app.py

import gradio as gr
import cv2
import numpy as np
from transformers import pipeline

# Load a video enhancement model from Hugging Face
# Replace 'your-model-name' with the actual model you want to use
video_enhancer = pipeline("image-enhancement", model="your-model-name")

def enhance_video(video_path):
    # Open the video file
    cap = cv2.VideoCapture(video_path)
    frames = []
    
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        # Enhance the frame using the model
        enhanced_frame = video_enhancer(frame)
        frames.append(enhanced_frame)

    cap.release()
    
    # Save the enhanced video
    output_path = "enhanced_video.mp4"
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(output_path, fourcc, 30.0, (frames[0].shape[1], frames[0].shape[0]))

    for frame in frames:
        out.write(frame)
    
    out.release()
    return output_path

# Create a Gradio interface
iface = gr.Interface(
    fn=enhance_video,
    inputs=gr.inputs.Video(label="Upload Video"),
    outputs=gr.outputs.Video(label="Enhanced Video"),
    title="Video Enhancer",
    description="Upload a video to enhance its quality using a Hugging Face model."
)

# Launch the interface
iface.launch()