api.py

#!/usr/bin python3

import os
import sys

from flask import Flask
from flask_restful import Resource, Api, reqparse
import numpy as np
import tensorflow as tf
from tensorflow import keras
from keras.models import load_model
from keras import backend as K

sys.path.append(os.path.abspath("data/src"))
from DftSpectrogram import DftSpectrogram
from focal_loss import focal_loss
K.clear_session()

from inference import test_pred

config = tf.ConfigProto(
    device_count={'GPU': 0},
    intra_op_parallelism_threads=1,
    allow_soft_placement=True
)

session = tf.Session(config=config)
K.set_session(session)


MODEL_PATH = os.path.abspath('data/model/model.h5')
custom_objects = {'DftSpectrogram': DftSpectrogram, 'focal_loss_fixed': focal_loss()}
print("Loading model from: {}".format(MODEL_PATH))
model = load_model(MODEL_PATH, custom_objects=custom_objects)
model._make_predict_function()
model.summary()


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


class Prediction(Resource):
    def __init__(self):
        self._required_features = ['FILE_PATH']
        self.reqparse = reqparse.RequestParser()
        for feature in self._required_features:
            self.reqparse.add_argument(feature, 
                                       type=str,
                                       required=True,
                                       location='json',
                                       help = 'No {} provided'.format(feature))
        super(Prediction, self).__init__()

    def post(self):
        global K, session
        K.clear_session()
        args = self.reqparse.parse_args()
        path_to_file = [args[f] for f in self._required_features]
        with session.as_default():
            with session.graph.as_default():
                y_pred = test_pred(model, path_to_file[0])
        return {'Human': "{o:.3f}".format(o=y_pred[0][0]),
                        'Attack': "{o:.3f}".format(o=y_pred[0][1])}


api.add_resource(Prediction, '/predict')


if __name__ == '__main__':
    app.run(debug=True, host='0.0.0.0')