# predict.py
# use some model and add automatically predicted labels to train and test splits, than save as a new dataset

from nkululeko.experiment import Experiment
import configparser
from nkululeko.util import Util
from  nkululeko.constants import VERSION
import argparse
import os

def main(src_dir):
    parser = argparse.ArgumentParser(description='Call the nkululeko framework.')
    parser.add_argument('--config', default='exp.ini', help='The base configuration')
    args = parser.parse_args()
    if args.config is not None:
        config_file = args.config    
    else:
        config_file = f'{src_dir}/exp.ini'

    # test if the configuration file exists
    if not os.path.isfile(config_file):
        print(f'ERROR: no such file: {config_file}')
        exit()
        
    # load one configuration per experiment
    config = configparser.ConfigParser()
    config.read(config_file)
    # create a new experiment
    expr = Experiment(config)
    util = Util('predict')
    util.debug(f'running {expr.name} from config {config_file}, nkululeko version {VERSION}')

    # load the data
    expr.load_datasets()

    # split into train and test
    expr.fill_train_and_tests()
    util.debug(f'train shape : {expr.df_train.shape}, test shape:{expr.df_test.shape}')

    # process the data
    df = expr.autopredict()
    target = util.config_val('DATA', 'target', 'emotion')
    if 'class_label' in df.columns:
        df = df.drop(columns=[target])
        df = df.rename(columns={'class_label':target})
    name = util.get_data_name()+'_predicted'
    df.to_csv(f'{expr.data_dir}/{name}.csv')
    util.debug(f'saved {name}.csv to {expr.data_dir}')
    print('DONE')


if __name__ == "__main__":
    cwd = os.path.dirname(os.path.abspath(__file__))
    main(cwd) # use this if you want to state the config file path on command line