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app.py
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app.py
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from flask import Flask, render_template, url_for, request
import numpy as np
import pandas as pd
import joblib
filename = 'model.pkl'
pipe = joblib.load(filename)
app = Flask(__name__)
@app.route('/')
def home():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
arr = []
if request.method == 'POST':
age = int(request.form['Age'])
marital_status = request.form['Marital_Status']
jobrole = request.form['Job_Role']
monthly_salary = int(request.form['Monthly_Salary'])//75.20
num_companies_worked = int(request.form['NumCompaniesWorked'])
years_at_company = int(request.form['YearsAtCompany'])
job_satisfaction = int(request.form['Job_Satisfaction'])
business_travel = request.form['Business_Travel']
overtime = request.form['Overtime']
arr = [[age, business_travel, jobrole, job_satisfaction, marital_status,
monthly_salary, num_companies_worked, overtime, years_at_company]]
X_test = pd.DataFrame(arr,columns=['Age','BusinessTravel','JobRole','JobSatisfaction','MaritalStatus',
'MonthlyIncome','NumCompaniesWorked','OverTime','YearsAtCompany'])
pred = pipe.predict(X_test)
if pred == 1:
pred_text = "It's time to look for new opportunities. According to past data, your job offer is likely to be revoked."
else:
pred_text = "Your Job is secure. Good luck for future endeavours!"
return render_template('results.html',pred_text=pred_text)
if __name__ == '__main__':
app.run()