"""Modell-Auswahlmenü (Phase 1: Cloud/OpenRouter).

Liefert die benutzbaren Chat-Modelle aus dem OpenRouter-Katalog mit Tool-Fähigkeit,
⬆/⬇-Preisen pro Mio. Token und einer geschätzten „$/Antwort" (aus dem live
gemessenen Turn-Token-Profil). Katalog wird gecacht. Lokale Modelle: Phase 2.

Siehe Ideen/MODELL_MENUE_KONZEPT.md.
"""
import time

import httpx

from app.runtime_config import runtime_settings

_CATALOG_URL = "https://openrouter.ai/api/v1/models"
_TTL = 3600.0  # Katalog ändert sich selten
_cache: dict = {"t": 0.0, "data": None}

# Im eval/tool_calling als web-suchtauglich bestätigt (✅). Klein halten/pflegen.
EVAL_VERIFIED = {
    "mistralai/mistral-small-3.2-24b-instruct",
}

# Annahmen, bis echte Turn-Messdaten vorliegen.
_DEFAULT_PROMPT_TOKENS = 700
_DEFAULT_COMPLETION_TOKENS = 130


def invalidate_cache() -> None:
    _cache["data"] = None


def _f(x) -> float:
    try:
        return float(x)
    except (TypeError, ValueError):
        return 0.0


async def _fetch_catalog(api_key: str) -> list:
    now = time.monotonic()
    if _cache["data"] is not None and now - _cache["t"] < _TTL:
        return _cache["data"]
    headers = {"Authorization": f"Bearer {api_key}"} if api_key else {}
    async with httpx.AsyncClient(timeout=30.0) as client:
        resp = await client.get(_CATALOG_URL, headers=headers)
        resp.raise_for_status()
        data = resp.json().get("data", [])
    _cache["data"] = data
    _cache["t"] = now
    return data


async def build_model_menu(store=None, cfg=None) -> dict:
    cfg = cfg or runtime_settings
    try:
        catalog = await _fetch_catalog(cfg.openrouter_api_key)
    except Exception as exc:  # noqa: BLE001 — graceful, UI zeigt Fehler
        return {"available": False, "error": str(exc), "models": [], "profile": {}}

    prof = store.get_turn_profile() if store is not None else {}
    avg_p = prof.get("avg_prompt") or _DEFAULT_PROMPT_TOKENS
    avg_c = prof.get("avg_completion") or _DEFAULT_COMPLETION_TOKENS

    models = []
    for m in catalog:
        modality = (m.get("architecture") or {}).get("modality") or ""
        if not modality.endswith("->text"):   # nur Chat-/Completion-Modelle
            continue
        pricing = m.get("pricing") or {}
        p_in = _f(pricing.get("prompt"))        # $/Token
        p_out = _f(pricing.get("completion"))
        mid = m.get("id")
        models.append({
            "id": mid,
            "name": m.get("name") or mid,
            "context_length": m.get("context_length"),
            "price_in_per_mtok": round(p_in * 1_000_000, 4),
            "price_out_per_mtok": round(p_out * 1_000_000, 4),
            "tool_supported": "tools" in (m.get("supported_parameters") or []),
            "eval_verified": mid in EVAL_VERIFIED,
            "est_cost_per_answer": round(avg_p * p_in + avg_c * p_out, 6),
        })
    # Sortierung: eval-bestätigt zuerst, dann tool-fähig, dann alphabetisch.
    models.sort(key=lambda x: (not x["eval_verified"], not x["tool_supported"], x["name"].lower()))

    return {
        "available": True,
        "models": models,
        "profile": {
            "avg_prompt": round(avg_p),
            "avg_completion": round(avg_c),
            "measured": bool(prof.get("samples")),
        },
        "current": cfg.openrouter_llm_model,
    }