old_contents: import sys, os
  curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
  sys.path.append("../../../predict/python/")
  sys.path.append("../../../python/")
  
  from mxnet_predict import Predictor, load_ndarray_file
  import mxnet as mx
  import logging
  import numpy as np
  from skimage import io, transform
  
  # Load the pre-trained model
  prefix = "Inception/Inception_BN"
  num_round = 39
  symbol_file = "%s-symbol.json" % prefix
  param_file = "%s-0039.params" % prefix
  predictor = Predictor(open(symbol_file).read(),
                        open(param_file).read(),
                        {'data':(1, 3, 224, 224)})
  mean_img = load_ndarray_file(open("Inception/mean_224.nd").read())["mean_img"]
  
  synset = [l.strip() for l in open('Inception/synset.txt').readlines()]
  
  def PreprocessImage(path, show_img=False):
      # load image
      img = io.imread(path)
      print("Original Image Shape: ", img.shape)
      # we crop image from center
      short_egde = min(img.shape[:2])
      yy = int((img.shape[0] - short_egde) / 2)
      xx = int((img.shape[1] - short_egde) / 2)
      crop_img = img[yy : yy + short_egde, xx : xx + short_egde]
      # resize to 224, 224
      resized_img = transform.resize(crop_img, (224, 224))
      if show_img:
          io.imshow(resized_img)
      # convert to numpy.ndarray
      sample = np.asarray(resized_img) * 255
      # swap channel from RGB to BGR
      sample = sample[:, :, [2,1,0]]
      # swap axes to make image from (224, 224, 3) to (3, 224, 224)
      sample = np.swapaxes(sample, 0, 2)
      sample = np.swapaxes(sample, 1, 2)
  
      # sub mean
      normed_img = sample - mean_img
      normed_img.resize(1, 3, 224, 224)
      return normed_img
  
  # Get preprocessed batch (single image batch)
  batch = PreprocessImage('./download.png', True)
  
  predictor.forward(data=batch)
  prob = predictor.get_output(0)[0]
  
  pred = np.argsort(prob)[::-1]
  # Get top1 label
  top1 = synset[pred[0]]
  print("Top1: ", top1)
  # Get top5 label
  top5 = [synset[pred[i]] for i in range(5)]
  print("Top5: ", top5)
  
  
new_contents: import sys, os
  curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
  sys.path.append("../../../amalgamation/python/")
  sys.path.append("../../../python/")
  
  from mxnet_predict import Predictor, load_ndarray_file
  import mxnet as mx
  import logging
  import numpy as np
  from skimage import io, transform
  
  # Load the pre-trained model
  prefix = "Inception/Inception_BN"
  num_round = 39
  symbol_file = "%s-symbol.json" % prefix
  param_file = "%s-0039.params" % prefix
  predictor = Predictor(open(symbol_file).read(),
                        open(param_file).read(),
                        {'data':(1, 3, 224, 224)})
  mean_img = load_ndarray_file(open("Inception/mean_224.nd").read())["mean_img"]
  
  synset = [l.strip() for l in open('Inception/synset.txt').readlines()]
  
  def PreprocessImage(path, show_img=False):
      # load image
      img = io.imread(path)
      print("Original Image Shape: ", img.shape)
      # we crop image from center
      short_egde = min(img.shape[:2])
      yy = int((img.shape[0] - short_egde) / 2)
      xx = int((img.shape[1] - short_egde) / 2)
      crop_img = img[yy : yy + short_egde, xx : xx + short_egde]
      # resize to 224, 224
      resized_img = transform.resize(crop_img, (224, 224))
      if show_img:
          io.imshow(resized_img)
      # convert to numpy.ndarray
      sample = np.asarray(resized_img) * 255
      # swap channel from RGB to BGR
      sample = sample[:, :, [2,1,0]]
      # swap axes to make image from (224, 224, 3) to (3, 224, 224)
      sample = np.swapaxes(sample, 0, 2)
      sample = np.swapaxes(sample, 1, 2)
  
      # sub mean
      normed_img = sample - mean_img
      normed_img.resize(1, 3, 224, 224)
      return normed_img
  
  # Get preprocessed batch (single image batch)
  batch = PreprocessImage('./download.png', True)
  
  predictor.forward(data=batch)
  prob = predictor.get_output(0)[0]
  
  pred = np.argsort(prob)[::-1]
  # Get top1 label
  top1 = synset[pred[0]]
  print("Top1: ", top1)
  # Get top5 label
  top5 = [synset[pred[i]] for i in range(5)]
  print("Top5: ", top5)
  
  
subject: fix example predict path changed