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import streamlit as st |
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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 urllib.parse import urlparse, parse_qs |
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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("image-classification", model="Wvolf/ViT_Deepfake_Detection") |
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model = load_model() |
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def get_thumbnail_url(video_url): |
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""" |
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Extracts the YouTube video ID and returns the thumbnail URL. |
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""" |
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parsed_url = urlparse(video_url) |
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video_id = None |
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if 'youtube' in parsed_url.netloc: |
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query_params = parse_qs(parsed_url.query) |
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video_id = query_params.get('v', [None])[0] |
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elif 'youtu.be' in parsed_url.netloc: |
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video_id = parsed_url.path.lstrip('/') |
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if video_id: |
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return f"https://img.youtube.com/vi/{video_id}/maxresdefault.jpg" |
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return None |
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def analyze_thumbnail(thumbnail_url): |
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""" |
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Downloads the thumbnail image and analyzes it using the deepfake detection model. |
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""" |
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response = requests.get(thumbnail_url) |
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if response.status_code == 200: |
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image = Image.open(BytesIO(response.content)).convert("RGB") |
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results = model(image) |
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return results, image |
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else: |
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st.error("Failed to retrieve the thumbnail image.") |
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return None, None |
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st.title("Deepfake Detection from YouTube Thumbnails") |
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video_url = st.text_input("Enter YouTube Video URL:") |
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if st.button("Analyze"): |
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if video_url: |
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thumbnail_url = get_thumbnail_url(video_url) |
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if thumbnail_url: |
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results, image = analyze_thumbnail(thumbnail_url) |
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if results and image: |
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st.image(image, caption="YouTube Video Thumbnail", use_column_width=True) |
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st.subheader("Detection Results:") |
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for result in results: |
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label = result['label'] |
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confidence = result['score'] * 100 |
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st.write(f"**{label}**: {confidence:.2f}%") |
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else: |
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st.error("Could not analyze the thumbnail.") |
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else: |
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st.error("Invalid YouTube URL. Please enter a valid URL.") |
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else: |
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st.warning("Please enter a YouTube video URL.") |
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