import datasets
from datasets.tasks import ImageClassification


_CITATION = """\
@data{t5s2-4j73-22,
doi = {10.21227/t5s2-4j73},
url = {https://dx.doi.org/10.21227/t5s2-4j73},
author = {Dimauro, Giovanni and Maglietta, Rosalia and Bai, Thulasi and Kasiviswanathan, Sivachandar},
publisher = {IEEE Dataport},
title = {Eyes-defy-anemia},
year = {2022} }
"""

_DESCRIPTION = """\
The dataset Eyes-defy-anemia contains 218 images of eyes, in particular 
conjunctivas, which can be used for research on the diagnosis/estimation 
of anemia based on the pallor of conjunctiva.
"""

_URLS = {
    "train": "https://huggingface.co/datasets/Yahaira/anemia-eyes/blob/main/data/train.zip",
    "validation": "https://huggingface.co/datasets/Yahaira/anemia-eyes/blob/main/data/validation.zip",
    "test": "https://huggingface.co/datasets/Yahaira/anemia-eyes/blob/main/data/test.zip",
}

_NAMES = ["NoAnemia, Anemia"]


class Anemia(datasets.GeneratorBasedBuilder):
    """Beans plant leaf images dataset."""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "image_file_path": datasets.Value("string"),
                    "image": datasets.Image(),
                    "labels": datasets.features.ClassLabel(names=_NAMES),
                }
            ),
            supervised_keys=("image", "labels"),
            citation=_CITATION,
            task_templates=[ImageClassification(image_column="image", label_column="labels")],
        )

    def _split_generators(self, dl_manager):
        data_files = dl_manager.download_and_extract(_URLS)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "files": dl_manager.iter_files([data_files["train"]]),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "files": dl_manager.iter_files([data_files["validation"]]),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "files": dl_manager.iter_files([data_files["test"]]),
                },
            ),
        ]

    def _generate_examples(self, files):
        for i, path in enumerate(files):
            file_name = os.path.basename(path)
            if file_name.endswith(".jpg"):
                yield i, {
                    "image_file_path": path,
                    "image": path,
                    "labels": os.path.basename(os.path.dirname(path)).lower(),
                }