|
|
from pytube import YouTube |
|
|
import streamlit as st |
|
|
from transformers import pipeline |
|
|
from PIL import Image |
|
|
import requests |
|
|
from io import BytesIO |
|
|
|
|
|
st.set_page_config(page_title="Video Deepfake Detector", layout="centered") |
|
|
st.title("🎥 Video Deepfake Detector") |
|
|
|
|
|
@st.cache_data |
|
|
def get_thumbnail(url): |
|
|
try: |
|
|
yt = YouTube(url) |
|
|
response = requests.get(yt.thumbnail_url) |
|
|
if response.status_code == 200: |
|
|
return Image.open(BytesIO(response.content)) |
|
|
except Exception as e: |
|
|
st.error(f"Error fetching thumbnail: {e}") |
|
|
return None |
|
|
|
|
|
@st.cache_resource |
|
|
def load_model(): |
|
|
return pipeline("image-classification", model="facebook/deit-base-distilled-patch16-224") |
|
|
|
|
|
def detect_deepfake(image, model): |
|
|
results = model(image) |
|
|
return results |
|
|
|
|
|
def main(): |
|
|
video_url = st.text_input("Enter YouTube Video URL:") |
|
|
if st.button("Analyze") and video_url: |
|
|
thumbnail = get_thumbnail(video_url) |
|
|
if thumbnail: |
|
|
st.image(thumbnail, caption="Video Thumbnail", use_container_width=True) |
|
|
model = load_model() |
|
|
results = detect_deepfake(thumbnail, model) |
|
|
st.subheader("Detection Results:") |
|
|
for res in results: |
|
|
st.write(f"{res['label']}: {res['score']:.4f}") |
|
|
else: |
|
|
st.warning("Unable to fetch thumbnail. Please check the video URL.") |
|
|
|
|
|
if __name__ == "__main__": |
|
|
main() |
|
|
|
