text: import tensorflow.keras
  from PIL import Image, ImageOps
  import numpy as np
  
  def prediction(pic_path):
  	# Disable scientific notation for clarity
  	np.set_printoptions(suppress=True)
  
  	# Load the model
  	model = tensorflow.keras.models.load_model('./model/keras_model.h5')
  
  	# Create the array of the right shape to feed into the keras model
  	# The 'length' or number of images you can put into the array is
  	# determined by the first position in the shape tuple, in this case 1.
  	data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
  
  	# Replace this with the path to your image
  	# Reads and converts image into RGB format
  	image = Image.open(pic_path).convert('RGB')
  
  	#resize the image to a 224x224 with the same strategy as in TM2:
  	#resizing the image to be at least 224x224 and then cropping from the center
  	size = (224, 224)
  	image = ImageOps.fit(image, size, Image.ANTIALIAS)
  
  	#turn the image into a numpy array
  	image_array = np.asarray(image)
  
  	# display the resized image
  	# image.show()
  
  	# Normalize the image
  	normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
  
  	# Load the image into the array
  	data[0] = normalized_image_array
  
  	# run the inference
  	prediction_data = model.predict(data)
  	
  	return prediction_data
  