old_contents: #
  # Copyright 2018 Analytics Zoo Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License 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.
  #
  
  from optparse import OptionParser
  from zoo.common.nncontext import init_nncontext
  from zoo.models.image.imageclassification import *
  
  
  def predict(model_path, img_path, topN):
      print("ImageClassification prediction")
      print("Model Path %s" % model_path)
      print("Image Path %s" % img_path)
      print("Top N : %d" % topN)
      imc = ImageClassifier.load_model(model_path)
      image_set = ImageSet.read(img_path, sc)
      output = imc.predict_image_set(image_set)
      labelMap = imc.get_config().label_map()
      predicts = output.get_predict().collect()
      for predict in predicts:
          (uri, probs) = predict
          sortedProbs = [(prob, index) for index, prob in enumerate(probs)]
          sortedProbs.sort()
          print("Image : %s, top %d prediction result" % (uri, topN))
          for i in range(topN):
              print("\t%s, %f" % (labelMap[sortedProbs[999 - i][1]], sortedProbs[999 - i][0]))
  
  
  if __name__ == "__main__":
      parser = OptionParser()
      parser.add_option("-f", "--folder", type=str, dest="img_path", default=".",
                        help="Path where the images are stored")
      parser.add_option("--model", type=str, dest="model_path", default="",
                        help="Path where the model is stored")
      parser.add_option("--topN", type=int, dest="topN", default=1, help="top N number")
      (options, args) = parser.parse_args(sys.argv)
  
      sc = init_nncontext(create_spark_conf().setAppName("Image Classification Example"))
  
      predict(options.model_path, options.img_path, options.topN)
  
new_contents: #
  # Copyright 2018 Analytics Zoo Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License 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.
  #
  
  from optparse import OptionParser
  from zoo.common.nncontext import init_nncontext
  from zoo.models.image.imageclassification import *
  
  
  def predict(model_path, img_path, topN):
      print("ImageClassification prediction")
      print("Model Path %s" % model_path)
      print("Image Path %s" % img_path)
      print("Top N : %d" % topN)
      imc = ImageClassifier.load_model(model_path)
      image_set = ImageSet.read(img_path, sc)
      output = imc.predict_image_set(image_set)
      labelMap = imc.get_config().label_map()
      predicts = output.get_predict().collect()
      for predict in predicts:
          (uri, probs) = predict
          sortedProbs = [(prob, index) for index, prob in enumerate(probs[0])]
          sortedProbs.sort()
          print("Image : %s, top %d prediction result" % (uri, topN))
          for i in range(topN):
              print("\t%s, %f" % (labelMap[sortedProbs[999 - i][1]], sortedProbs[999 - i][0]))
  
  
  if __name__ == "__main__":
      parser = OptionParser()
      parser.add_option("-f", "--folder", type=str, dest="img_path", default=".",
                        help="Path where the images are stored")
      parser.add_option("--model", type=str, dest="model_path", default="",
                        help="Path where the model is stored")
      parser.add_option("--topN", type=int, dest="topN", default=1, help="top N number")
      (options, args) = parser.parse_args(sys.argv)
  
      sc = init_nncontext(create_spark_conf().setAppName("Image Classification Example"))
  
      predict(options.model_path, options.img_path, options.topN)
  
subject: update image classification predict according to predict API change (#429)