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import streamlit as st
from transformers import pipeline
from PIL import Image
import requests
from urllib.parse import urlparse, parse_qs
from io import BytesIO
# Initialize the deepfake detection model
@st.cache_resource
def load_model():
return pipeline("image-classification", model="Wvolf/ViT_Deepfake_Detection")
model = load_model()
def get_thumbnail_url(video_url):
"""
Extracts the YouTube video ID and returns the thumbnail URL.
"""
parsed_url = urlparse(video_url)
video_id = None
if 'youtube' in parsed_url.netloc:
query_params = parse_qs(parsed_url.query)
video_id = query_params.get('v', [None])[0]
elif 'youtu.be' in parsed_url.netloc:
video_id = parsed_url.path.lstrip('/')
if video_id:
return f"https://img.youtube.com/vi/{video_id}/maxresdefault.jpg"
return None
def analyze_thumbnail(thumbnail_url):
"""
Downloads the thumbnail image and analyzes it using the deepfake detection model.
"""
response = requests.get(thumbnail_url)
if response.status_code == 200:
image = Image.open(BytesIO(response.content)).convert("RGB")
results = model(image)
return results, image
else:
st.error("Failed to retrieve the thumbnail image.")
return None, None
# Streamlit UI
st.title("Deepfake Detection from YouTube Thumbnails")
video_url = st.text_input("Enter YouTube Video URL:")
if st.button("Analyze"):
if video_url:
thumbnail_url = get_thumbnail_url(video_url)
if thumbnail_url:
results, image = analyze_thumbnail(thumbnail_url)
if results and image:
st.image(image, caption="YouTube Video Thumbnail", use_column_width=True)
st.subheader("Detection Results:")
for result in results:
label = result['label']
confidence = result['score'] * 100
st.write(f"**{label}**: {confidence:.2f}%")
else:
st.error("Could not analyze the thumbnail.")
else:
st.error("Invalid YouTube URL. Please enter a valid URL.")
else:
st.warning("Please enter a YouTube video URL.")
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