"""LLM fallback: classify unmatched DXF text entities as rooms via LiteLLM."""
import json
import logging
import os

from openai import AsyncOpenAI

logger = logging.getLogger(__name__)

_client: AsyncOpenAI | None = None


def _get_client() -> AsyncOpenAI:
    global _client
    if _client is None:
        _client = AsyncOpenAI(
            base_url=os.getenv("LITELLM_BASE_URL", "http://host.docker.internal:4000"),
            api_key=os.getenv("LITELLM_API_KEY", "sk-dummy"),
        )
    return _client


SYSTEM = """You are a specialist extracting room data from Czech architectural DXF floor plans.
You receive text entities (text, x, y) that were not matched by rule-based parsing.

Identify pairs of room number + Czech room name/description.
Czech room numbers: 4-6 digit codes, sometimes prefixed with "č.m.".
Czech room names: e.g. "Chodba", "Serverovna", "Sklep", "WC", "Kancelář", etc.

Return ONLY a JSON array of objects:
{"room": "XXXXX", "description": "Czech name", "confidence": 0.0-1.0}

Skip: measurements (m2, m²), material names (beton, dlažba), dimensions, unrelated text.
Only include entries with confidence > 0.5."""


async def enhance_with_llm(unmatched: list[dict]) -> list[dict]:
    api_key = os.getenv("LITELLM_API_KEY", "")
    if not api_key or api_key == "sk-dummy":
        logger.info("LITELLM_API_KEY not set — skipping LLM enhancement")
        return []

    sample = unmatched[:200]
    text_block = "\n".join(
        f'- "{t["text"]}" x={t["x"]:.0f} y={t["y"]:.0f}' for t in sample
    )
    model = os.getenv("LLM_MODEL", "gpt-4o-mini")

    try:
        resp = await _get_client().chat.completions.create(
            model=model,
            messages=[
                {"role": "system", "content": SYSTEM},
                {"role": "user", "content": f"Text entities:\n{text_block}"},
            ],
            temperature=0.1,
            max_tokens=2000,
        )
        raw = resp.choices[0].message.content or "[]"
        # Strip markdown code fences if present
        raw = raw.strip().removeprefix("```json").removeprefix("```").removesuffix("```").strip()
        data = json.loads(raw)
        if isinstance(data, dict):
            data = data.get("rooms", data.get("result", []))

        return [
            {
                "room": str(r["room"]),
                "description": r.get("description", ""),
                "x": 0.0,
                "y": 0.0,
                "source": "llm",
                "confidence": float(r.get("confidence", 0.7)),
            }
            for r in data
            if isinstance(r, dict) and r.get("room")
        ]
    except Exception as exc:
        logger.error("LLM enhancement failed: %s", exc)
        return []