text: import tensorflow.compat.v1 as tf
  import sys
  import os
  import tkinter as tk
  from tkinter import filedialog
  
  # Disable tensorflow compilation warnings
  os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
  import tensorflow.compat.v1 as tf
  
  # image_path = sys.argv[1]
  
  root = tk.Tk()
  root.withdraw()
  
  image_path = filedialog.askopenfilename()
  
  if image_path:
      
      # Read the image_data
      image_data = tf.gfile.FastGFile(image_path, 'rb').read()
  
      # Loads label file, strips off carriage return
      label_lines = [line.rstrip() for line 
                         in tf.gfile.GFile("tf_files/retrained_labels.txt")]
  
      # Unpersists graph from file
      with tf.gfile.FastGFile("tf_files/retrained_graph.pb", 'rb') as f:
          graph_def = tf.GraphDef()
          graph_def.ParseFromString(f.read())
          _ = tf.import_graph_def(graph_def, name='')
  
      with tf.Session() as sess:
          # Feed the image_data as input to the graph and get first prediction
          softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
          
          predictions = sess.run(softmax_tensor, \
                   {'DecodeJpeg/contents:0': image_data})
          
          # Sort to show labels of first prediction in order of confidence
          top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
          
          for node_id in top_k:
              human_string = label_lines[node_id]
              score = predictions[0][node_id]
              print('%s (score = %.5f)' % (human_string, score))