app.py

import streamlit as st
import pickle
import numpy as np

# Load the Naive Bayes model, vectorizer, and feature names
with open('naive_bayes_model.pkl', 'rb') as model_file:
    model = pickle.load(model_file)

with open('vectorizer.pkl', 'rb') as vectorizer_file:
    vectorizer = pickle.load(vectorizer_file)

# Streamlit app
st.title("Spam Email Detection App")

# Pre-fill the text input with the sample instance
sample_text = (
    "Subject: naturally irresistible your corporate identity  lt is really hard to recollect a company : the  market is full of suqgestions and the information isoverwhelminq ; but a good  catchy logo , stylish statlonery and outstanding website  will make the task much easier .  we do not promise that havinq ordered a iogo your  company will automaticaily become a world ieader : it isguite ciear that  without good products , effective business organization and practicable aim it  will be hotat nowadays market ; but we do promise that your marketing efforts  will become much more effective . here is the list of clear  benefits : creativeness : hand - made , original logos , specially done  to reflect your distinctive company image . convenience : logo and stationery  are provided in all formats ; easy - to - use content management system letsyou  change your website content and even its structure . promptness : you  will see logo drafts within three business days . affordability : your  marketing break - through shouldn ' t make gaps in your budget . 100 % satisfaction  guaranteed : we provide unlimited amount of changes with no extra fees for you to  be surethat you will love the result of this collaboration . have a look at our  portfolio _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ not interested . . . _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _"
)

# Input field for the email text
email_text = st.text_area("Enter the email text:", value=sample_text, height=300)

# Predict button
if st.button("Predict"):
    # Transform the input text using the loaded vectorizer
    input_vector = vectorizer.transform([email_text])

    # Make prediction
    prediction = model.predict(input_vector)[0]

    # Display the result
    if prediction == 1:
        st.error("The email is predicted to be SPAM.")
    else:
        st.success("The email is predicted to be NOT SPAM.")