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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()
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