# Copyright (c) 2024-2026 Tencent Zhuque Lab. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Requirement: Any integration or derivative work must explicitly attribute
# Tencent Zhuque Lab (https://github.com/Tencent/AI-Infra-Guard) in its
# documentation or user interface, as detailed in the NOTICE file.

import logging
import time
from typing import Literal

from pydantic import BaseModel


class contentSchema(BaseModel):
    timestamp: str = str(time.time())


# 大标题
class newPlanStep(contentSchema):
    stepId: str
    title: str


# 步骤状态更新
class statusUpdate(contentSchema):
    stepId: str
    brief: str
    description: str
    status: Literal["running", "completed", "failed"]


# 工具使用
class toolUsed(contentSchema):
    stepId: str
    tool_id: str
    tool_name: str | None = None
    brief: str
    status: Literal["todo", "doing", "done"]
    params: str


# 工具日志
class actionLog(contentSchema):
    tool_id: str
    tool_name: str
    stepId: str
    log: str


# 错误日志
class errorLog(contentSchema):
    msg: str


class AgentMsg(BaseModel):
    type: str
    content: dict


class McpLogger:
    def __init__(self):
        logger = logging.getLogger("mcpLogger")
        logger.setLevel(logging.INFO)
        # 创建控制台处理器
        console_handler = logging.StreamHandler()
        console_handler.setLevel(logging.INFO)
        # 设置日志格式
        formatter = logging.Formatter("%(message)s")
        console_handler.setFormatter(formatter)
        logger.addHandler(console_handler)
        self.logger = logger

    def _log(self, type: str, content: BaseModel | dict):
        if isinstance(content, BaseModel):
            content.timestamp = str(time.time())
            content = content.model_dump()
        self.logger.info(AgentMsg(type=type, content=content).model_dump_json())

    def new_plan_step(self, stepId: str, stepName: str):
        self._log("newPlanStep", newPlanStep(stepId=stepId, title=stepName))

    def status_update(
        self,
        stepId: str,
        brief: str,
        description: str,
        status: Literal["running", "completed", "failed"],
    ):
        self._log(
            "statusUpdate",
            statusUpdate(stepId=stepId, brief=brief, description=description, status=status),
        )

    def tool_used(
        self,
        stepId: str,
        tool_id: str,
        brief: str,
        status: Literal["todo", "doing", "done"],
        tool_name: str = None,
        params: str = "",
    ):
        self._log(
            "toolUsed",
            toolUsed(
                stepId=stepId,
                tool_id=tool_id,
                tool_name=tool_name,
                brief=brief,
                status=status,
                params=params,
            ),
        )

    def action_log(self, tool_id: str, tool_name: str, stepId: str, log: str):
        self._log(
            "actionLog", actionLog(tool_id=tool_id, tool_name=tool_name, stepId=stepId, log=log)
        )

    def result_update(self, content: dict):
        self._log("resultUpdate", content)

    def error_log(self, msg: str):
        self._log("error", errorLog(msg=msg))


mcpLogger = McpLogger()

if __name__ == "__main__":
    mcpLogger.new_plan_step(stepId="0", stepName="Step 1")
    mcpLogger.new_plan_step(stepId="1", stepName="Step 2")
    mcpLogger.new_plan_step(stepId="2", stepName="Step 3")
    # mcpLogger.status_update(stepId="step1", brief="Step 1", description="Step 1", status="running")
    # mcpLogger.tool_used(stepId="step1", tool_id="tool1", brief="Tool 1", status="todo")
    # mcpLogger.action_log(tool_id="tool1", tool_name="Tool 1", stepId="step1", log="Log 1")
    # mcpLogger.result_update(content={"a": "b"})
