app.py

from flask import Flask, render_template,request
import pandas as pd
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('home.html')

@app.route('/predict', methods = ['POST'])
def predict():
	df = pd.read_csv("data/Youtube01-Psy.csv")
	df_data = df[['CONTENT', 'CLASS']]
	# Features and Labels
	df_x = df_data['CONTENT']
	df_y = df_data.CLASS
	#Extract the features with countVectorizer
	corpus = df_x
	cv = CountVectorizer()
	X = cv.fit_transform(corpus)
	from sklearn.model_selection import train_test_split

	X_train, X_test, y_train, y_test = train_test_split(X, df_y, test_size = 0.33, random_state = 42)

	#Navie Bayes
	clf = MultinomialNB()
	clf.fit(X_train, y_train)
	clf.score(X_test, y_test)

	#Save Model
	joblib.dump(clf, 'model.pkl')
	print("Model dumped!")

	#ytb_model = open('spam_model.pkl', 'rb')
	clf = joblib.load('model.pkl')

	if request.method == 'POST':
		comment = request.form['comment']
		data = [comment]
		vect = cv.transform(data).toarray()
		my_prediction = clf.predict(vect)

	return render_template('result.html', prediction = my_prediction)


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