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import streamlit as st
from yt_dlp import YoutubeDL
from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO
@st.cache_resource
def load_model():
return pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch16")
def fetch_thumbnail(video_url):
ydl_opts = {
'skip_download': True,
'quiet': True,
'extract_flat': True,
'cookiefile': 'cookies.txt'
}
with YoutubeDL(ydl_opts) as ydl:
info = ydl.extract_info(video_url, download=False)
return info.get('title'), info.get('thumbnail')
def classify_thumbnail(model, thumbnail_url):
response = requests.get(thumbnail_url)
image = Image.open(BytesIO(response.content)).convert("RGB")
labels = ["real", "AI-generated", "manipulated", "deepfake"]
return model(image, candidate_labels=labels)
def main():
st.title("🎥 Video Integrity Checker")
video_url = st.text_input("Enter YouTube video URL:")
if st.button("Submit") and video_url:
try:
title, thumbnail = fetch_thumbnail(video_url)
st.success(f"Video Title: {title}")
if thumbnail:
st.image(thumbnail, caption="Video Thumbnail", use_container_width=True)
model = load_model()
with st.spinner("Analyzing..."):
results = classify_thumbnail(model, thumbnail)
st.subheader("Detection Results:")
for label, score in zip(results['labels'], results['scores']):
st.write(f"{label}: {score:.2%}")
else:
st.warning("No thumbnail found.")
except Exception as e:
st.error(f"Error: {e}")
if __name__ == "__main__":
main()
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