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main.py
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main.py
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from flask import Flask,render_template,request,url_for
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
app = Flask(__name__)
@app.route("/")
def index():
return render_template("index.html")
@app.route("/", methods=['POST'])
def predict():
url = "https://raw.githubusercontent.com/prateeksawhney97/Flask-Application-for-Spam-vs-Non-Spam-Classification/master/YoutubeSpamMergedData.csv"
df= pd.read_csv(url)
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)
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)
from sklearn.naive_bayes import MultinomialNB
mnb = MultinomialNB()
mnb.fit(X_train, y_train)
mnb.score(X_test, y_test)
if request.method == 'POST':
comment = request.form['comment']
data = [comment]
vect = cv.transform(data).toarray()
my_prediction = mnb.predict(vect)
return render_template("results.html", prediction=my_prediction, comment=comment)
if __name__ == '__main__':
app.run(host="127.0.0.1",port=8080,debug=True)