Create app.py

app.py

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+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()