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app.py
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from flask import Flask, render_template, request
import jsonify
import requests
import pickle
import numpy as np
import sklearn
from sklearn.preprocessing import StandardScaler
app = Flask(__name__)
model = pickle.load(open('random_forest_regression_model.pkl', 'rb'))
@app.route('/',methods=['GET'])
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'])
Kms_Driven2 = np.log(Kms_Driven)
Owner = int(request.form['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_Diesel == "Diesel":
Fuel_Type_Petrol = 0
Fuel_Type_Diesel = 1
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_Driven2,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 cannot sell this car")
else:
return render_template('index.html',prediction_text = "You Can Sell The Car at {}".format(output))
else:
return render_template('index.html')
if __name__ == "__main__":
app.run(debug = True)