"""LLM 调用共享工具（OpenAI 兼容 API / Cursor SDK）。"""

from __future__ import annotations

import json
import re
from typing import Any

import certifi
import httpx

from daily.config import env, env_int
from daily.cursor_client import cursor_chat

_JSON_BLOCK = re.compile(r"```(?:json)?\s*([\s\S]*?)```", re.IGNORECASE)


def extract_json_object(text: str) -> dict[str, Any]:
    text = text.strip()
    if not text:
        return {}
    try:
        data = json.loads(text)
        return data if isinstance(data, dict) else {}
    except json.JSONDecodeError:
        pass
    match = _JSON_BLOCK.search(text)
    if match:
        try:
            data = json.loads(match.group(1).strip())
            return data if isinstance(data, dict) else {}
        except json.JSONDecodeError:
            pass
    start, end = text.find("{"), text.rfind("}")
    if start >= 0 and end > start:
        try:
            data = json.loads(text[start : end + 1])
            return data if isinstance(data, dict) else {}
        except json.JSONDecodeError:
            pass
    return {}


def _has_openai_configured() -> bool:
    return bool(env("DAILY_LLM_API_KEY") or env("OPENAI_API_KEY"))


def _has_cursor_configured() -> bool:
    return bool(env("CURSOR_API_KEY"))


def llm_provider() -> str:
    raw = (env("DAILY_LLM_PROVIDER") or "auto").strip().lower()
    if raw in {"openai", "cursor"}:
        return raw
    return "auto"


def resolve_llm_backend() -> str:
    """返回 openai | cursor | 空字符串。"""
    provider = llm_provider()
    has_openai = _has_openai_configured()
    has_cursor = _has_cursor_configured()

    if provider == "openai":
        if has_openai:
            return "openai"
        return "cursor" if has_cursor else ""

    if provider == "cursor":
        if has_cursor:
            return "cursor"
        return "openai" if has_openai else ""

    agent_mode = (env("DAILY_REPORT_MODE") or "").strip().lower() == "agent"
    if agent_mode and has_cursor:
        return "cursor"
    if has_openai:
        return "openai"
    if has_cursor:
        return "cursor"
    return ""


def _openai_chat(system: str, user: str) -> str:
    api_key = (env("DAILY_LLM_API_KEY") or env("OPENAI_API_KEY") or "").strip()
    if not api_key:
        return ""
    base = (env("DAILY_LLM_API_BASE") or env("OPENAI_API_BASE") or "https://api.openai.com/v1").rstrip("/")
    model = env("DAILY_LLM_MODEL") or env("OPENAI_MODEL") or "gpt-4o-mini"
    timeout = env_int("DAILY_LLM_TIMEOUT", 120)
    payload = {
        "model": model,
        "temperature": 0.2,
        "messages": [
            {"role": "system", "content": system},
            {"role": "user", "content": user},
        ],
    }
    with httpx.Client(timeout=timeout, verify=certifi.where()) as client:
        resp = client.post(
            f"{base}/chat/completions",
            headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
            json=payload,
        )
        resp.raise_for_status()
        data = resp.json()
    return str(data["choices"][0]["message"]["content"] or "").strip()


def llm_chat(system: str, user: str) -> str:
    backend = resolve_llm_backend()
    if backend == "openai":
        return _openai_chat(system, user)
    if backend == "cursor":
        return cursor_chat(system, user)
    return ""


def has_llm_configured() -> bool:
    return bool(resolve_llm_backend())