import gradio as gr
import cv2
import numpy as np
from framevis import FrameVis

def process_video(video_path, nframes, height, width, direction, trim, average, blur_amount):
    """Process video using FrameVis and return the visualization"""
    try:
        fv = FrameVis()
        
        # Process the video
        output_image = fv.visualize(
            video_path, 
            nframes=nframes,
            height=height if height > 0 else None,
            width=width if width > 0 else None,
            direction=direction,
            trim=trim,
            quiet=False
        )
        
        # Apply post-processing if requested
        if average:
            output_image = fv.average_image(output_image, direction)
        elif blur_amount > 0:
            output_image = fv.motion_blur(output_image, direction, blur_amount)
        
        # Convert from BGR to RGB for Gradio
        output_image = cv2.cvtColor(output_image, cv2.COLOR_BGR2RGB)
        return output_image
        
    except Exception as e:
        raise gr.Error(str(e))

# Create the Gradio interface
with gr.Blocks(title="FrameVis - Video Frame Visualizer") as demo:
    gr.Markdown("""
    # 🎬 FrameVis - Video Frame Visualizer
    Upload a video to create a beautiful visualization of its frames. The tool will extract frames at regular intervals 
    and combine them into a single image.
    """)
    
    with gr.Row():
        with gr.Column(scale=1):
            # Input components
            video_input = gr.Video(label="Upload Video")
            with gr.Row():
                nframes = gr.Slider(minimum=1, maximum=500, value=100, step=1, 
                                 label="Number of Frames")
                direction = gr.Radio(["horizontal", "vertical"], value="horizontal",
                                   label="Direction")
            
            with gr.Row():
                height = gr.Number(value=0, label="Frame Height (0 for auto)")
                width = gr.Number(value=0, label="Frame Width (0 for auto)")
            
            with gr.Row():
                trim = gr.Checkbox(label="Auto-trim black bars")
                average = gr.Checkbox(label="Average colors")
                blur_amount = gr.Slider(minimum=0, maximum=200, value=0, step=1,
                                     label="Motion Blur Amount")
            
            process_btn = gr.Button("Generate Visualization", variant="primary")
        
        with gr.Column(scale=2):
            # Output component
            output_image = gr.Image(label="Visualization Result", height=300)
            
    # Handle processing
    process_btn.click(
        fn=process_video,
        inputs=[
            video_input,
            nframes,
            height,
            width,
            direction,
            trim,
            average,
            blur_amount
        ],
        outputs=output_image
    )

if __name__ == "__main__":
    demo.launch()