main.py

from flask import Flask,render_template,url_for,request
import pandas as pd 
import pickle
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.externals import joblib


app = Flask(__name__)

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

@app.route('/predict',methods=['POST'])
def predict():
# Load the vector 
	vector=open('model/vectorization.pkl','rb')
	cv=joblib.load(vector)

# Load the previously saved model.

	NB_spam_model = open('model/NB_spam_model.pkl','rb')
	clf = joblib.load(NB_spam_model)

	if request.method == 'POST':
		message = request.form['message']
		data = [message]
		vect = cv.transform(data).toarray()
		my_prediction = clf.predict(vect)
	return render_template('result.html',prediction = my_prediction)



if __name__ == '__main__':
	app.run(debug=True)