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