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app.py

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+import streamlit as st
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import CountVectorizer
+import pandas as pd
+import numpy as np
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.metrics import accuracy_score
+
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+
+
+df = pd.read_csv("spam.csv")
+st.title(":red[Email Spam or Ham Classification]")
+
+x = df["Message"]
+y = df["Category"]
+ham = df[df["Category"] == "ham"]
+
+fig, ax = plt.subplots()
+
+sns.countplot(data = df, x= "Category", ax = ax)
+st.pyplot(fig)
+
+bow = CountVectorizer(stop_words = "english")
+
+final_X = pd.DataFrame(bow.fit_transform(x).toarray(), columns = bow.get_feature_names_out())
+
+X_train, X_test, y_train, y_test = train_test_split(final_X, y , test_size= 0.25, random_state = 23)
+
+nav_base = MultinomialNB()
+
+nav_base.fit(X_train, y_train)
+
+y_pred = nav_base.predict(X_test)
+
+res = st.button("predict_score")
+if res:
+    st.write(accuracy_score(y_test,y_pred))
+    st.snow()
+
+input = st.text_input("enter email")
+
+def fun(email):
+    data = bow.transform([email]).toarray()
+    st.write(nav_base.predict(data)[0])
+
+if st.button("predict"):
+    fun(input)
+    st.balloons()

requirements.txt

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+streamlit
+pandas
+numpy
+scikit-learn