app.py

import streamlit as st
import pickle
import string
from nltk.stem.porter import PorterStemmer
import nltk
from nltk.corpus import stopwords
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_area("Enter the message")
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")