main.py

import streamlit as st
import pickle
import string
import nltk
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer

nltk.download('punkt')
ps = PorterStemmer()


def transform_text(text):
    text = text.lower()  # lower case
    text = nltk.word_tokenize(text)
    y = []
    for i in text:
        if i.isalnum():  # if i is alpha numeric
            y.append(i)

    text = y[:]  # cloning the list
    y.clear()
    for i in text:
        if i not in stopwords.words('english') and i not in string.punctuation:
            y.append(i)

    text = y[:]
    y.clear()

    for i in text:
        y.append(ps.stem(i))

    return " ".join(y)


tfidf = pickle.load(open('vectorizer.pkl', 'rb'))
model = pickle.load(open('model.pkl', 'rb'))


def main():
    st.title("Email Spam Classifier")
    input_sms = st.text_area("Enter the message:", height=250)
    if st.button('Predict'):
        # 1. preprocess
        transformed_sms = transform_text(input_sms)
        # 2. vectorize
        vector_input = tfidf.transform([transformed_sms])
        # 3. predict
        result = model.predict(vector_input)[0]
        # 4.Display
        if result == 1:
            st.header("Spam")
        else:
            st.header("Not Spam")


if __name__ == "__main__":
    main()