text: import numpy as np
  import pandas as pd
  from csv import reader
  
  from regression_model.processing.data_management import load_pipeline
  from regression_model.config import config
  from regression_model.processing.validation import validate_inputs
  from regression_model import __version__ as _version
  
  import logging
  import typing as t
  
  
  _logger = logging.getLogger(__name__)
  
  pipeline_file_name = f"{config.PIPELINE_SAVE_FILE}{_version}.pkl"
  print(pipeline_file_name)
  _price_pipe = load_pipeline(file_name=pipeline_file_name)
  
  
  
  #def make_prediction(*, input_data: t.Union[pd.DataFrame, dict],
  #                    ) -> dict:
  
  def make_prediction(input_data):
      """Make a prediction using a saved model pipeline.
  
      Args:
          input_data: Array of model prediction inputs.
  
      Returns:
          Predictions for each input row, as well as the model version.
      """
      #data= pd.read_csv(input_data)
      data = input_data
  
      validated_data = validate_inputs(input_data=data)
      print("validated data", validated_data)
  
      prediction = _price_pipe.predict(validated_data[config.FEATURES])
  
      print("PREDICTION",prediction)
      output = np.exp(prediction)
  
      #results = {"predictions": output, "version": _version}
      results = output
      _logger.info(
          f"Making predictions with model version: {_version} "
          f"Inputs: {validated_data} "
          f"Predictions: {results}"
      )
  
      return results
  
  #print(config.DATASET_DIR)
  
  
  #result= make_prediction(config.DATASET_DIR/'atest.csv')
  #print(result)