Create app.py

app.py

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+import cv2
+import os
+import torch
+from transformers import pipeline
+from moviepy.editor import VideoFileClip
+
+def extract_keyframes(video_path, interval=5):
+    cap = cv2.VideoCapture(video_path)
+    frame_rate = int(cap.get(cv2.CAP_PROP_FPS))
+    keyframes = []
+    frame_count = 0
+    
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        if frame_count % (frame_rate * interval) == 0:
+            keyframes.append(frame)
+        frame_count += 1
+    
+    cap.release()
+    return keyframes
+
+def generate_captions(frames):
+    caption_generator = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
+    captions = [caption_generator(frame)[0]['generated_text'] for frame in frames]
+    return captions
+
+def summarize_text(texts):
+    summarizer = pipeline("summarization")
+    summary = summarizer(" ".join(texts), max_length=50, min_length=10, do_sample=False)
+    return summary[0]['summary_text']
+
+def summarize_video(video_path):
+    frames = extract_keyframes(video_path)
+    captions = generate_captions(frames)
+    summary = summarize_text(captions)
+    return summary
+
+if __name__ == "__main__":
+    video_path = "input.mp4"  # Change to your video file
+    summary = summarize_video(video_path)
+    print("Video Summary:", summary)