text: import argparse
  import torch
  from .data import DataProcessing, DataTransform
  import psychXRF.model as Model
  import h5py
  
  class psychXRF:
      def __init__(self, model_path, transform_file):
          self.model_path = model_path
          self.h5_inputs_data = None
          self.transform_file = transform_file
          self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
          self.model = torch.load(self.model_path).to(self.device)
      
      def load_inputs_from_file(self, h5_inputs_data):
          self.h5_inputs_data = h5_inputs_data
          with h5py.File(self.h5_inputs_data, 'r') as fin:
              self.inputs = fin['inputs'][:]
          return self
      
      def sample_inputs_from_tiff(tiff_dir, labels):
          pass
      
      def predict(self):
          if hasattr(self, 'inputs'):
              dtrans = DataTransform(self.inputs).load_transform_info(self.transform_file).input_transform()
          else:
              raise ValueError('No inputs yet')
          self.inputs = torch.Tensor(dtrans.inputs).to(self.device)
          predicted = self.model(self.inputs)
          if isinstance(predicted, tuple):
              self.predicted = []
              for prediction in predicted:
                  self.predicted.append(prediction.cpu().detach().numpy())
          else:
              self.predicted = predicted.cpu().detach().numpy()
          return self.predicted
          
          
  