text: import io
  import sys
  import pandas as pd
  from Crypto.Cipher import AES
  
  test = sys.argv[1]
  
  #open text file in read mode
  text_file = open("Output.txt", "r")
   
  #read whole file to a string
  datatest = text_file.read()
   
  #close file
  text_file.close()
  print(datatest)
  
  data = io.StringIO(datatest)
  
  df = pd.read_csv(data, sep=",")
  
  # K-Means Clustering
  
  # Importing the libraries
  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  
  # Importing the dataset
  dataset = df
  X = dataset.iloc[:, [3, 4]].values
  
  # Using the elbow method to find the optimal number of clusters
  from sklearn.cluster import KMeans
  wcss = []
  for i in range(1, 11):
      kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)
      kmeans.fit(X)
      wcss.append(kmeans.inertia_)
  plt.plot(range(1, 11), wcss)
  plt.title('The Elbow Method')
  plt.xlabel('Number of clusters')
  plt.ylabel('WCSS')
  plt.show()
  
  # Training the K-Means model on the dataset
  kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42)
  y_kmeans = kmeans.fit_predict(X)
  
  # Visualising the clusters
  plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')
  plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')
  plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')
  plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')
  plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')
  plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')
  plt.title('Clusters of customers')
  plt.xlabel('Annual Income (k$)')
  plt.ylabel('Spending Score (1-100)')
  plt.legend()
  plt.show()
  print("Successfully executed")