Update app.py

app.py

@@ -1,28 +1,57 @@
 import streamlit as st
-from pytube import YouTube
+import yt_dlp
+import torch
 from transformers import pipeline
-import tempfile
-import os
 
-st.title("Deepfake Video Detection")
+st.set_page_config(page_title="Video Deepfake Detector", layout="centered")
+
+# Download video using yt-dlp
+def download_video(video_url, output_path="video.mp4"):
+    ydl_opts = {
+        'format': 'best',
+        'outtmpl': output_path,
+        'quiet': True,
+    }
+    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+        ydl.download([video_url])
+    return output_path
+
+# Load the deepfake detection model
+def load_model():
+    return pipeline("image-classification", model="microsoft/resnet-50")
+
+# Analyze video frames for deepfake detection
+def analyze_video(video_path, model):
+    import cv2
+    cap = cv2.VideoCapture(video_path)
+    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    step = max(frame_count // 5, 1)
+    results = []
+    
+    for i in range(0, frame_count, step):
+        cap.set(cv2.CAP_PROP_POS_FRAMES, i)
+        ret, frame = cap.read()
+        if not ret:
+            continue
+        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        prediction = model(rgb_frame)
+        results.append(prediction[0])
+    cap.release()
+    return results
+
+# Streamlit interface
+st.title("🎥 Video Deepfake Detector")
 video_url = st.text_input("Enter YouTube Video URL:")
+
 if st.button("Submit") and video_url:
     with st.spinner("Downloading and analyzing video..."):
         try:
-            yt = YouTube(video_url)
-            stream = yt.streams.filter(file_extension='mp4', progressive=True).first()
-            temp_dir = tempfile.mkdtemp()
-            video_path = os.path.join(temp_dir, "video.mp4")
-            stream.download(output_path=temp_dir, filename="video.mp4")
-            
-            model = pipeline("image-classification", model="facebook/deit-base-distilled-patch16-224")
-            results = model(video_path)
-            
-            st.success("Analysis Complete")
-            st.write("Prediction:", results[0]['label'])
-            st.write("Confidence:", f"{results[0]['score'] * 100:.2f}%")
+            video_path = download_video(video_url)
+            model = load_model()
+            predictions = analyze_video(video_path, model)
 
-            os.remove(video_path)
-            os.rmdir(temp_dir)
+            st.success("Analysis Complete!")
+            for idx, pred in enumerate(predictions):
+                st.write(f"Frame {idx + 1}: {pred['label']} with confidence {pred['score']:.2f}")
         except Exception as e:
             st.error(f"Error: {e}")