import csv
from datasets import DatasetInfo, Features, Value, SplitGenerator, Split, GeneratorBasedBuilder

_CITATION = ""
_DESCRIPTION = "Medical translation pairs with semantic glosses and UMLS CUIs."

class MedicalTSV(GeneratorBasedBuilder):
    VERSION = "1.0.0"

    def _info(self):
        return DatasetInfo(
            description=_DESCRIPTION,
            features=Features({
                "sentence_id": Value("string"),
                "src_lang": Value("string"),
                "tgt_lang": Value("string"),
                "gender_variant": Value("string"),
                "source_text": Value("string"),
                "target_text": Value("string"),
                "semantic_gloss": Value("string"),
                "CUI_semantic_gloss": Value("string"),
            }),
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # If you later add dev/test, replicate with different files or config.
        train_path = dl_manager.manual_dir / "train.tsv"
        return [SplitGenerator(name=Split.TRAIN, gen_kwargs={"filepath": train_path})]

    def _generate_examples(self, filepath):
        with open(filepath, "r", encoding="utf-8") as f:
            reader = csv.DictReader(f, delimiter="\t")
            for i, row in enumerate(reader):
                yield i, {
                    "sentence_id": row.get("sentence_id", ""),
                    "src_lang": row.get("src_lang", ""),
                    "tgt_lang": row.get("tgt_lang", ""),
                    "gender_variant": row.get("gender_variant", ""),
                    "source_text": row.get("source_text", ""),
                    "target_text": row.get("target_text", ""),
                    "semantic_gloss": row.get("semantic_gloss", ""),
                    "CUI_semantic_gloss": row.get("CUI_semantic_gloss", ""),
                }