app.py

import json
import streamlit as st
import pickle
import string
from nltk.corpus import stopwords
import nltk
from nltk.stem.porter import PorterStemmer
from streamlit_lottie import st_lottie
import time

# Load NLTK resources
nltk.download('punkt')
nltk.download('stopwords')

# Create a PorterStemmer object
ps = PorterStemmer()

# Load the vectorizer and model from pickle files
tfidf = pickle.load(open('vectorizer.pkl', 'rb'))
model = pickle.load(open('model.pkl', 'rb'))

# Function to load Lottie file
def load_lottiefile(filepath: str):
    with open(filepath, 'r') as f:
        return json.load(f)

# Function to transform text
def transform_text(text: str) -> str:
    text = text.lower()
    text = nltk.word_tokenize(text)
    text = [word for word in text if word.isalnum()]
    text = [word for word in text if word not in stopwords.words('english') and word not in string.punctuation]
    text = [ps.stem(word) for word in text]
    return ' '.join(text)

# App layout
st.image("logo.svg")
st.title("Identify spam messages with a click of a button")
st.markdown('Protect yourself from *getting spammed* by using this service.')

# Columns for layout
col1, col2, col3 = st.columns((1.5, 0.5, 1))

with col1:
    st.header("How it works")
    st.markdown("This application uses **Multinomial Naive Bayes** to classify messages into Spam or Not Spam.   "
                "*The **precision** of the result is 100% and the **accuracy** is 97.2%.*")

with col2:
    st.text("")

with col3:
    lottie_spam = load_lottiefile("side_image.json")
    st_lottie(
        lottie_spam,
        speed=0.5,
        reverse=False,
        loop=True,
        quality="high",
        key=None,
        height=250,
        width=250,
    )

# Input message area
st.header("Email/SMS Spam Classifier")
input_msg = st.text_area("Enter the message")

# Predict button
if st.button('Predict'):
    # Preprocess input message
    transformed_msg = transform_text(input_msg)
    # Vectorize input message
    vector_input = tfidf.transform([transformed_msg])
    # Predict spam or not spam
    result = model.predict(vector_input)[0]
    # Display result
    if result == 1:
        with st.spinner('Wait for it...'):
            time.sleep(1)
        st.error("This is a Spam message")
    else:
        with st.spinner('Wait for it...'):
            time.sleep(1)
        st.success("This is not a Spam Message")