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| import streamlit as st | |
| import requests | |
| from transformers import pipeline | |
| from PIL import Image | |
| import torch | |
| import torchvision.transforms as transforms | |
| import io | |
| # Load Fake News Detection Model from Hugging Face | |
| fake_news_pipeline = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection") | |
| def classify_text(news_text): | |
| result = fake_news_pipeline(news_text)[0]['label'].lower() | |
| return "Fake" if result == "fake" else "Real" | |
| def analyze_image(image): | |
| # Convert image to tensor (Placeholder: Model should be updated with a real image classifier) | |
| transform = transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor()]) | |
| image_tensor = transform(image).unsqueeze(0) | |
| return "Image analysis feature coming soon!" | |
| def verify_news(news_text): | |
| search_url = f"https://www.google.com/search?q={'+'.join(news_text.split())}" | |
| return search_url | |
| # Streamlit UI | |
| st.set_page_config(page_title="Fake News Detector", layout="wide") | |
| st.title("📰 Fake News Detector") | |
| # Tabs for Input and Results | |
| tab1, tab2 = st.tabs(["Input", "Results"]) | |
| with tab1: | |
| st.sidebar.title("Select Input Type") | |
| option = st.sidebar.radio("Choose an option", ["Text", "Image", "Video Link"]) | |
| if option == "Text": | |
| news_text = st.text_area("Enter the news content to check:", height=200) | |
| if st.button("Analyze News"): | |
| if not news_text.strip(): | |
| st.warning("Please enter some text.") | |
| else: | |
| st.session_state["news_text"] = news_text | |
| st.session_state["analyze_text"] = True | |
| st.experimental_rerun() | |
| elif option == "Image": | |
| uploaded_image = st.file_uploader("Upload a news image", type=["jpg", "png", "jpeg"]) | |
| if uploaded_image and st.button("Analyze Image"): | |
| image = Image.open(uploaded_image) | |
| st.session_state["news_image"] = image | |
| st.session_state["analyze_image"] = True | |
| st.experimental_rerun() | |
| elif option == "Video Link": | |
| video_url = st.text_input("Enter the video link:") | |
| if st.button("Analyze Video"): | |
| if not video_url.strip(): | |
| st.warning("Please enter a valid video link.") | |
| else: | |
| st.session_state["video_url"] = video_url | |
| st.session_state["analyze_video"] = True | |
| st.experimental_rerun() | |
| with tab2: | |
| if st.session_state.get("analyze_text", False): | |
| news_text = st.session_state.get("news_text", "") | |
| with st.spinner("Analyzing text..."): | |
| result = classify_text(news_text) | |
| verification_link = verify_news(news_text) | |
| if result == "Fake": | |
| st.error("❌ This news is likely **Fake**!", icon="⚠️") | |
| else: | |
| st.success("✅ This news is likely **Real**!", icon="✅") | |
| st.subheader("🔍 Verification & Trusted Sources") | |
| sources = ["https://www.bbc.com/news", "https://www.cnn.com", "https://www.reuters.com"] | |
| for link in sources: | |
| st.markdown(f"[🔗 {link}]({link})") | |
| st.markdown(f"[🔎 Verify on Google]({verification_link})") | |
| if st.session_state.get("analyze_image", False): | |
| image = st.session_state.get("news_image") | |
| st.image(image, caption="Uploaded Image", use_column_width=True) | |
| st.info(analyze_image(image)) | |
| if st.session_state.get("analyze_video", False): | |
| video_url = st.session_state.get("video_url", "") | |
| st.video(video_url) | |
| st.info("Video analysis feature coming soon!") | |