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app.py

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+import pandas as pd
+import json
+import numpy as np
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.model_selection import train_test_split
+from sklearn.naive_bayes import BernoulliNB
+import streamlit as st
+
+# Load the dataset
+df = pd.read_json('sarcasm.json', lines=True)
+df = df[["headline", "is_sarcastic"]]
+df["is_sarcastic"] = df["is_sarcastic"].map({0: "Serious", 1: "Sarcastic/Lie"})
+
+# Train the model
+x = np.array(df["headline"])
+y = np.array(df["is_sarcastic"])
+cv = CountVectorizer()
+X = cv.fit_transform(x)
+x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)
+model = BernoulliNB()
+model.fit(x_train, y_train)
+
+# Define Streamlit app
+def main():
+    st.title('Sarcasm & Lie Detector :clown_face:')
+    st.write('Autism Special Edition')
+    st.image('machine.png')
+    # Input field for user to enter text
+    user_input = st.text_input("Enter text:", "Dogs can fly now")
+    
+    if st.button("Check"):
+        # Make prediction
+        data = cv.transform([user_input]).toarray()
+        prediction = model.predict(data)
+        
+        # Display prediction result
+        st.write("This is ", prediction[0])
+
+    st.write('Reference dataframe')
+    st.dataframe(df.head(300))
+if __name__ == '__main__':
+    main()

requirements.txt

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+pandas
+scikit-learn
+streamlit