 'rb').read())
        
        # Get video and reference image dimensions
        cap = cv2.VideoCapture(input_video)
        ref_img = Image.open(reference_image)
        ref_height = ref_img.height
        ref_width = ref_img.width
        
        # Get original video dimensions
        original_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        original_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        
        # Calculate new dimensions to match reference height
        new_height = ref_height
        scale_factor = new_height / original_height
        new_width = int(original_width * scale_factor)
        
        # Create output video writer
        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
        out = cv2.VideoWriter(temp_video_path, fourcc, 30.0, (new_width, new_height))
        
        frame_count = 0
        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        
        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break
            
            # Resize frame to match new dimensions
            resized_frame = cv2.resize(frame, (new_width, new_height))
            
            # Simulate outpainting by adding a border that matches reference style
            # In a real implementation, this would use AI to generate content
            border_size = int(new_width * outpaint_ratio)
            if border_size > 0:
                # Create a border that tries to match the reference image style
                border_color = np.mean(resized_frame, axis=(0, 1))
                bordered_frame = cv2.copyMakeBorder(
                    resized_frame,
                    0, 0, border_size, border_size,
                    cv2.BORDER_CONSTANT,
                    value=border_color
                )
                out.write(bordered_frame)
            else:
                out.write(resized_frame)
            
            frame_count += 1
            if frame_count % 10 == 0:
                yield f"Processing frame {frame_count}/{total_frames}..."
        
        cap.release()
        out.release()
        
        # Return the processed video
        yield temp_video_path
        
    except Exception as e:
        yield f"Error: {str(e)}"
    finally:
        # Clean up temporary files
        try:
            if 'temp_video_path' in locals() and os.path.exists(temp_video_path):
                os.unlink(temp_video_path)
            if 'temp_ref_path' in locals() and os.path.exists(temp_ref_path):
                os.unlink(temp_ref_path)
        except:
            pass

# Create the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# 🎬 Video Outpainting with Reference Height Matching")
    gr.Markdown("Upload a video and a reference image. The video will be outpainted to match the height of the reference image.")
    
    with gr.Row():
        with gr.Column():
            gr.Markdown("### Input Video")
            video_input = gr.Video(label="Upload Video", type="filepath")
            gr.Markdown("### Reference Image")
            reference_input = gr.Image(label="Upload Reference Image", type="filepath")
            
            outpaint_ratio = gr.Slider(
                minimum=0.1,
                maximum=1.0,
                value=0.3,
                step=0.1,
                label="Outpaint Ratio (percentage of new width)"
            )
            
            process_button = gr.Button("Process Video", variant="primary")
            
        with gr.Column():
            gr.Markdown("### Output")
            output_video = gr.Video(label="Processed Video")
            status_text = gr.Textbox(label="Status", interactive=False)
    
    # Process function
    def process_video(video, reference, ratio):
        if not video or not reference:
            return None, "Please upload both video and reference image"
        
        status_text = ""
        for status in outpaint_video_with_reference(video, reference, ratio):
            if isinstance(status, str):
                status_text = status
                yield None, status_text
            else:
                yield status, "Processing complete!"
    
    process_button.click(
        fn=process_video,
        inputs=[video_input, reference_input, outpaint_ratio],
        outputs=[output_video, status_text],
        api_visibility="public"
    )

# Launch the app with modern theme
demo.launch(
    theme=gr.themes.Soft(
        primary_hue="blue",
        secondary_hue="indigo",
        neutral_hue="slate",
        font=gr.themes.GoogleFont("Inter"),
        text_size="lg",
        spacing_size="lg",
        radius_size="md"
    ).set(
        button_primary_background_fill="*primary_600",
        button_primary_background_fill_hover="*primary_700",
        block_title_text_weight="600",
    ),
    footer_links=[{"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"}]
)<|end_of_box|>