app.py

import streamlit as st
import torch
From newspaper import Article

# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("sofzcc/distilbert-base-uncased-fake-news-checker")
model = AutoModelForSequenceClassification.from_pretrained("sofzcc/distilbert-base-uncased-fake-news-checker")

def newspaper_text_extraction(article_url):
    article = Article(article_url)
    article.download()
    article.parse()
    return article. title,article.text


# Function to predict if news is real or fake
def predict_news(news_text):
    inputs = tokenizer(news_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    predictions = torch.argmax(logits, dim=-1).item()
    return "Real" if predictions == 1 else "Fake"

# Streamlit App
st.title("Fake News Detector")

st.write("Enter a news article URL below to check if it's real or fake:")

news_url = st.text_area("News URL", height=100)

if st.button("Evaluate"):
    if news_url:
        news_text = newspaper_text_extraction(news_url)
        prediction = predict_news(news_text)
        st.write(f"The news article is predicted to be: **{prediction}**")
    else:
        st.write("Please enter some news URL to evaluate.")