from __future__ import annotations

import re
from datetime import date

import dateparser
import spacy
from spacy.language import Language

from models import ParseResponse

# ── Language model registry ──────────────────────────────────────────────────

_MODEL_NAMES: dict[str, str] = {
    "de": "de_core_news_sm",
    "en": "en_core_web_sm",
    "es": "es_core_news_sm",
}

_nlp_cache: dict[str, Language] = {}


def get_nlp(lang: str) -> Language:
    if lang not in _MODEL_NAMES:
        raise ValueError(f"Unsupported language: {lang!r}. Valid: {list(_MODEL_NAMES)}")
    if lang not in _nlp_cache:
        _nlp_cache[lang] = spacy.load(_MODEL_NAMES[lang])
    return _nlp_cache[lang]


def load_all_models() -> None:
    for lang in _MODEL_NAMES:
        get_nlp(lang)


# ── Step 1: Person name extraction ──────────────────────────────────────────

def extract_person_names(doc) -> list[str]:
    """Return PER entity texts in left-to-right span order."""
    return [ent.text for ent in doc.ents if ent.label_ == "PER"]