RestAPI.py

import json
import pickle
import Classifier

from functools      import wraps
from flask_restful  import Resource, Api
from flask          import Flask, request, jsonify, abort

app = Flask(__name__)
api = Api(app)

# The actual decorator function
def require_appkey(view_function):
	@wraps(view_function)
	# the new, post-decoration function. Note *args and **kwargs here.
	def decorated_function(*args, **kwargs):
		with open('api.key', 'r') as apikey:
			key = apikey.read().replace('\n', '')
			print("KEY: ", key)
		if request.headers.get('x-api-key') and request.headers.get('x-api-key') == key:
			return view_function(*args, **kwargs)
		else:
			abort(401)
	return decorated_function

# Main class
class Emotion(Resource):
	@require_appkey
	def post(self):
		text = request.json['text']
		emotions = Classifier.get_emotion(text, count_vect, tf_transformer, calibrated_svc, label_dict)
		return emotions

# Load data function
def load_data():
    # load configuration file
    js = open('config-api.json').read()
    config = json.loads(js)

    # load model
    model = pickle.load(open(config['pre-trained-model'], 'rb')) 

    # load encoded data
    count_vect, transformer, labels = pickle.load(open(config['encoded-data'], 'rb')) 

    # load label dictionary
    label_dict = config['label-dict']

    return count_vect, transformer, model, label_dict

# Routes
api.add_resource(Emotion, '/svc/v1/emotion')  

# Main
if __name__ == '__main__':
	count_vect, tf_transformer, calibrated_svc, label_dict = load_data()
	app.run(host='0.0.0.0', port='6232')