app.py

import streamlit as st

import string
import nltk
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
import os
import pickle

nltk_data_path = os.path.join(os.path.expanduser("~"), "nltk_data")
if not os.path.exists(nltk_data_path):
    os.makedirs(nltk_data_path)

nltk.download('punkt', download_dir=nltk_data_path)
nltk.download('stopwords', download_dir=nltk_data_path)
nltk.download("punkt_tab", download_dir=nltk_data_path)

nltk.data.path.append(nltk_data_path)


# Initialize stemmer
ps = PorterStemmer()

# Preprocess function
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)

BASE_DIR = os.path.dirname(os.path.abspath(__file__))


# Load the vectorizer
vectorizer_path = os.path.join(BASE_DIR, "vectorizer.pkl")
with open(vectorizer_path, "rb") as f:
    tfidf = pickle.load(f)

# Load the trained model
model_path = os.path.join(BASE_DIR, "model.pkl")
with open(model_path, "rb") as f:
    model = pickle.load(f)


# Streamlit UI
st.title("📩 SMS Spam Detection")

input_sms = st.text_area("Enter your message")

if st.button("Predict"):   # only run when button clicked
    # 1. Preprocess
    transformed_sms = transform_text(input_sms)

    # 2. Vectorize
    vectorInput = tfidf.transform([transformed_sms])

    # 3. Predict
    result = model.predict(vectorInput)[0]

    # 4. Display
    if result == 0:
        st.header("✅ Ham (Not Spam!)")
    else:
        st.header("🚨 Spam")