text: 
  from urllib.request import urlopen
  from datetime import datetime
  from operator import itemgetter
  
  import tensorflow as tf
  from PIL import Image, ImageDraw
  from object_detection import ObjectDetection
  from azure.storage.blob import BlobServiceClient, BlobClient
  import numpy as np
  import sys
  import uuid
  import io
  import time
  
  
  MODEL_FILENAME = 'model.pb'
  LABELS_FILENAME = 'labels.txt'
  CONTAINER_NAME = "forkliftimages"
  
  od_model = None
  last_upload_time = None
  latest_blob_image = ''
  send_to_blob_interval = 5
  labels = []
  
  class TFObjectDetection(ObjectDetection):
      """Object Detection class for TensorFlow"""
  
      def __init__(self, graph_def, labels):
          super(TFObjectDetection, self).__init__(labels)
          self.graph = tf.compat.v1.Graph()
          with self.graph.as_default():
              input_data = tf.compat.v1.placeholder(tf.float32, [1, None, None, 3], name='Placeholder')
              tf.import_graph_def(graph_def, input_map={"Placeholder:0": input_data}, name="")
  
      def predict(self, preprocessed_image):
          inputs = np.array(preprocessed_image, dtype=np.float)[:, :, (2, 1, 0)]  # RGB -> BGR
  
          with tf.compat.v1.Session(graph=self.graph) as sess:
              output_tensor = sess.graph.get_tensor_by_name('model_outputs:0')
              outputs = sess.run(output_tensor, {'Placeholder:0': inputs[np.newaxis, ...]})
              return outputs[0]
  
  def initialize():
      global od_model
  
      # Load a TensorFlow model
      graph_def = tf.compat.v1.GraphDef()
      with tf.io.gfile.GFile(MODEL_FILENAME, 'rb') as f:
          graph_def.ParseFromString(f.read())
  
      # Load labels
      with open(LABELS_FILENAME, 'r') as f:
          labels = [l.strip() for l in f.readlines()]
  
      od_model = TFObjectDetection(graph_def, labels)
     
            
  def predict_image(image, blob_service_client):
      global last_upload_time
      global latest_blob_image
      try:
          if image.mode != "RGB":
              print("Converting to RGB")
              image = image.convert("RGB")
  
          predictions = od_model.predict_image(image)
  
          response = { 'created': datetime.utcnow().isoformat(), 'forklift': 0, 'bloburl' : latest_blob_image }
         
          if predictions:
              highest_prediction = max(predictions, key=itemgetter('probability'))
              print(highest_prediction)
              if highest_prediction['probability'] > 0.6:
                  response['forklift'] = 1
                  if last_upload_time is None or time.time() - last_upload_time >= send_to_blob_interval:
  
                      bounding_box = highest_prediction['boundingBox']
  
                      x0 = image.width * bounding_box['left']
                      y0 = image.height * bounding_box['top']
                      x1 = image.width * (bounding_box['left'] + bounding_box['width'])
                      y1 = image.height * (bounding_box['top'] + bounding_box['height'])
  
                      draw = ImageDraw.Draw(image)
                      draw.rectangle(((x0, y0), (x1, y1)), outline="red", width=3)
  
                      stream = io.BytesIO()
                      image.save(stream, format='PNG')
                      img_byte_array = stream.getvalue()
  
                      file_name = str(uuid.uuid4()) + ".png"
                      blob_client = blob_service_client.get_blob_client(container=CONTAINER_NAME, blob=file_name)
                      blob_client.upload_blob(img_byte_array)
  
                      last_upload_time = time.time()
                      latest_blob_image = blob_client.url
  
                      response['bloburl'] = latest_blob_image
  
                      print("sent image to blob:" + str(latest_blob_image))
          else:
              latest_blob_image = ''
              response['bloburl'] = latest_blob_image
  
          print("Results: " + str(response))
          return response
  
      except Exception as e:
          print(str(e))
          return 'Error: Could not preprocess image for prediction. ' + str(e)
  
  