File size: 1,802 Bytes
dc4f473 1dbeab5 93dd3e3 1dbeab5 93dd3e3 1dbeab5 93dd3e3 1dbeab5 93dd3e3 1dbeab5 93dd3e3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
import streamlit as st
import yt_dlp
import torch
from transformers import pipeline
st.set_page_config(page_title="Video Deepfake Detector", layout="centered")
# Download video using yt-dlp
def download_video(video_url, output_path="video.mp4"):
ydl_opts = {
'format': 'best',
'outtmpl': output_path,
'quiet': True,
}
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
ydl.download([video_url])
return output_path
# Load the deepfake detection model
def load_model():
return pipeline("image-classification", model="microsoft/resnet-50")
# Analyze video frames for deepfake detection
def analyze_video(video_path, model):
import cv2
cap = cv2.VideoCapture(video_path)
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
step = max(frame_count // 5, 1)
results = []
for i in range(0, frame_count, step):
cap.set(cv2.CAP_PROP_POS_FRAMES, i)
ret, frame = cap.read()
if not ret:
continue
rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
prediction = model(rgb_frame)
results.append(prediction[0])
cap.release()
return results
# Streamlit interface
st.title("🎥 Video Deepfake Detector")
video_url = st.text_input("Enter YouTube Video URL:")
if st.button("Submit") and video_url:
with st.spinner("Downloading and analyzing video..."):
try:
video_path = download_video(video_url)
model = load_model()
predictions = analyze_video(video_path, model)
st.success("Analysis Complete!")
for idx, pred in enumerate(predictions):
st.write(f"Frame {idx + 1}: {pred['label']} with confidence {pred['score']:.2f}")
except Exception as e:
st.error(f"Error: {e}")
|