"""
agent/agent.py
Agent 核心 —— 通过 SkillRegistry 统一调用本地工具和在线 MCP Skill
"""

import json
import time
from dataclasses import dataclass, field
from typing import Any

from config.settings import settings
from mcp.skill_registry import DispatchResult, SkillRegistry
from utils.logger import get_logger

logger = get_logger("Agent")


# ════════════════════════════════════════════════════════════════
# 消息 / 历史
# ════════════════════════════════════════════════════════════════

@dataclass
class Message:
    role:         str                    # system | user | assistant | tool
    content:      str        = ""
    tool_call_id: str        = ""
    tool_name:    str        = ""
    tool_calls:   list[dict] = field(default_factory=list)

    def to_api_dict(self) -> dict:
        d: dict[str, Any] = {"role": self.role}
        if self.role == "tool":
            d["content"]      = self.content
            d["tool_call_id"] = self.tool_call_id
        elif self.tool_calls:
            d["content"]    = self.content or None
            d["tool_calls"] = self.tool_calls
        else:
            d["content"] = self.content
        return d


# ════════════════════════════════════════════════════════════════
# LLM 客户端（OpenAI-compatible）
# ════════════════════════════════════════════════════════════════

class LLMClient:
    """
    OpenAI-compatible LLM 客户端
    支持 function calling / tool_calls
    """

    def __init__(self):
        try:
            from openai import OpenAI
            self._client = OpenAI(
                api_key=settings.llm.api_key or "sk-placeholder",
                base_url=settings.llm.api_base_url or None,
            )
            self._available = True
        except ImportError:
            logger.warning("⚠️  openai 未安装，LLM 调用将使用 mock 模式")
            self._available = False

    def chat(
        self,
        messages:    list[dict],
        tools:       list[dict] | None = None,
        stream:      bool              = False,
    ) -> dict:
        """
        发送对话请求

        Returns:
            {
              "content":    str,           # 文本回复（可能为空）
              "tool_calls": list[dict],    # function calling 调用列表
              "finish_reason": str,
            }
        """
        if not self._available:
            return self._mock_response(messages, tools)

        kwargs: dict[str, Any] = {
            "model":       settings.llm.model_name,
            "messages":    messages,
            "max_tokens":  settings.llm.max_tokens,
            "temperature": settings.llm.temperature,
            "stream":      stream,
        }
        if tools and settings.llm.function_calling:
            kwargs["tools"]       = [{"type": "function", "function": t} for t in tools[:128]]
            kwargs["tool_choice"] = "auto"

        resp = self._client.chat.completions.create(**kwargs)

        if stream:
            return self._collect_stream(resp)

        msg    = resp.choices[0].message
        finish = resp.choices[0].finish_reason
        return {
            "content":       msg.content or "",
            "tool_calls":    self._parse_tool_calls(msg.tool_calls),
            "finish_reason": finish,
        }

    @staticmethod
    def _parse_tool_calls(raw) -> list[dict]:
        if not raw:
            return []
        result = []
        for tc in raw:
            try:
                args = json.loads(tc.function.arguments)
            except (json.JSONDecodeError, AttributeError):
                args = {}
            result.append({
                "id":       tc.id,
                "name":     tc.function.name,
                "arguments": args,
            })
        return result

    @staticmethod
    def _collect_stream(stream) -> dict:
        content     = ""
        tool_calls  = []
        finish      = ""
        tc_buffers: dict[int, dict] = {}

        for chunk in stream:
            delta  = chunk.choices[0].delta
            finish = chunk.choices[0].finish_reason or finish

            if delta.content:
                content += delta.content

            if delta.tool_calls:
                for tc in delta.tool_calls:
                    idx = tc.index
                    if idx not in tc_buffers:
                        tc_buffers[idx] = {
                            "id":   tc.id or "",
                            "name": tc.function.name if tc.function else "",
                            "args": "",
                        }
                    if tc.function and tc.function.arguments:
                        tc_buffers[idx]["args"] += tc.function.arguments

        for buf in tc_buffers.values():
            try:
                args = json.loads(buf["args"])
            except json.JSONDecodeError:
                args = {}
            tool_calls.append({
                "id":        buf["id"],
                "name":      buf["name"],
                "arguments": args,
            })

        return {"content": content, "tool_calls": tool_calls, "finish_reason": finish}

    @staticmethod
    def _mock_response(messages: list[dict], tools: list[dict] | None) -> dict:
        """无 LLM 时的 mock 响应（用于测试）"""
        last = messages[-1].get("content", "") if messages else ""
        if tools and ("搜索" in last or "search" in last.lower()):
            return {
                "content":    None,
                "tool_calls": [{
                    "id":        "mock_001",
                    "name":      tools[0]["name"],
                    "arguments": {"query": last},
                }],
                "finish_reason": "tool_calls",
            }
        return {
            "content":       f"[Mock LLM] 收到: {last[:100]}",
            "tool_calls":    [],
            "finish_reason": "stop",
        }


# ════════════════════════════════════════════════════════════════
# Agent 核心
# ════════════════════════════════════════════════════════════════

class Agent:
    """
    Agent 核心

    通过 SkillRegistry 统一调用本地工具和在线 MCP Skill，
    Agent 无需感知工具来源。

    用法:
        registry = SkillRegistry()
        registry.register_local_many(CalculatorTool(), WebSearchTool())
        registry.connect_skills()   # 连接在线 MCP Skill

        agent = Agent(registry)
        reply = agent.chat("帮我搜索 Python 最新版本")
        print(reply)
    """

    SYSTEM_PROMPT = (
        "你是一个智能助手，可以调用工具完成用户的任务。\n"
        "调用工具时请确保参数完整准确。\n"
        "工具调用结果会自动返回给你，请根据结果给出最终回答。"
    )

    def __init__(
        self,
        registry:      SkillRegistry,
        system_prompt: str | None = None,
    ):
        self.registry      = registry
        self.llm           = LLMClient()
        self.history:      list[Message] = []
        self.system_prompt = system_prompt or self.SYSTEM_PROMPT
        self._max_steps    = settings.agent.max_chain_steps

        logger.info(
            f"🤖 Agent 初始化完成\n"
            f"   LLM      : {settings.llm.provider} / {settings.llm.model_name}\n"
            f"   工具总数  : {len(registry.get_all_schemas())} 个\n"
            f"   最大步数  : {self._max_steps}\n"
            f"   工具列表  :\n" +
            "\n".join(
                f"     {'🔵' if t['source'] == 'local' else '🟢'} "
                f"[{t['source']:20s}] {t['name']}"
                for t in registry.list_all_tools()
            )
        )

    # ── 对话入口 ──────────────────────────────────────────────

    def chat(self, user_input: str) -> str:
        """
        单轮对话入口（支持多步工具调用链）

        Args:
            user_input: 用户输入文本

        Returns:
            最终回复文本
        """
        logger.info(f"💬 用户输入: {user_input}")
        self.history.append(Message(role="user", content=user_input))

        reply = self._run_loop()

        self.history.append(Message(role="assistant", content=reply))
        # 历史记录截断（来自 config.yaml memory.max_history）
        max_h = settings.memory.max_history
        if len(self.history) > max_h:
            self.history = self.history[-max_h:]

        return reply

    def reset(self) -> None:
        """清空对话历史"""
        self.history.clear()
        logger.info("🔄 对话历史已清空")

    # ── 多步推理循环 ──────────────────────────────────────────

    def _run_loop(self) -> str:
        """
        多步工具调用循环

        流程:
            1. 构造消息列表 → 调用 LLM
            2. 若 LLM 返回 tool_calls → 执行工具 → 追加结果 → 回到 1
            3. 若 LLM 返回文本 → 结束，返回文本
        """
        tools   = self.registry.get_all_schemas()
        step    = 0
        loop_history: list[Message] = list(self.history)

        while step < self._max_steps:
            step += 1
            logger.info(f"🔁 推理步骤 {step}/{self._max_steps}")

            # 构造 API 消息列表
            messages = self._build_messages(loop_history)

            # 调用 LLM
            llm_resp = self.llm.chat(
                messages=messages,
                tools=tools if settings.llm.function_calling else None,
                stream=settings.llm.stream,
            )

            content     = llm_resp.get("content", "") or ""
            tool_calls  = llm_resp.get("tool_calls", [])
            finish      = llm_resp.get("finish_reason", "stop")

            logger.debug(
                f"   LLM 响应: finish={finish}  "
                f"tool_calls={len(tool_calls)}  "
                f"content={content[:80]}"
            )

            # ── 无工具调用：直接返回文本 ──────────────────────
            if not tool_calls:
                return content or "(无回复)"

            # ── 有工具调用：执行并追加结果 ────────────────────
            # 追加 assistant 消息（含 tool_calls）
            loop_history.append(Message(
                role="assistant",
                content=content,
                tool_calls=[{
                    "id":   tc["id"],
                    "type": "function",
                    "function": {
                        "name":      tc["name"],
                        "arguments": json.dumps(tc["arguments"], ensure_ascii=False),
                    },
                } for tc in tool_calls],
            ))

            # 执行每个工具调用
            for tc in tool_calls:
                result = self._execute_tool(tc["name"], tc["arguments"])
                loop_history.append(Message(
                    role="tool",
                    content=str(result),
                    tool_call_id=tc["id"],
                    tool_name=tc["name"],
                ))

            # finish_reason == stop 且有工具结果时继续循环让 LLM 总结
            if finish == "stop":
                break

        # 超过最大步数，强制返回最后一条 assistant 内容
        for msg in reversed(loop_history):
            if msg.role == "assistant" and msg.content:
                return msg.content
        return "（已达最大推理步数，无法给出最终回答）"

    # ── 工具执行 ──────────────────────────────────────────────

    def _execute_tool(
        self,
        tool_name:  str,
        arguments:  dict[str, Any],
    ) -> str:
        """执行工具调用，返回结果字符串"""
        logger.info(
            f"🔧 执行工具: {tool_name}\n"
            f"   来源: {self.registry.get_tool_info(tool_name)}\n"
            f"   参数: {json.dumps(arguments, ensure_ascii=False)[:200]}"
        )
        start  = time.time()
        result = self.registry.dispatch(tool_name, arguments)
        elapsed = time.time() - start

        icon = "✅" if result.success else "❌"
        logger.info(
            f"{icon} 工具结果: {tool_name}  "
            f"source={result.source}  耗时={elapsed:.2f}s\n"
            f"   {str(result)[:200]}"
        )
        return str(result)

    # ── 消息构造 ──────────────────────────────────────────────

    def _build_messages(self, history: list[Message]) -> list[dict]:
        messages = [{"role": "system", "content": self.system_prompt}]
        messages += [m.to_api_dict() for m in history]
        return messages

    # ── 调试工具 ──────────────────────────────────────────────

    def show_tools(self) -> str:
        """打印所有可用工具（含来源）"""
        tools = self.registry.list_all_tools()
        lines = [f"📦 可用工具（共 {len(tools)} 个）:", "─" * 50]
        for t in tools:
            icon = "🔵" if t["source"] == "local" else "🟢"
            lines.append(
                f"  {icon} [{t['source']:25s}] {t['name']}\n"
                f"       {t['description']}"
            )
        return "\n".join(lines)


# ════════════════════════════════════════════════════════════════
# Demo 入口
# ════════════════════════════════════════════════════════════════

def create_agent() -> tuple[Agent, SkillRegistry]:
    """
    工厂函数：创建并初始化 Agent + SkillRegistry

    Returns:
        (agent, registry) —— registry 需在程序退出时调用 .close()
    """
    from tools.calculator      import CalculatorTool
    from tools.code_executor   import CodeExecutorTool
    from tools.file_reader     import FileReaderTool
    from tools.ssh_docker      import SSHDockerTool
    from tools.static_analyzer import StaticAnalyzerTool
    from tools.web_search      import WebSearchTool

    registry = SkillRegistry()

    # 注册本地工具（根据 config.yaml mcp.enabled_tools 过滤）
    enabled = settings.mcp.enabled_tools
    tool_map = {
        "calculator":      CalculatorTool,
        "web_search":      WebSearchTool,
        "file_reader":     FileReaderTool,
        "code_executor":   CodeExecutorTool,
        "static_analyzer": StaticAnalyzerTool,
        "ssh_docker":      SSHDockerTool,
    }
    for name in enabled:
        if name in tool_map:
            registry.register_local(tool_map[name]())

    # 连接在线 MCP Skill（来自 config.yaml mcp_skills）
    registry.connect_skills()

    agent = Agent(registry)
    return agent, registry


if __name__ == "__main__":
    import atexit

    print(settings.display())

    agent, registry = create_agent()
    atexit.register(registry.close)   # 程序退出时自动关闭连接

    print(agent.show_tools())
    print("─" * 60)
    print("💡 输入 'exit' 退出，'reset' 清空历史，'tools' 查看工具列表")
    print("─" * 60)

    while True:
        try:
            user_input = input("\n🧑 You: ").strip()
        except (EOFError, KeyboardInterrupt):
            print("\n👋 再见！")
            break

        if not user_input:
            continue
        if user_input.lower() == "exit":
            print("👋 再见！")
            break
        if user_input.lower() == "reset":
            agent.reset()
            print("🔄 对话历史已清空")
            continue
        if user_input.lower() == "tools":
            print(agent.show_tools())
            continue

        reply = agent.chat(user_input)
        print(f"\n🤖 Agent: {reply}")