| from transformers import pipeline | |
| import streamlit as st | |
| import torch | |
| import requests | |
| from PIL import Image | |
| from pytube import YouTube | |
| import tempfile | |
| def detect_image(image): | |
| model = pipeline("image-classification", model="google/vit-base-patch16-224") | |
| return model(image) | |
| def detect_video(video_url): | |
| yt = YouTube(video_url) | |
| stream = yt.streams.filter(file_extension='mp4').first() | |
| with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tmp_file: | |
| stream.download(filename=tmp_file.name) | |
| video_path = tmp_file.name | |
| return video_path | |
| def main(): | |
| st.title("VerifiAI - Image & Video Authenticity Checker") | |
| option = st.sidebar.selectbox("Select Option", ["Image Detection", "Video Detection"]) | |
| if option == "Image Detection": | |
| uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"]) | |
| if uploaded_image: | |
| image = Image.open(uploaded_image) | |
| st.image(image, caption="Uploaded Image", use_container_width=True) | |
| with st.spinner("Analyzing Image..."): | |
| results = detect_image(image) | |
| for result in results: | |
| st.write(f"{result['label']}: {result['score']*100:.2f}%") | |
| elif option == "Video Detection": | |
| video_url = st.text_input("Enter YouTube Video Link") | |
| if video_url: | |
| st.video(video_url) | |
| with st.spinner("Analyzing Video..."): | |
| video_path = detect_video(video_url) | |
| st.success("Video downloaded for analysis. Video detection model coming soon.") | |
| if __name__ == "__main__": | |
| if not torch.cuda.is_available(): | |
| st.warning("CUDA not available. Running on CPU might be slower.") | |
| main() | |