text: import numpy as np
  from sklearn import datasets
  from google.cloud import storage
  import pickle
  import requests
  
  url='https://europe-west1-ecstatic-elf-337907.cloudfunctions.net/m22'
  BUCKET_NAME = 'mlops-project-6'
  MODEL_FILE = 'm22/model/model.pkl'
  
  iris_X, iris_y = datasets.load_iris(return_X_y=True)
  
  client = storage.Client()
  bucket = client.get_bucket(BUCKET_NAME)
  blob = bucket.get_blob(MODEL_FILE)
  my_model = pickle.loads(blob.download_as_string())
  
  
  np.random.seed(0)
  indices = np.random.permutation(len(iris_X))
  iris_X_train = iris_X[indices[:-10]]
  iris_y_train = iris_y[indices[:-10]]
  iris_X_test = iris_X[indices[-10:]]
  iris_y_test = iris_y[indices[-10:]]
  
  
  
  r=requests.post(url,json={'input_data':[1,2,3,4]})
  # r=requests.get(url)
  print( r.text)
  
  
  
  """
  from google.cloud import storage
  import pickle
  
  BUCKET_NAME = 'mlops-project-6'
  MODEL_FILE = 'm22/model/model.pkl'
  
  client = storage.Client()
  bucket = client.get_bucket(BUCKET_NAME)
  blob = bucket.get_blob(MODEL_FILE)
  my_model = pickle.loads(blob.download_as_string())
  
  def knn_classifier(request):
     request_json = request.get_json()
     if request_json and 'input_data' in request_json:
           data = request_json['input_data']
           input_data = list(map(int, data.split(',')))
           prediction = my_model.predict([input_data])
           return f'Belongs to class: {prediction}'
     else:
           return 'No input data received'
  
  
  """