def sample_classification(event, context):
    """Triggered by a change to a Firestore document.
    Args:
         event (dict): Event payload.
         context (google.cloud.functions.Context): Metadata for the event.
    """
    resource_string = context.resource
    # print out the resource string that triggered the function
    print(f"Function triggered by change to: {resource_string}.")
    # now print out the entire event object
    print(str(event))

    with open('label_encoder.pkl', 'rb') as label_encoder_file:
      loaded_label_encoder = pickle.load(label_encoder_file)

    with open('gaussian_naive_bayes_model.pkl', 'rb') as model_file:
      loaded_model = pickle.load(model_file)

    input_data = pd.DataFrame({'calculatedRatio': 0.231057205, 'deviceRatio': 0.231057205,'led1Buffer': 23776.33, 'led2Buffer': 26401.67, 
      'led1Sample': 16286, 'led2Sample': 6952.67}, index=[0])

    predicted_result = loaded_model.predict(new_data)
    print(predicted_result.item())