from flask import Flask,render_template,request
import jsonify
import requests
import numpy as np
import sklearn
from sklearn.preprocessing import StandardScaler
import pickle

app = Flask(__name__)
model = pickle.load(open("random_forest_regression_car_model.pkl","rb"))

@app.route('/')
def Home():
    return render_template('index.html')

standard_to = StandardScaler()

@app.route("/predict",methods = ['POST'])
def predict():
	Fuel_Type_Diesel = 0
	if request.method == "POST":
		Year = int(request.form['Year'])
		Present_Price = float(request.form['Present_Price'])
		Kms_Driven = int(request.form['Kms_Driven'])
		
		Owner = int(request.form['Owner'])
		# if Owner <=3:
			# print(Owner)
		Fuel_Type_Petrol = request.form['Fuel_Type_Petrol']
		if(Fuel_Type_Petrol == "Petrol"):
			Fuel_Type_Petrol = 1
			Fuel_Type_Diesel = 0

		elif(Fuel_Type_Petrol == 'Diesel'):
			Fuel_Type_Diesel = 1
			Fuel_Type_Petrol = 0

		else :
			Fuel_Type_Petrol = 0
			Fuel_Type_Diesel = 0

		Year = 2020 - Year
		Seller_Type_Individual = request.form['Seller_Type_Individual']
		if (Seller_Type_Individual == "Individual"):
			Seller_Type_Individual = 1

		else:
			Seller_Type_Individual = 0

		Transmission_Mannual = request.form['Transmission_Mannual']
		if Transmission_Mannual == "Mannual":
			Transmission_Mannual = 1
		else :
			Transmission_Mannual = 0

		prediction = model.predict([[Present_Price,Kms_Driven,Owner,Year,Fuel_Type_Diesel,Fuel_Type_Petrol,Seller_Type_Individual,Transmission_Mannual]])
		output = round(prediction[0],2)
		if output < 0:
			return render_template('index.html',prediction_texts = "Sorry You Cannt sell this car")

		else:
			return render_template('index.html',prediction_text = "Your can sell The car at {} L".format(output))

	else:
		return render_template('index.html')

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