application.py

from flask import Flask,render_template,request
import pandas as pd
import numpy as np
import pickle
app = Flask(__name__)
model = pickle.load(open("LinearRegressionModel.pkl","rb"))
car = pd.read_csv('cleaned car.csv')
@app.route('/')
def index():
    companies = sorted(car['company'].unique())
    car_models = sorted(car['name'].unique())
    year = sorted(car['year'].unique(), reverse=True)
    fuel_type = car['fuel_type'].unique()
    return render_template('index.html',companies = companies,car_models=car_models,years = year,fuel_type = fuel_type)

@app.route('/predict',methods=['POST'])
def predict():
    company = request.form.get('company')
    car_model = request.form.get('car_models')
    year = request.form.get('year')
    fuel = request.form.get('fuel_type')
    kms_driven = request.form.get('kms_driven')


    prediction = model.predict(pd.DataFrame([[car_model,company,year,kms_driven,fuel]],columns=['name','company','year','kms_driven','fuel_type']))
  
    return str(np.round(prediction[0],2))

if __name__ == '__main__':
    app.run(debug=True)