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| import streamlit as st | |
| import os | |
| import cv2 | |
| import torch | |
| import torchaudio | |
| import torchvision | |
| import tensorflow as tf | |
| from transformers import pipeline | |
| # Load a fake news detection model from Hugging Face | |
| fake_news_pipeline = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection") | |
| # 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"]) | |
| # Function to fetch real news links from various open sources | |
| def fetch_real_news_links(): | |
| return [ | |
| "https://www.bbc.com/news", | |
| "https://www.cnn.com", | |
| "https://www.reuters.com", | |
| "https://huggingface.co/datasets/misinformation", | |
| "https://www.wildfire.ai/deepfake-news-dataset", | |
| "https://www.snopes.com", | |
| "https://www.factcheck.org" | |
| ] | |
| 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"] = True | |
| st.rerun() | |
| elif option == "Image": | |
| uploaded_file = st.file_uploader("Upload an image of a news article", type=["jpg", "png", "jpeg"]) | |
| if uploaded_file is not None: | |
| st.image(uploaded_file, caption="Uploaded Image", use_column_width=True) | |
| st.info("🔍 Image analysis coming soon!") | |
| elif option == "Video Link": | |
| video_url = st.text_input("Enter a video news link to check") | |
| if st.button("Analyze Video"): | |
| if not video_url.strip(): | |
| st.warning("Please enter a valid URL.") | |
| else: | |
| st.info("🔍 Video analysis coming soon!") | |
| with tab2: | |
| if st.session_state.get("analyze", False): | |
| news_text = st.session_state.get("news_text", "") | |
| with st.spinner("Analyzing..."): | |
| # Check using Hugging Face model | |
| hf_result = fake_news_pipeline(news_text)[0]['label'].lower() | |
| # Display result | |
| if hf_result == "fake": | |
| st.error("❌ This news is likely **Fake**!", icon="⚠️") | |
| st.markdown('<style>div.stAlert {background-color: #ffdddd;}</style>', unsafe_allow_html=True) | |
| conclusion = "The analysis suggests that this news might be fabricated or misleading. Please verify from credible sources." | |
| elif hf_result == "real": | |
| st.success("✅ This news is likely **Real**!", icon="✅") | |
| st.markdown('<style>div.stAlert {background-color: #ddffdd;}</style>', unsafe_allow_html=True) | |
| conclusion = "The analysis indicates that this news appears to be credible and factual." | |
| else: | |
| st.info("🤔 The result is uncertain. Please verify from trusted sources.") | |
| conclusion = "There is uncertainty in the classification. Further verification is recommended." | |
| # Conclusion Section | |
| st.subheader("📌 Conclusion") | |
| st.write(conclusion) | |
| # Display real news sources | |
| st.subheader("🔗 Reliable News Sources") | |
| for link in fetch_real_news_links(): | |
| st.markdown(f"[🔗 {link}]({link})") |