app.py

import streamlit as st
import pickle
import string
import nltk
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
ps=PorterStemmer()
tfidf=pickle.load(open("vectorizer.pkl","rb"))
model=pickle.load(open("model.pkl","rb"))


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)
st.title("Email/Sms spam Classifier")

# input_sms=st.text_input("Enter the message")# you can get sort space
input_sms=st.text_area("Enter the message")# you can get long space
if st.button("predict"):

    # 1.proprocess
    transform_sms=transform_text(input_sms)
    # 2.vectorize
    vector_input=tfidf.transform([transform_sms])
    # 3.predict
    result=model.predict(vector_input)[0]
    # 4.display
    if result==1:
        st.header("Spam")
    else:
        st.header("Not Spam")