'''
  Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

  Licensed under the Apache License, Version 2.0 (the "License").
  You may not use this file except in compliance with the License.
  A copy of the License is located at

      http://www.apache.org/licenses/LICENSE-2.0

  or in the "license" file accompanying this file. This file is distributed
  on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
  express or implied. See the License for the specific language governing
  permissions and limitations under the License.
'''

import os
import time
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications import resnet50

tf.keras.backend.set_image_data_format('channels_last')

# Create input from image
img_sgl = image.load_img('kitten_small.jpg', target_size=(224, 224))
img_arr = image.img_to_array(img_sgl)
img_arr2 = np.expand_dims(img_arr, axis=0)
img_arr3 = resnet50.preprocess_input(img_arr2)

# Load model
COMPILED_MODEL_DIR = './resnet50_neuron/'
predictor_inferentia = tf.contrib.predictor.from_saved_model(COMPILED_MODEL_DIR)

# Run Inference and Display results
model_feed_dict={'input': img_arr3}
infa_rslts = predictor_inferentia(model_feed_dict)
print(resnet50.decode_predictions(infa_rslts["output"], top=5)[0])