fake app.py

app.py

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+# Module 1: Import necessary packages
+import streamlit as st
+import numpy as np
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
+from sklearn.svm import LinearSVC
+from sklearn.naive_bayes import MultinomialNB
+import warnings
+import streamlit_lottie
+
+warnings.filterwarnings("ignore")
+
+# Set page configuration (must be the first Streamlit command)
+page_icon = ":metro:"  # emojis: https://www.webfx.com/tools/emoji-cheat-sheet/
+layout = "wide"
+page_title = "Fake News Detection"
+st.set_page_config(page_title=page_title, page_icon=page_icon, layout=layout)
+
+# Module 2: Load the dataset
+@st.cache_data
+def load_data():
+    data = pd.read_csv("fake_or_real_news.csv")
+    data['fake'] = data['label'].apply(lambda x: 0 if x == 'REAL' else 1)
+    return data
+
+# Module 3: Select Vectorizer and Classifier
+def select_model():
+    vectorizer_type = st.sidebar.selectbox("Select Vectorizer", ["TF-IDF", "Bag of Words"])
+    classifier_type = st.sidebar.selectbox("Select Classifier", ["Linear SVM", "Naive Bayes"])
+    
+    vectorizer = None
+    if vectorizer_type == "TF-IDF":
+        vectorizer = TfidfVectorizer(stop_words='english', max_df=0.7)
+    elif vectorizer_type == "Bag of Words":
+        vectorizer = CountVectorizer(stop_words='english', max_df=0.7)
+    
+    classifier = None
+    if classifier_type == "Linear SVM":
+        classifier = LinearSVC()
+    elif classifier_type == "Naive Bayes":
+        classifier = MultinomialNB()
+    
+    return vectorizer, classifier
+
+# Module 4: Train the model (no caching here)
+def train_model(data, vectorizer, classifier):
+    x_vectorized = vectorizer.fit_transform(data['text'])
+    clf = classifier.fit(x_vectorized, data['fake'])
+    return clf
+
+# Module 5: Streamlit app
+def main():
+    # Streamlit app
+    st.title(page_title + " " + page_icon)
+    st.lottie("https://lottie.host/bd0c4818-c5a6-4e42-b407-746bc448c2c7/ipVUdgFncO.json", width=200, height=200)
+
+    # --- HIDE STREAMLIT STYLE ---
+    hide_st_style = """
+    <style>
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    header {visibility: hidden;}
+    </style>
+    """
+    st.markdown(hide_st_style, unsafe_allow_html=True)
+
+    # Load data
+    data = load_data()
+    
+    # Select vectorizer and classifier
+    vectorizer, classifier = select_model()
+    
+    # Text input for user to input news article
+    user_input = st.text_area("Enter your news article here:")
+    
+    # When user submits the input
+    if st.button("Check"):
+        # Train the model
+        clf = train_model(data, vectorizer, classifier)
+        
+        # Vectorize the user input
+        input_vectorized = vectorizer.transform([user_input])
+        
+        # Predict the label of the input
+        prediction = clf.predict(input_vectorized)
+        
+        # Convert prediction to integer for interpretation
+        result = int(prediction[0])
+        
+        # Display the result
+        if result == 1:
+            st.error("This news article is likely fake!")
+        else:
+            st.success("This news article seems to be real.")
+
+# Run the Streamlit app
+if __name__ == "__main__":
+    main()
+
+st.markdown("**Created with enthusiasm by SuperSam**")