Upload fake_video_detector.py

fake_video_detector.py

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+# fake_video_detector.py
+
+import streamlit as st
+import requests
+from PIL import Image
+from io import BytesIO
+from transformers import pipeline
+
+def extract_thumbnail_url(youtube_url):
+    """Extracts the thumbnail URL from a YouTube video link."""
+    video_id = youtube_url.split("v=")[-1].split("&")[0]
+    return f"https://img.youtube.com/vi/{video_id}/maxresdefault.jpg"
+
+def load_image(url):
+    """Loads an image from a URL."""
+    response = requests.get(url)
+    if response.status_code == 200:
+        return Image.open(BytesIO(response.content))
+    return None
+
+def main():
+    st.title("🔎 YouTube Fake Video Detector")
+    st.write("Enter a YouTube video link to detect if its thumbnail is AI-generated or manipulated.")
+
+    youtube_url = st.text_input("YouTube Video Link")
+
+    if youtube_url:
+        thumbnail_url = extract_thumbnail_url(youtube_url)
+        st.subheader("Thumbnail Preview:")
+        image = load_image(thumbnail_url)
+
+        if image:
+            st.image(image, caption="Video Thumbnail", use_column_width=True)
+
+            with st.spinner("Analyzing thumbnail..."):
+                # Load a pretrained model for image classification (can be replaced with a custom model)
+                model = pipeline("image-classification", model="nateraw/resnet50-oxford-flowers")
+                results = model(thumbnail_url)
+
+            st.subheader("Detection Results:")
+            for result in results:
+                st.write(f"**{result['label']}**: {result['score']*100:.2f}% confidence")
+        else:
+            st.error("Failed to load thumbnail. Please check the YouTube link.")
+
+if __name__ == "__main__":
+    main()