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app.py
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app.py
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from flask import Flask,render_template,request
import pickle
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import OrdinalEncoder
app = Flask(__name__)
app.secret_key="abc"
def encode(wheel_d,engine_l):
enc = pickle.load(open('encode.pkl','rb'))
x1=enc.fit_transform([[wheel_d,engine_l]])
return x1
def pred(list1):
model = pickle.load(open('predict.pkl','rb'))
price=model.predict([list1])[0]
return price
@app.route("/")
def home():
return render_template ('Home.html')
@app.route("/pred",methods=["POST"])
def check_fun():
wheel_drive=request.form["wheel_drive"]
engine_location=request.form["engine_location"]
width=float(request.form["width"])
engine_size=float(request.form["engine_size"])
horsepower=float(request.form["horsepower"])
city_mpg=float(request.form["city_mpg"])
highway_mpg=float(request.form["highway_mpg"])
x=encode(wheel_drive,engine_location)
l1=[x[0][0],x[0][1],width,engine_size,horsepower,city_mpg,highway_mpg]
p=pred(l1)
return render_template("Home.html",price=round(p,2))
if __name__ =='__main__':
app.run(debug=True)