# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: Address all TODOs and remove all explanatory comments
"""id-review-gen: An Indonesian Review Generation Dataset."""


import csv
import pandas as pd

import datasets

_DESCRIPTION = """\
This dataset is built as a playground for review text generation.
"""

_HOMEPAGE = "https://github.com/jakartaresearch"

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_TRAIN_URL = (
    "https://github.com/jakartaresearch/id-review-gen/blob/main/data/id-review-generation.csv"
)


# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class ReviewGen(datasets.GeneratorBasedBuilder):
    """GooglePlayReview: An Indonesian Sentiment Analysis Dataset."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        features = datasets.Features(
            {"text": datasets.Value("string"), "label": datasets.Value("string")}
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION, features=features, homepage=_HOMEPAGE
        )

    def _split_generators(self, dl_manager):
        train_path = dl_manager.download_and_extract(_TRAIN_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}
            )
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath):
        """Generate examples."""
        df = pd.read_csv(filepath, encoding="utf-8")
        for item in df.itertuples():
            print(item)
            yield item.Index, {"text": item.text, "label": item.label}
        
        # with open(filepath, encoding="utf-8") as csv_file:
        #     csv_reader = csv.reader(csv_file, delimiter=",")
        #     next(csv_reader)
        #     for id_, row in enumerate(csv_reader):
        #         text, label = row
        #         yield id_, {"text": text, "label": label}