text: #Imports
  import os
  import fnmatch
  import matplotlib.pyplot as plt
  from tensorflow import keras
  
  def load_images_from_folder(folder, filter):       
      # Get files in the folder
      files = fnmatch.filter(os.listdir(folder), filter)
  
      # Load all files
      result = []    
      for file in files:
          # Get full file path
          file_path = os.path.join(folder, file)    
                  
          # Load image and convert to unit8 array
          img = keras.preprocessing.image.load_img(file_path)    
          img = keras.preprocessing.image.img_to_array(img)
  
          # Get expected label
          label = file.split('_')[1]
  
          # Append image with the label
          result.append((img, label))       
          
      return result
  
  def plot_results(images_with_labels, predicted_labels, row_count, col_count, font_size):
      # Prepare plot
      plt.figure()
  
      # Iterate over rows and cols
      for i in range(row_count * col_count):
          
          plt.subplot(row_count, col_count, i+1)                    
  
          # Disable ticks
          plt.xticks([])
          plt.yticks([])
          plt.grid(False)
  
          # Display image
          plt.imshow(images_with_labels[i][0] / 255)
  
          # Prepare and display actual and predicted labels
          label = predicted_labels[i] + ' (actual: ' + images_with_labels[i][1] + ')'
          plt.xlabel(label, fontsize=font_size)
  
      
      # Add padding
      plt.tight_layout(pad=1.0)
  
      # Show plot
      plt.show()
  
  def say_text(text):
      os.system('echo ' + text + ' | festival --tts')
  