from __future__ import annotations

import json
import re
import uuid
from typing import Any, Dict, List

from .base import AgentProvider, AssistantTurn, ToolCall


class RuleBasedMeetingProvider(AgentProvider):
    """Offline provider for demos. It still uses the same tool-call loop as an LLM provider."""

    def generate(self, messages: List[Dict[str, Any]], tools: List[Dict[str, Any]]) -> AssistantTurn:
        last = messages[-1]
        if last.get("role") == "tool":
            return AssistantTurn(content=_summarize_tool_result(last.get("name", ""), last.get("content", "{}")))
        user_message = _last_user_message(messages)
        knowledge_base_id = _extract_knowledge_base_id(user_message)
        path = _extract_path(user_message)
        if _looks_like_list_bases(user_message):
            return AssistantTurn(
                tool_calls=[ToolCall(id=f"call_{uuid.uuid4().hex}", name="list_knowledge_bases", arguments={})]
            )
        if _looks_like_import(user_message) and path:
            args: dict[str, Any] = {"file_path": path}
            if knowledge_base_id:
                args["knowledge_base_id"] = knowledge_base_id
            meeting_date = _extract_date(user_message)
            if meeting_date:
                args["meeting_date"] = meeting_date
            return AssistantTurn(
                tool_calls=[ToolCall(id=f"call_{uuid.uuid4().hex}", name="import_meeting_transcript", arguments=args)]
            )
        query_args: dict[str, Any] = {"query": user_message, "limit": 8}
        if knowledge_base_id:
            query_args["knowledge_base_id"] = knowledge_base_id
        return AssistantTurn(
            tool_calls=[
                ToolCall(
                    id=f"call_{uuid.uuid4().hex}",
                    name="query_knowledge",
                    arguments=query_args,
                )
            ]
        )


def _summarize_tool_result(tool_name: str, raw: str) -> str:
    try:
        data = json.loads(raw)
    except json.JSONDecodeError:
        return raw
    if tool_name == "import_meeting_transcript":
        counts = data.get("counts", {})
        return (
            "已导入会议并更新知识库周会台账。\n"
            f"- knowledge_base_id: {data.get('knowledge_base_id') or data.get('team_id')}\n"
            f"- meeting_id: {data.get('meeting_id')}\n"
            f"- ledger: {data.get('ledger_path')}\n"
            f"- 抽取：指标 {counts.get('metrics', 0)}，待办 {counts.get('actions', 0)}，风险 {counts.get('risks', 0)}，"
            f"完成事项 {counts.get('completed', 0)}，追踪项 {counts.get('followups', 0)}"
        )
    if tool_name == "query_knowledge":
        return data.get("answer", json.dumps(data, ensure_ascii=False, indent=2))
    if tool_name == "list_knowledge_bases":
        bases = data.get("knowledge_bases", [])
        return "当前已有知识库：" + ("、".join(bases) if bases else "暂无")
    return json.dumps(data, ensure_ascii=False, indent=2)


def _last_user_message(messages: List[Dict[str, Any]]) -> str:
    for message in reversed(messages):
        if message.get("role") == "user":
            return str(message.get("content", ""))
    return ""


def _looks_like_import(text: str) -> bool:
    return any(word in text for word in ("导入", "读取", "更新", "import")) and bool(_extract_path(text))


def _extract_path(text: str) -> str:
    quoted = re.search(r"['\"]([^'\"]+\.(?:txt|md))['\"]", text)
    if quoted:
        return quoted.group(1)
    match = re.search(r"([A-Za-z]:\\[^\s，。；]+?\.(?:txt|md))", text)
    return match.group(1) if match else ""


def _extract_knowledge_base_id(text: str) -> str | None:
    match = re.search(r"(?:knowledge_base_id|知识库|库|team_id|团队|team)[:：= ]*([A-Za-z0-9_\-\u4e00-\u9fff]+)", text)
    return match.group(1) if match else None


def _extract_date(text: str) -> str:
    match = re.search(r"\d{4}-\d{2}-\d{2}", text)
    return match.group(0) if match else ""



def _looks_like_list_bases(text: str) -> bool:
    return any(word in text for word in ("有哪些知识库", "知识库列表", "列出知识库", "可用知识库", "有哪些库"))