"""
Reads ai_service_config.json from the shared config volume.
30-second TTL cache + env var overrides (dev credentials stay out of git).

Env var overrides (all optional):
  AI_PROVIDER          — "lmstudio" | "ollama" | "anthropic"
  LMSTUDIO_BASE_URL, LMSTUDIO_API_KEY, LMSTUDIO_MODEL
  OLLAMA_BASE_URL, OLLAMA_MODEL, OLLAMA_API_KEY
  ANTHROPIC_API_KEY, ANTHROPIC_MODEL
"""
import asyncio
import json
import os
import time
from copy import deepcopy
from pathlib import Path

from app.core.config import settings

_DEFAULT_CONFIG: dict = {
    "provider": "lmstudio",
    "timeout_seconds": 60,
    "max_retries": 2,
    "anthropic": {"api_key": "", "model": "claude-haiku-4-5-20251001"},
    "ollama": {"base_url": "http://host.docker.internal:11434/v1", "model": "llama3.2", "api_key": "ollama"},
    "lmstudio": {"base_url": "http://host.docker.internal:1234/v1", "model": "local-model", "api_key": "lm-studio"},
}

_cache: dict | None = None
_cache_at: float = 0.0
_CACHE_TTL = 30.0


def _read_config_sync() -> dict:
    path = Path(settings.CONFIG_PATH)
    if not path.exists():
        return _apply_env_overrides(deepcopy(_DEFAULT_CONFIG))
    with open(path) as f:
        return _apply_env_overrides(json.load(f))


def _apply_env_overrides(config: dict) -> dict:
    cfg = deepcopy(config)

    if v := os.environ.get("AI_PROVIDER"):
        cfg["provider"] = v

    lms = cfg.setdefault("lmstudio", {})
    if v := os.environ.get("LMSTUDIO_BASE_URL"):
        lms["base_url"] = v
    if v := os.environ.get("LMSTUDIO_API_KEY"):
        lms["api_key"] = v
    if v := os.environ.get("LMSTUDIO_MODEL"):
        lms["model"] = v

    oll = cfg.setdefault("ollama", {})
    if v := os.environ.get("OLLAMA_BASE_URL"):
        oll["base_url"] = v
    if v := os.environ.get("OLLAMA_MODEL"):
        oll["model"] = v
    if v := os.environ.get("OLLAMA_API_KEY"):
        oll["api_key"] = v

    ant = cfg.setdefault("anthropic", {})
    if v := os.environ.get("ANTHROPIC_API_KEY"):
        ant["api_key"] = v
    if v := os.environ.get("ANTHROPIC_MODEL"):
        ant["model"] = v

    return cfg


async def load_ai_config() -> dict:
    global _cache, _cache_at
    now = time.monotonic()
    if _cache is not None and (now - _cache_at) < _CACHE_TTL:
        return _cache
    data = await asyncio.to_thread(_read_config_sync)
    _cache = data
    _cache_at = now
    return data