code: def create_app(estimator, app_kwargs: Optional[dict] = None) -> None:
      """
      This function creates a basic gradio app for inference.
      It will later be expanded for other app types such as
      Streamlit.
  
  
      Example
      -------
      >>> from pycaret.datasets import get_data
      >>> boston = get_data('boston')
      >>> from pycaret.regression import *
      >>> exp_name = setup(data = boston,  target = 'medv')
      >>> lr = create_model('lr')
      >>> create_app(lr)
  
  
      estimator: scikit-learn compatible object
          Trained model object
  
  
      app_kwargs: dict, default = {} (empty dict)
          arguments to be passed to app class.
  
  
      Returns:
          None
      """
      return _CURRENT_EXPERIMENT.create_app(
          estimator=estimator, app_kwargs=app_kwargs
      )
  
imports: import logging
  import os
  from typing import Any, BinaryIO, Callable, Dict, List, Optional, Union
  import pandas
  from joblib.memory import Memory
  from pycaret.internal.parallel.parallel_backend import ParallelBackend
  from pycaret.loggers.base_logger import BaseLogger
  from pycaret.regression.oop import RegressionExperiment
  from pycaret.utils.constants import DATAFRAME_LIKE, SEQUENCE_LIKE, TARGET_LIKE
  from pycaret.utils.generic import check_if_global_is_not_none
label_desc: Use image node_id 31 for calling a global function with example usage: create_app(estimator, app_kwargs) and returns: _CURRENT_EXPERIMENT