text: """This example shows how you can use ebonite to wrap your sklearn model into flask service"""
  from ebonite import Ebonite
  
  
  def main():
      #  create local ebonite client. This client stores metadata and artifacts on local fs.
      ebnt = Ebonite.local()
  
      model = ebnt.get_model(project='my_project', task='regression_is_my_profession', model_name='mymodel')
  
      # run flask service with this model
      from ebonite.runtime import run_model_server
      run_model_server(model)
      # now you can use client.py to call this service or go to http://localhost:9000/apidocs to view swagger ui
  
  
  if __name__ == '__main__':
      main()
  