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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+fake_or_real_news.csv filter=lfs diff=lfs merge=lfs -text

app.py

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+import pandas as pd
+import numpy as np
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.model_selection import train_test_split
+from sklearn.naive_bayes import MultinomialNB
+import streamlit as st
+
+# Set page title and icon
+st.set_page_config(page_title='Fake News Detection', page_icon=":postal_horn:")
+
+# Load the dataset
+df = pd.read_csv("fake_or_real_news.csv")
+
+# Train the model
+x = np.array(df["title"])
+y = np.array(df["label"])
+cv = CountVectorizer()
+x = cv.fit_transform(x)
+
+xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2, random_state=42)
+model = MultinomialNB()
+model.fit(xtrain, ytrain)
+
+
+st.title('Fake News Detection :postal_horn:')
+
+# Display an image
+st.image("https://upload.wikimedia.org/wikipedia/commons/f/f7/The_fin_de_siècle_newspaper_proprietor_%28cropped%29.jpg")
+
+# Input field for user to enter news headline
+default_example = "Alians decided to my America after Russia Exploded"
+news_headline = st.text_input("Enter a news headline:", default_example)
+
+if st.button("Check"):
+    # Make prediction
+    df_input = cv.transform([news_headline]).toarray()
+    prediction = model.predict(df_input)
+    
+    # Display prediction result
+    if prediction[0] == 'FAKE':
+        st.error("This news headline is likely FAKE.")
+    else:
+        st.success("This news headline is likely REAL.")

fake_or_real_news.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bb7fa746dd7148b63b0a10e47b329f4b4825afc85b206d3fea18ecfce28ee731
+size 30696129

requirements.txt

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+pandas
+numpy
+scikit-learn
+streamlit