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app.py
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app.py
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from flask import Flask, render_template, request
import pandas as pd
import pickle
import sklearn
import mimetypes
import regex as re
import nltk
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
mimetypes.add_type('text/css', '.css')
mimetypes.add_type('text/javascript', '.js')
app = Flask(__name__)
app.config['TEMPLATES_AUTO_RELOAD']=True
smsModel = pickle.load(open('sms_classification.pkl','rb'))
cv = pickle.load(open('countVectorizer.pkl','rb'))
try:
nltk.download('stopwords')
except:
print('error downloading stopwords')
@app.route('/favicon.ico')
def favicon():
return app.send_static_file('favicon.ico')
@app.route('/')
def index():
return render_template('index.html')
@app.route('/predict', methods = ["GET", "POST"])
def predict():
if request.method == 'POST':
message = request.form['sms_text']
ps = PorterStemmer()
review = re.sub('[^a-zA-Z]',' ',message)
review = review.lower()
review = review.split()
try:
review = [ps.stem(word) for word in review if not word in stopwords.words('english')]
except:
print('stopwords not downloaded')
review = ' '.join(review)
X = cv.transform([review])
prediction = smsModel.predict(X)
return "Bwawhhh! It's a spam message!⚠️" if prediction[0]==1 else "Looks like this message is ham🤙"
if __name__ == "__main__":
debug = True
app.run()