import datasets
import pandas as pd

_CITATION = """\
@InProceedings{huggingface:dataset,
title = {hand-gesture-recognition-dataset},
author = {TrainingDataPro},
year = {2023}
}
"""

_DESCRIPTION = """\
The dataset consists of videos showcasing individuals demonstrating 5 different
hand gestures (*"one", "four", "small", "fist", and "me"*). Each video captures
a person prominently displaying a single hand gesture, allowing for accurate
identification and differentiation of the gestures.
The dataset offers a diverse range of individuals performing the gestures,
enabling the exploration of variations in hand shapes, sizes, and movements
across different individuals. 
The videos in the dataset are recorded in reasonable lighting conditions and
with adequate resolution, to ensure that the hand gestures can be easily
observed and studied.
"""
_NAME = 'hand-gesture-recognition-dataset'

_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"

_LICENSE = "cc-by-nc-nd-4.0"

_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"


class HandGestureRecognitionDataset(datasets.GeneratorBasedBuilder):

    def _info(self):
        return datasets.DatasetInfo(description=_DESCRIPTION,
                                    features=datasets.Features({
                                        'set_id': datasets.Value('int32'),
                                        'fist': datasets.Value('string'),
                                        'four': datasets.Value('string'),
                                        'me': datasets.Value('string'),
                                        'one': datasets.Value('string'),
                                        'small': datasets.Value('string')
                                    }),
                                    supervised_keys=None,
                                    homepage=_HOMEPAGE,
                                    citation=_CITATION,
                                    license=_LICENSE)

    def _split_generators(self, dl_manager):
        files = dl_manager.download_and_extract(f"{_DATA}files.zip")
        annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
        files = dl_manager.iter_files(files)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN,
                                    gen_kwargs={
                                        "files": files,
                                        'annotations': annotations
                                    }),
        ]

    def _generate_examples(self, files, annotations):
        annotations_df = pd.read_csv(annotations, sep=';')

        files = sorted(files)
        files = [files[i:i + 5] for i in range(0, len(files), 5)]
        for idx, files_set in enumerate(files):
            set_id = int((files_set[0].split('/'))[-2])
            data = {'set_id': set_id}

            for file in files_set:
                if 'fist' in file.lower():
                    data['fist'] = file
                elif 'four' in file.lower():
                    data['four'] = file
                elif 'me' in file.lower():
                    data['me'] = file
                elif 'one' in file.lower():
                    data['one'] = file
                elif 'small' in file.lower():
                    data['small'] = file
            print(data)
            yield idx, data