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import streamlit as st
from transformers import pipeline
from pytube import YouTube
import tempfile
from moviepy.editor import VideoFileClip
from PIL import Image
import requests
from io import BytesIO
st.set_page_config(page_title="AI Media Verifier", layout="wide")
@st.cache_resource
def load_image_model():
return pipeline("image-classification", model="google/vit-base-patch16-224")
@st.cache_resource
def load_video_model():
return pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
def detect_image(image):
model = load_image_model()
return model(image)
def detect_video(video_url):
yt = YouTube(video_url)
stream = yt.streams.filter(file_extension='mp4', progressive=True).order_by('resolution').desc().first()
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmp:
stream.download(filename=tmp.name)
clip = VideoFileClip(tmp.name).subclip(0, min(10, yt.length))
frame = clip.get_frame(clip.duration / 2)
image = Image.fromarray(frame)
return detect_image(image)
st.title("AI Media Verifier: Image & Video Authenticity")
option = st.radio("Choose input type:", ("Image", "Video"))
if option == "Image":
uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
if uploaded_image:
image = Image.open(uploaded_image)
st.image(image, caption="Uploaded Image", use_container_width=True)
with st.spinner("Analyzing image..."):
results = detect_image(uploaded_image)
st.success("Analysis Complete")
for res in results:
st.write(f"**{res['label']}**: {res['score']*100:.2f}%")
elif option == "Video":
video_url = st.text_input("Enter YouTube Video Link")
if video_url:
st.video(video_url)
with st.spinner("Analyzing video..."):
results = detect_video(video_url)
st.success("Video Analysis Complete")
for res in results:
st.write(f"**{res['label']}**: {res['score']*100:.2f}%")
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