main.py

from flask import Flask, render_template, request
import pandas as pd
import numpy as np
import pickle



app=Flask(__name__)
car = pd.read_csv("Cleanded Car.csv")

model = pickle.load(open("LinearRegressionModel.pkl", "rb"))

@app.route('/')
def index():
    companies = sorted(car['company'].unique())
    car_model = sorted(car['name'].unique())
    year = sorted(car['year'].unique(), reverse=True)
    fuel_type = car['fuel_type'].unique()

    companies.insert(0, 'Select Company')

    return render_template('index.html', companies=companies, car_model=car_model, years=year, fuel_types=fuel_type)

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

    prediction = model.predict(pd.DataFrame([[car_model, company, year, kms_driven, fuel_type]], columns=['name', 'company',
                                                                                                         'year', 'kms_driven',
                                                                                                         'fuel_type']))


    return str(np.round(prediction[0], 2))

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