text: import datetime
  import pandas as pd
  import numpy as np
  import subprocess
  import pickle
  from numpy import array
  import seaborn as sn
  import tensorflow as tf
  import matplotlib.pyplot as plt
  
  
  #combining predicitions and generating plot for pred. representation
  def predict(data):
      with open('rolf.pickle', 'rb') as handle:
          rolf = pickle.load(handle)
      #preproccessed data
      data = pd.DataFrame.from_dict(data)
      del data['epoch']
      data = data.to_numpy()
      data = data.astype('float64')
      results = []
      for i in range(len(data)):
          results.append(rolf.predict(data[i]))
      result1 = results
      model = tf.keras.models.load_model('model')
      data = pd.DataFrame.from_dict(data)
      del data['epoch']
      data = data.to_numpy()
      data = data.astype('float64')
      results = []
      for i in range(len(data)):
          results.append(np.argmax(model.predict(array([data[i]])))-1)
      result2 = results
      result1 = np.array(result1)
      result2 = np.array(result2)
  
      assert len(result1) == len(result2)
      #couting through the arrays and generate plotting infos
      counter1 = np.zeros(29)
      counter2 = np.zeros(29)
      for int in np.unique(result1):
          for i in range(len(result1)):
              if result1[i] == int:
                  counter1[int] = counter1[int] + 1
      for int in np.unique(result2):
          for i in range(len(result2)):
              if result1[i] == int:
                  counter2[int] = counter2[int] + 1
      colors1 = []
      for i in range(29):
          color = list(np.random.choice(range(256), size=3) / 256)
          colors1.append(color)
      colors2 = []
      for i in range(29):
          color = list(np.random.choice(range(256), size=3) / 256)
          colors2.append(color)
      plt.bar(x=np.arange(29), height=counter1, color=colors1)
      plt.bar(x=np.arange(29), height=counter2, color=colors2)
      plt.title('occurance of predicted clusters')
      plt.xlabel('cluster/class')
      plt.xticks(np.arange(29), rotation=90)
      plt.ylabel('occurance')
      plt.savefig('prediction.png')