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

from __future__ import annotations

import json
import logging
import os
import re
from typing import Any

import certifi
import httpx

from daily.config import ROOT, env, env_int

logger = logging.getLogger(__name__)

_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 _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 _cursor_chat(system: str, user: str) -> str:
    api_key = (env("CURSOR_API_KEY") or "").strip()
    if not api_key:
        return ""
    from cursor_sdk import Agent, AgentOptions, Client, CursorAgentError, LocalAgentOptions

    from daily.bridge_manager import warm_cursor_bridge

    cwd = env("DAILY_CURSOR_CWD") or str(ROOT)
    os.environ["CURSOR_CWD"] = cwd
    warm_cursor_bridge()
    model = env("CURSOR_MODEL") or "composer-2.5"
    prompt = f"{system}\n\n{user}"

    # SDK 默认 unary_timeout 只有 60s，CURSOR_MODEL=auto 时后端首 token 常超时；
    # 自建带大超时的 Client 绕开 _default_client()，unary/stream 都放大。
    unary_timeout = env_int("DAILY_CURSOR_UNARY_TIMEOUT", 300)
    stream_timeout = env_int("DAILY_CURSOR_STREAM_TIMEOUT", 900)
    client = Client(
        base_url=os.environ["CURSOR_SDK_BRIDGE_URL"],
        auth_token=os.environ["CURSOR_SDK_BRIDGE_TOKEN"],
        unary_timeout=unary_timeout,
        stream_timeout=stream_timeout,
    )
    try:
        result = Agent.prompt(
            prompt,
            AgentOptions(
                api_key=api_key,
                model=model,
                local=LocalAgentOptions(cwd=cwd),
            ),
            client=client,
        )
    except CursorAgentError as exc:
        raise RuntimeError(f"LLM 调用失败：{exc.message}") from exc
    finally:
        client.close()
    if result.status == "error":
        raise RuntimeError(f"LLM 调用失败：{result.result or '未知错误'}")
    return (result.result or "").strip()


def llm_chat(system: str, user: str) -> str:
    """优先 OpenAI 兼容 API，否则 Cursor SDK。"""
    if env("DAILY_LLM_API_KEY") or env("OPENAI_API_KEY"):
        return _openai_chat(system, user)
    if env("CURSOR_API_KEY"):
        return _cursor_chat(system, user)
    return ""


def has_cursor_configured() -> bool:
    return bool((env("CURSOR_API_KEY") or "").strip())


def cursor_agent_prompt(system: str, user: str) -> str:
    """仅 Cursor SDK Agent（可用 WebSearch 等工具），不走 OpenAI 兼容 API。"""
    if not has_cursor_configured():
        return ""
    return _cursor_chat(system, user)


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