import csv
import datasets

_DOWNLOAD_URL = "https://huggingface.co/datasets/cfigueroa/glosas_etiquetadas/resolve/main/datasetNew.csv"

class GlosasEtiquetadas(datasets.GeneratorBasedBuilder):
    """Glosas Etiquetadas classification dataset."""

    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "label": datasets.ClassLabel(names = ["LIBRERIA_ACCESORIOS","ALIMENTOS","ASEOPERSONAL","VEHICULOS",
"BANO","BEBE","BEBIDAS","CARNE","CERVEZA","CONSTRUCCION",
"DECORACION","DEPORTE","DORMITORIO","ELECTRONICA","EQUIPAJE","FERRETERIA",
"GRIFERIA","ILUMINACION","JARDIN","JOYERIA","JUGUETERIA","LACTEO","LAVANDERIA",
"LICORES","LIMPIEZA","MASCOTAS","MENAJE","MOBILIARIO",
"ORGANIZACION","OUTDOOR","PANADERIA","PISOSMUROS","PUERTASVENTANAS",
"SERVICIOS","TECNOLOGIA","TERRAZAS","VESTUARIO","VINOS","ZAPATERIA", "ILEGIBLE"] ),
                }
            )
        )

    def _split_generators(self, dl_manager):
        path = dl_manager.download_and_extract(_DOWNLOAD_URL)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": path, "is_test": False}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": path, "is_test": True}),
        ]

    def _generate_examples(self, filepath, is_test, test_size = 0.3):
        """Generate Glosas Etiquetadas examples."""
        with open(filepath, 'r', encoding='latin-1')  as csv_file:
            train_threshold = 40001
            csv_reader = csv.reader(
                csv_file, delimiter=';'
            )
            # next(csv_reader, None)  # skip the headers
            for id_, row in enumerate(csv_reader):
                if id_ > 0:
                    text, label = row
                    current_row = id_, {"text": text, "label": int(label)}
                    if (id_ < train_threshold) & (not is_test):
                        yield current_row
                    if (id_ >= train_threshold) & (is_test):
                        yield current_row