app.py

from flask import Flask, render_template, request
import nltk
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
import pickle
import string

app = Flask(__name__)

nltk.download('punkt')
nltk.download('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)

def predict_spam(message):
    # Preprocess
    transformed_sms = transform_text(message)
    # Vectorize
    vector_input = tfidf.transform([transformed_sms])
    # Predict
    result = model.predict(vector_input)[0]
    return result

@app.route('/')
def home():
    return render_template('index.html')

@app.route('/predict', methods=['POST'])
def predict():
    if request.method == 'POST':
        input_sms = request.form['message']
        result = predict_spam(input_sms)
        return render_template('index.html', result=result)  # Pass 'result' to the template


if __name__ == '__main__':
    tfidf = pickle.load(open('vectorizer.pkl', 'rb'))
    model = pickle.load(open('model.pkl', 'rb'))
    app.run(host='0.0.0.0')