import csv
import datasets
from datasets.tasks import TextClassification


_DESCRIPTION = """\
Sentiment analysis dataset extracted and labeled from Digikala and Snapp Food comments
"""

_DOWNLOAD_URLS = {

    "train": "https://huggingface.co/datasets/hezarai/sentiment-dksf/raw/main/sentiment_dksf_train.csv",
    "test": "https://huggingface.co/datasets/hezarai/sentiment-dksf/raw/main/sentiment_dksf_test.csv"
}


class SentimentDKSF(datasets.GeneratorBasedBuilder):
    """Sentiment analysis on Digikala/SnappFood comments"""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["negative", "positive", "neutral"])}
            ),
            supervised_keys=None,
            homepage="https://huggingface.co/datasets/hezar-ai/sentiment-dksf",
            task_templates=[TextClassification(text_column="text", label_column="label")],
        )

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

    def _generate_examples(self, filepath):
        """Generate examples."""
        label_mapping = {"negative": 0, "positive": 1, "neutral": 2}
        with open(filepath, encoding="utf-8") as csv_file:
            csv_reader = csv.reader(
                csv_file, quotechar='"', skipinitialspace=True
            )
            for id_, row in enumerate(csv_reader):
                text, label = row
                label = label_mapping[label]
                yield id_, {"text": text, "label": label}