Sources
https://github.com/abhinavsagar/Machine-Learning-Deployment
https://towardsdatascience.com/how-to-easily-deploy-machine-learning-models-using-flask-b95af8fe34d4

Project Structure

This project has four parts :

model.py — This contains code for the machine learning model to predict sales in the third month based on the sales in the first two months.

app.py — This contains Flask APIs that receives sales details through GUI or API calls, computes the predicted value based on our model and returns it.

request.py — This uses requests module to call APIs defined in app.py and displays the returned value.

HTML/CSS — This contains the HTML template and CSS styling to allow user to enter sales detail and displays the predicted sales in the third month.

app.py

import numpy as np
	from flask import Flask, request, jsonify, render_template
	import pickle

	app = Flask(__name__)
	model = pickle.load(open('model.pkl', 'rb'))

	@app.route('/')
	def home():
	return render_template('index.html')

	@app.route('/predict',methods=['POST'])
	def predict():

	int_features = [int(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='Sales should be $ {}'.format(output))

	@app.route('/results',methods=['POST'])
	def results():

	data = request.get_json(force=True)
	prediction = model.predict([np.array(list(data.values()))])

	output = prediction[0]
	return jsonify(output)

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

import requests

	url = 'http://localhost:5000/results'
	r = requests.post(url,json={'rate':5, 'sales_in_first_month':200, 'sales_in_second_month':400})

	print(r.json())