text: import json
  import tensorflow as tf
  import numpy as np
  import os
  import random
  import string
  
  from flask import Flask, request
  
  app = Flask(__name__)
  
  model = tf.keras.models.load_model('model.h5')
  feature_model = tf.keras.models.Model(model.inputs, [layer.output for layer in model.layers])
  
  _, (x_test, _) = tf.keras.datasets.mnist.load_data()
  x_test = x_test / 255.
  
  def get_prediction():
      index = np.random.choice(x_test.shape[0])
      image = x_test[index,:,:]
      image_arr = np.reshape(image, (1, 784))
      return feature_model.predict(image_arr), image
  
  @app.route('/', methods=['GET', 'POST'])
  def index():
      if request.method == 'POST':
          preds, image = get_prediction()
          final_preds = [p.tolist() for p in preds]
          return json.dumps({'prediction': final_preds, 'image': image.tolist()}).encode('utf-8')
      return 'Welcome to the ml server'
  
  if __name__ == '__main__':
      app.run()
  
      