import datasets

_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Small image-text set},
author={James Briggs},
year={2022}
}
"""

_DESCRIPTION = """\
DEMO..
"""
_HOMEPAGE = "https://huggingface.co/datasets/Souvikrad365/datademo"

_LICENSE = ""
#descriptions=['CT scan image of a brain with intracranial hemorrhage']
#description1 = "CT scan image of a brain with intracranial hemorrhage"

# Create a list with the description repeated 220 times
descriptions = ['CT scan image of a brain with intracranial hemorrhage',
 'CT scan image of a brain with intracranial hemorrhage',
 'CT scan image of a brain with intracranial hemorrhage',
 'CT scan image of a brain with intracranial hemorrhage',
 'CT scan image of a brain with intracranial hemorrhage',
 'CT scan image of a brain with intracranial hemorrhage',
 'CT scan image of a brain with intracranial hemorrhage',
 'CT scan image of a brain with intracranial hemorrhage',
 'CT scan image of a brain with intracranial hemorrhage',
 'CT scan image of a brain with intracranial hemorrhage']

_REPO = "https://huggingface.co/datasets/Souvikrad365/datademo"

class ImageSet(datasets.GeneratorBasedBuilder):
    """Small sample of image-text pairs"""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    'text': datasets.Value("string"),
                    'image': datasets.Image(),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        images_archive = dl_manager.download(f"{_REPO}/resolve/main/10imagesdataset.tar.gz")
        image_iters = dl_manager.iter_archive(images_archive)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "images": image_iters
                }
            ),
        ]

    def _generate_examples(self, images):
        """ This function returns the examples in the raw (text) form."""
        
        idx=0
        for filepath,image in images:
            yield idx,{
                "image":{"path":filepath,"bytes":image.read()},
                "text":descriptions[idx]
            }
            idx+=1