app.py

import streamlit as st
import pickle
import string
import nltk
nltk.download('punkt')
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
ps=PorterStemmer()


def transform_text(text):
    text = text.lower()
    text = nltk.word_tokenize(text)
    y = []
    for i in text:
        if i.isalnum():
            y.append(i)
    text = y[:]
    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'))
st.title("Email/SMS Spam Classifier")
input_sms=st.text_input("Enter the message")

if st.button('Predict'):
    # preprocess
    transform_sms = transform_text(input_sms)
    # vectorize
    vector_input = tfidf.transform([transform_sms])
    # predict
    result = model.predict(vector_input)[0]
    # display
    if result == 1:
        st.header("Spam")
    else:
        st.header("Not Spam")