Update app.py
Browse files
app.py
CHANGED
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@@ -7,6 +7,13 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification
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tokenizer = AutoTokenizer.from_pretrained("sofzcc/distilbert-base-uncased-fake-news-checker")
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model = AutoModelForSequenceClassification.from_pretrained("sofzcc/distilbert-base-uncased-fake-news-checker")
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# Function to predict if news is real or fake
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def predict_news(news_text):
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inputs = tokenizer(news_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
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@@ -19,13 +26,14 @@ def predict_news(news_text):
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# Streamlit App
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st.title("Fake News Detector")
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st.write("Enter a news article below to check if it's real or fake:")
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if st.button("Evaluate"):
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if
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prediction = predict_news(news_text)
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st.write(f"The news article is predicted to be: **{prediction}**")
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else:
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st.write("Please enter some news
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tokenizer = AutoTokenizer.from_pretrained("sofzcc/distilbert-base-uncased-fake-news-checker")
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model = AutoModelForSequenceClassification.from_pretrained("sofzcc/distilbert-base-uncased-fake-news-checker")
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def newspaper_text_extraction(article_url):
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article = Article(article_url)
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article.download()
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article.parse()
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return article. title,article.text
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# Function to predict if news is real or fake
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def predict_news(news_text):
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inputs = tokenizer(news_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
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# Streamlit App
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st.title("Fake News Detector")
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st.write("Enter a news article URL below to check if it's real or fake:")
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news_url = st.text_area("News URL", height=100)
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if st.button("Evaluate"):
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if news_url:
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news_text = newspaper_text_extraction(news_url)
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prediction = predict_news(news_text)
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st.write(f"The news article is predicted to be: **{prediction}**")
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else:
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st.write("Please enter some news URL to evaluate.")
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