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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
data = pd.read_csv("", encoding= 'latin-1')
data = data[["class", "message"]]
x = np.array(data["message"])
y = np.array(data["class"])
cv = CountVectorizer()
X = cv.fit_transform(x) # Fit the Data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
clf = MultinomialNB(),y_train)
import streamlit as st
st.title("Spam Detection System")
def spamdetection():
user = st.text_area("Enter any Message or Email: ")
if len(user) < 1:
st.write(" ")
sample = user
data = cv.transform([sample]).toarray()
a = clf.predict(data)
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