webapp.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.externals import joblib
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split
app = Flask(__name__)

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

@app.route("/predict",methods=["POST"])
def predict():
    df= pd.read_csv("YoutubeSpamData.csv")
    df_data = df[["CONTENT","CLASS"]]
    df_x = df_data['CONTENT']
    df_y = df_data.CLASS
    cv = CountVectorizer()
    X = cv.fit_transform(df_x)    
    X_train, X_test, y_train, y_test = train_test_split(X, df_y, test_size=0.33, random_state=42)    
    clf = MultinomialNB() # Naive Bayes classifier
    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)