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import streamlit as st |
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import requests |
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from PIL import Image |
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from transformers import pipeline |
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from urllib.parse import urlparse, parse_qs |
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st.set_page_config(page_title="Deepfake Video Detector", layout="centered") |
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st.title("🎥 Deepfake Video Detector") |
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st.write("Enter a YouTube video URL (supports Shorts & standard videos) to check for deepfakes.") |
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@st.cache_data |
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def extract_video_id(url): |
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parsed_url = urlparse(url) |
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if "youtube" in parsed_url.netloc: |
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if parsed_url.path.startswith("/watch"): |
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return parse_qs(parsed_url.query).get("v", [None])[0] |
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elif parsed_url.path.startswith("/shorts/"): |
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return parsed_url.path.split("/")[-1] |
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return None |
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@st.cache_data |
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def get_thumbnail(video_id): |
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return f"https://img.youtube.com/vi/{video_id}/hqdefault.jpg" |
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@st.cache_resource |
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def load_model(): |
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return pipeline("image-classification", model="facebook/deit-base-distilled-patch16-224") |
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model = load_model() |
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video_url = st.text_input("🔗 Paste YouTube video URL:") |
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if st.button("Detect Deepfake") and video_url: |
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video_id = extract_video_id(video_url) |
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if video_id: |
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thumbnail_url = get_thumbnail(video_id) |
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try: |
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response = requests.get(thumbnail_url) |
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response.raise_for_status() |
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thumbnail = Image.open(requests.get(thumbnail_url, stream=True).raw) |
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st.image(thumbnail, caption="Video Thumbnail", use_container_width=True) |
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st.write("🔎 Analyzing thumbnail...") |
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results = model(thumbnail) |
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deepfake_score = sum(result['score'] for result in results if 'fake' in result['label'].lower()) |
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if deepfake_score > 0.5: |
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st.error(f"⚠️ High probability of deepfake detected! (Confidence: {deepfake_score:.2%})") |
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else: |
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st.success(f"✅ No strong evidence of deepfake detected. (Confidence: {1 - deepfake_score:.2%})") |
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except requests.exceptions.RequestException: |
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st.error("❌ Failed to fetch thumbnail. Please check the video URL.") |
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else: |
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st.error("❌ Invalid YouTube URL. Please enter a valid video or Shorts link.") |
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