import datasets

_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Small image-text set},
author={Plaban Nayak},
year={2023}
}
"""
_DESCRIPTION = """\
Demo dataset for testing or showing image-text capabilities.
"""
_HOMEPAGE = "https://huggingface.co/datasets/Plaban81/image-demo"

_LICENSE = ""

_REPO = "https://huggingface.co/datasets/Plaban81/image-demo"

_URL ="https://huggingface.co/datasets/Plaban81/image-demo/resolve/main/images.json.gz"

descriptions = ['kajol', 'kagna', 'nohra', 'aish', 'kareena', 'shakti']
#
class image_demo(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((_URL)
        print(images_archive)
        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."""
        
        for idx, (filepath, image) in enumerate(images):
            #description = filepath.split('/')[-1][:-4]
            #description = description.replace('_', ' ')
            yield idx, {
                "image": {"path": filepath, "image": image.read()},
                "text": descriptions[idx],
            }