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