from fastapi import APIRouter, Depends, HTTPException
from pydantic import BaseModel
import spacy

from ..auth import verify_token

router = APIRouter(prefix="/analyze", tags=["analysis"])

LANGUAGE_MODELS: dict[str, str] = {
    "en": "en_core_web_sm",
    "fr": "fr_core_news_sm",
    "es": "es_core_news_sm",
    "it": "it_core_news_sm",
    "de": "de_core_news_sm",
}

_nlp_cache: dict[str, spacy.Language] = {}


def _get_nlp(language: str) -> spacy.Language:
    if language not in LANGUAGE_MODELS:
        raise HTTPException(
            status_code=400,
            detail=f"Unsupported language '{language}'. Supported: {list(LANGUAGE_MODELS)}",
        )
    if language not in _nlp_cache:
        _nlp_cache[language] = spacy.load(LANGUAGE_MODELS[language])
    return _nlp_cache[language]


class POSRequest(BaseModel):
    text: str
    language: str


class TokenInfo(BaseModel):
    text: str
    lemma: str
    pos: str
    tag: str
    dep: str
    is_stop: bool


class POSResponse(BaseModel):
    language: str
    tokens: list[TokenInfo]


@router.post("/pos", response_model=POSResponse)
def analyze_pos(request: POSRequest, _: dict = Depends(verify_token)) -> POSResponse:
    nlp = _get_nlp(request.language)
    doc = nlp(request.text)
    tokens = [
        TokenInfo(
            text=token.text,
            lemma=token.lemma_,
            pos=token.pos_,
            tag=token.tag_,
            dep=token.dep_,
            is_stop=token.is_stop,
        )
        for token in doc
        if not token.is_space
    ]
    return POSResponse(language=request.language, tokens=tokens)