text: import os
  os.environ['GDAL_DATA'] = os.environ['CONDA_PREFIX'] + r'\Library\share\gdal'
  os.environ['PROJ_LIB'] = os.environ['CONDA_PREFIX'] + r'\Library\share'
  
  import pandas as pd
  from joblib import dump, load
  
  from generate_full_predictions import predict_cover
  
  #hucs = ['010500021301', '030902040303', '070801050901', '080102040304', '130202090102', '140801040103', '180500020905']
  #hucs = ['custom2']
  #hucs = ['missouri_sub', 'nawlins_sub', 'montana_sub']
  hucs = ['maine_sub']
  models = ['bin_deep_massagedDHM_v2']
  
  ###
  par = r'E:\gen_model'
  
  sas = pd.read_excel(os.path.join(par, r'study_areas.xlsx'), dtype={'HUC12': object})
  sas = sas.set_index('HUC12')
  
  for huc in hucs:
      for model in models: # need to make it so the read data is reused for different models
          print(f'Generating {model}: {huc}')
          fol = os.path.join(par, 'study_areas', huc)
          model_folder = os.path.join(par, 'models', model)
          clf, feature_cols = load(os.path.join(model_folder, 'clf_package.joblib'))
          imp = load(os.path.join(model_folder, 'importances_package.joblib'))
          img = predict_cover(fol, os.path.join(model_folder,huc), feature_cols, clf, imp, sas.loc[huc].EPSG)