import csv
import datasets

_CITATION = """\
@dataset{qasim2025animalsounds,
  title = {Animal Sound Classification Dataset},
  author = {Muhammad Qasim},
  year = {2025},
  url = {https://huggingface.co/datasets/MuhammadQASIM111/Animal_Sound_Classification}
}
"""

_DESCRIPTION = """\
A meticulously curated dataset of labeled animal sounds (dogs, cats, cows) for audio classification tasks. The dataset contains trimmed and cleaned audio clips with labels.
"""

_HOMEPAGE = "https://huggingface.co/datasets/MuhammadQASIM111/Animal_Sound_Classification"

_LICENSE = "mit"

_URLS = {
    "train": "ML1_features.csv"
}

class AnimalSoundClassification(datasets.GeneratorBasedBuilder):
    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                "audio": datasets.Value("string"),  # path to audio file or URL
                "label": datasets.ClassLabel(names=["dog", "cat", "cow"]),
            }),
            supervised_keys=("audio", "label"),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

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

    def _generate_examples(self, filepath):
        with open(filepath, encoding="utf-8") as csv_file:
            reader = csv.DictReader(csv_file)
            for id_, row in enumerate(reader):
                yield id_, {
                    "audio": row["audio"],  # Make sure column name matches your CSV
                    "label": row["label"].lower(),  # Lowercasing to match ClassLabel names
                }