'''
  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 numpy as np
import grpc
import tensorflow as tf
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow.keras.applications.resnet50 import decode_predictions
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc

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

if __name__ == '__main__':
    channel = grpc.insecure_channel('localhost:8500')
    stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
    img_file = tf.keras.utils.get_file(
        "./kitten_small.jpg",
        "https://raw.githubusercontent.com/awslabs/mxnet-model-server/master/docs/images/kitten_small.jpg")
    img = image.load_img(img_file, target_size=(224, 224))
    img_array = preprocess_input(image.img_to_array(img)[None, ...])
    request = predict_pb2.PredictRequest()
    request.model_spec.name = 'resnet50_inf1_serve'
    request.inputs['input'].CopyFrom(
        tf.contrib.util.make_tensor_proto(img_array, shape=img_array.shape))
    result = stub.Predict(request)
    prediction = tf.make_ndarray(result.outputs['output'])
    print(decode_predictions(prediction))