#import libraries
import numpy as np
from flask import Flask, render_template,request
import pickle#Initialize the flask App
app = Flask(__name__)
model = pickle.load(open('model.pkl', 'rb'))if __name__ == "__main__":
app.run(debug=True)# How To add ML to web. Go from down to up. Please#default page of our web-app
@app.route('/')
def home():
return render_template('index.html')#To use the predict button in our web-app
@app.route('/predict',methods=['POST'])
def predict():
#For rendering results on HTML GUI
int_features = [float(x) for x in request.form.values()]
final_features = [np.array(int_features)]
prediction = model.predict(final_features)
output = round(prediction[0], 2)
return render_template('index.html', prediction_text='CO2 Emission of the vehicle is :{}'.format(output))