server.py

# import Flask class from the flask module
from flask import Flask, request

import numpy as np
import pickle

# Create Flask object to run
app = Flask(__name__)

@app.route('/')
def home():
    return "Hi, Welcome to Flask!!"

@app.route('/predict')
def predict():
	# Get data sent via the GET request
	sepLen = request.args['sepal_length']
	sepWid = request.args['sepal_width']
	petLen = request.args['petal_length']
	petWid = request.args['petal_width']
	
	testData = np.array([sepLen, sepWid, petLen, petWid]).reshape(1,4)
	class_prediced = int(svmIrisModel.predict(testData)[0])
	output = "Predicted Iris Class: " + str(class_prediced)
	
	return (output)
	
	
@app.route('/predict_iris', methods=['POST'])
def predict_iris():
	if request.method == 'POST':		
		# Get data sent via the GET request
		data = request.form
		sepLen = data['sepal_length']
		sepWid = data['sepal_width']
		petLen = data['petal_length']
		petWid = data['petal_width']
		
		testData = np.array([sepLen, sepWid, petLen, petWid]).reshape(1,4)
		class_prediced = int(svmIrisModel.predict(testData)[0])
		output = "Predicted Iris Class: " + str(class_prediced)
		
		return output
        
	
	
# Load the pre-trained and persisted SVM model
# Note: The model will be loaded only once at the start of the server
def load_model():
	global svmIrisModel
	
	svmIrisFile = open('models/SVMModel.pckl', 'rb')
	svmIrisModel = pickle.load(svmIrisFile)
	svmIrisFile.close()

if __name__ == "__main__":
	print("**Starting Flask Server...")
	
	# Call function that loads Model
	load_model()
	
	# Run Server
	app.run()