how to connect an ml model to a web application

                                    #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))

                                    # How To add ML to web. Go from down to up. Please

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

                                    import pandas as pd
from sklearn.linear_model import LinearRegression
import pickle

df = pd.read_csv("FuelConsumption.csv")
#use required features
cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]

#Training Data and Predictor Variable
# Use all data for training (tarin-test-split not used)
x = cdf.iloc[:, :3]
y = cdf.iloc[:, -1]
regressor = LinearRegression()

#Fitting model with trainig data
regressor.fit(x, y)

# Saving model to current directory
# Pickle serializes objects so they can be saved to a file, and loaded in a program again later on.
pickle.dump(regressor, open('model.pkl','wb'))

'''
#Loading model to compare the results
model = pickle.load(open('model.pkl','rb'))
print(model.predict([[2.6, 8, 10.1]]))
'''