app.py

#importing required libraries

from flask import Flask, request, render_template
import numpy as np
import pandas as pd
from sklearn import metrics
import warnings
import pickle
from convert import convertion
warnings.filterwarnings('ignore')
from feature import FeatureExtraction

file = open("newmodel.pkl","rb")
gbc = pickle.load(file)
file.close()


app = Flask(__name__)
@app.route("/")
def home():
    return render_template("index.html")
@app.route('/result',methods=['POST','GET'])
def predict():
    if request.method == "POST":
        url = request.form["name"]
        obj = FeatureExtraction(url)
        x = np.array(obj.getFeaturesList()).reshape(1,30)
    
        y_pred =gbc.predict(x)[0]
            #1 is safe
            #-1 is unsafe
        #y_pro_phishing = gbc.predict_proba(x)[0,0]
        #y_pro_non_phishing = gbc.predict_proba(x)[0,1]
            # if(y_pred ==1 ):
        #3pred = "It is {0:.2f} % safe to go ".format(y_pro_phishing*100)
        #xx =y_pred
        name=convertion(url,int(y_pred))
        return render_template("index.html", name=name)
@app.route('/usecases', methods=['GET', 'POST'])
def usecases():
    return render_template('usecases.html')
if __name__ == "__main__":
    app.run(debug=True)