Delete main.py

main.py

@@ -1,73 +0,0 @@
-from flask import Flask, render_template, request
-import pickle
-import string
-import nltk
-from nltk.corpus import stopwords
-from nltk.stem.porter import PorterStemmer
-
-app = Flask(__name__)
-
-# Downloading NLTK libraries
-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)
-
-# Load the TF-IDF vectorizer and the model
-with open('vectorizer.pkl', 'rb') as f:
-    tfidf = pickle.load(f)
-
-with open('model.pkl', 'rb') as f:
-    model = pickle.load(f)
-
-@app.route('/')
-def index():
-    return render_template('index.html')
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    if request.method == 'POST':
-        input_sms = request.form['sms']
-
-        # Preprocess the input
-        transform_sms = transform_text(input_sms)
-
-        # Vectorize the input
-        vector_input = tfidf.transform([transform_sms])
-
-        # Predict
-        result = model.predict(vector_input)[0]
-        # Convert result to string
-        if result == 1:
-            result_text = "SPAM"
-        else:
-            result_text = "NOT SPAM"
-
-        # Return prediction result
-        return render_template('result.html', result=result_text)
-
-if __name__ == '__main__':
-    app.run(debug=True)