text: import json
  import numpy
  import requests
  
  import tensorflow as tf
  from matplotlib import pyplot as plt
  import numpy as np
  
  labels_path = tf.keras.utils.get_file('ImageNetLabels.txt','https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt')
  imagenet_labels = np.array(open(labels_path).read().splitlines())
  
  
  
  file = tf.keras.utils.get_file(
      "grace_hopper.jpg",
      "https://storage.googleapis.com/download.tensorflow.org/example_images/grace_hopper.jpg")
  img = tf.keras.preprocessing.image.load_img(file, target_size=[224, 224])
  plt.imshow(img)
  plt.axis('off')
  x = tf.keras.preprocessing.image.img_to_array(img)
  x = tf.keras.applications.mobilenet.preprocess_input(
      x[tf.newaxis,...]
  )
  
  
  data = json.dumps({"signature_name": "serving_default","instances": x.tolist()})
  headers = {"content-type": "application/json"}
  json_response = requests.post('http://localhost:8501/v1/models/model1/versions/1:predict',data=data, headers=headers)
  predictions1 = numpy.array(json.loads(json_response.text)["predictions"])
  
  
  
  data = json.dumps({"signature_name": "serving_default","instances": x.tolist()})
  headers = {"content-type": "application/json"}
  json_response = requests.post('http://localhost:8501/v1/models/model2:predict',data=data, headers=headers)
  predictions2 = numpy.array(json.loads(json_response.text)["predictions"])
  
  
  #TODO Prometheus
  
  
  
  decoded1 = imagenet_labels[np.argsort(predictions1)[0,::-1][:5]+1]
  decoded2 = imagenet_labels[np.argsort(predictions2)[0,::-1][:5]+1]
  
  
  
  print(decoded1,decoded2)
  