app.py

from flask import Flask,render_template,url_for,request
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

app = Flask(__name__)

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

@app.route('/predict', methods=['POST'])
def predict():
	df = pd.read_csv("YoutubeSpamMergedData.csv")
	df_data = df[["CONTENT", "CLASS"]]
	# Features and Labels
	df_x = df_data['CONTENT']
	df_y = df_data.CLASS
	# Extract Feature With CountVectorizer
	corpus = df_x
	cv = CountVectorizer()
	X = cv.fit_transform(corpus)  # Fit the Data

	X_train, X_test, y_train, y_test = train_test_split(X, df_y, test_size=0.33, random_state=42)
	# Naive Bayes Classifier

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


	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(debug=True)