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application.py
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application.py
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from flask import Flask, render_template,request
import pandas as pd
import pickle
import numpy as np
app = Flask(__name__)
model = pickle.load(open("LinearRegression.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()
companies.insert(0,"Select Company")
return render_template('index.html', companies=companies, car_models=car_models, 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'))
print(company,car_model,year,fuel_type,kms_driven)
prediction = model.predict(pd.DataFrame([[car_model, company, year, kms_driven, fuel_type]], columns=['name',
'company','year','kms_driven','fuel_type']))
print(prediction)
return str(np.round(prediction[0],2))
if __name__=="__main__":
app.run(debug=True)