760 lines
30 KiB
Python
760 lines
30 KiB
Python
from collections import defaultdict
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from datetime import datetime, timedelta
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from typing import Any, Dict, Tuple, List
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from src.common.logger import get_module_logger
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from src.manager.async_task_manager import AsyncTask
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from ...common.database.database import db # This db is the Peewee database instance
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from ...common.database.database_model import OnlineTime, LLMUsage, Messages # Import the Peewee model
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from src.manager.local_store_manager import local_storage
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logger = get_module_logger("maibot_statistic")
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# 统计数据的键
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TOTAL_REQ_CNT = "total_requests"
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TOTAL_COST = "total_cost"
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REQ_CNT_BY_TYPE = "requests_by_type"
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REQ_CNT_BY_USER = "requests_by_user"
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REQ_CNT_BY_MODEL = "requests_by_model"
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IN_TOK_BY_TYPE = "in_tokens_by_type"
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IN_TOK_BY_USER = "in_tokens_by_user"
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IN_TOK_BY_MODEL = "in_tokens_by_model"
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OUT_TOK_BY_TYPE = "out_tokens_by_type"
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OUT_TOK_BY_USER = "out_tokens_by_user"
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OUT_TOK_BY_MODEL = "out_tokens_by_model"
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TOTAL_TOK_BY_TYPE = "tokens_by_type"
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TOTAL_TOK_BY_USER = "tokens_by_user"
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TOTAL_TOK_BY_MODEL = "tokens_by_model"
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COST_BY_TYPE = "costs_by_type"
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COST_BY_USER = "costs_by_user"
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COST_BY_MODEL = "costs_by_model"
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ONLINE_TIME = "online_time"
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TOTAL_MSG_CNT = "total_messages"
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MSG_CNT_BY_CHAT = "messages_by_chat"
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class OnlineTimeRecordTask(AsyncTask):
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"""在线时间记录任务"""
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def __init__(self):
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super().__init__(task_name="Online Time Record Task", run_interval=60)
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self.record_id: int | None = None # Changed to int for Peewee's default ID
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"""记录ID"""
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self._init_database() # 初始化数据库
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@staticmethod
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def _init_database():
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"""初始化数据库"""
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with db.atomic(): # Use atomic operations for schema changes
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OnlineTime.create_table(safe=True) # Creates table if it doesn't exist, Peewee handles indexes from model
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async def run(self):
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try:
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current_time = datetime.now()
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extended_end_time = current_time + timedelta(minutes=1)
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if self.record_id:
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# 如果有记录，则更新结束时间
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query = OnlineTime.update(end_timestamp=extended_end_time).where(OnlineTime.id == self.record_id)
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updated_rows = query.execute()
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if updated_rows == 0:
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# Record might have been deleted or ID is stale, try to find/create
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self.record_id = None # Reset record_id to trigger find/create logic below
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if not self.record_id: # Check again if record_id was reset or initially None
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# 如果没有记录，检查一分钟以内是否已有记录
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# Look for a record whose end_timestamp is recent enough to be considered ongoing
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recent_record = (
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OnlineTime.select()
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.where(OnlineTime.end_timestamp >= (current_time - timedelta(minutes=1)))
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.order_by(OnlineTime.end_timestamp.desc())
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.first()
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)
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if recent_record:
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# 如果有记录，则更新结束时间
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self.record_id = recent_record.id
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recent_record.end_timestamp = extended_end_time
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recent_record.save()
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else:
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# 若没有记录，则插入新的在线时间记录
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new_record = OnlineTime.create(
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timestamp=current_time.timestamp(), # 添加此行
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start_timestamp=current_time,
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end_timestamp=extended_end_time,
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duration=5, # 初始时长为5分钟
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)
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self.record_id = new_record.id
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except Exception as e:
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logger.error(f"在线时间记录失败，错误信息：{e}")
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def _format_online_time(online_seconds: int) -> str:
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"""
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格式化在线时间
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:param online_seconds: 在线时间（秒）
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:return: 格式化后的在线时间字符串
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"""
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total_oneline_time = timedelta(seconds=online_seconds)
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days = total_oneline_time.days
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hours = total_oneline_time.seconds // 3600
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minutes = (total_oneline_time.seconds // 60) % 60
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seconds = total_oneline_time.seconds % 60
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if days > 0:
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# 如果在线时间超过1天，则格式化为"X天X小时X分钟"
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return f"{total_oneline_time.days}天{hours}小时{minutes}分钟{seconds}秒"
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elif hours > 0:
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# 如果在线时间超过1小时，则格式化为"X小时X分钟X秒"
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return f"{hours}小时{minutes}分钟{seconds}秒"
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else:
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# 其他情况格式化为"X分钟X秒"
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return f"{minutes}分钟{seconds}秒"
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class StatisticOutputTask(AsyncTask):
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"""统计输出任务"""
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SEP_LINE = "-" * 84
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def __init__(self, record_file_path: str = "maibot_statistics.html"):
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# 延迟300秒启动，运行间隔300秒
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super().__init__(task_name="Statistics Data Output Task", wait_before_start=0, run_interval=300)
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self.name_mapping: Dict[str, Tuple[str, float]] = {}
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"""
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联系人/群聊名称映射 {聊天ID: (联系人/群聊名称, 记录时间（timestamp）)}
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注：设计记录时间的目的是方便更新名称，使联系人/群聊名称保持最新
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"""
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self.record_file_path: str = record_file_path
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"""
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记录文件路径
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"""
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now = datetime.now()
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if "deploy_time" in local_storage:
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# 如果存在部署时间，则使用该时间作为全量统计的起始时间
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deploy_time = datetime.fromtimestamp(local_storage["deploy_time"])
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else:
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# 否则，使用最大时间范围，并记录部署时间为当前时间
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deploy_time = datetime(2000, 1, 1)
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local_storage["deploy_time"] = now.timestamp()
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self.stat_period: List[Tuple[str, timedelta, str]] = [
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("all_time", now - deploy_time, "自部署以来"), # 必须保留"all_time"
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("last_7_days", timedelta(days=7), "最近7天"),
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("last_24_hours", timedelta(days=1), "最近24小时"),
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("last_hour", timedelta(hours=1), "最近1小时"),
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]
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"""
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统计时间段 [(统计名称, 统计时间段, 统计描述), ...]
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"""
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def _statistic_console_output(self, stats: Dict[str, Any], now: datetime):
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"""
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输出统计数据到控制台
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:param stats: 统计数据
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:param now: 基准当前时间
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"""
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# 输出最近一小时的统计数据
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output = [
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self.SEP_LINE,
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f" 最近1小时的统计数据 (自{now.strftime('%Y-%m-%d %H:%M:%S')}开始，详细信息见文件：{self.record_file_path})",
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self.SEP_LINE,
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self._format_total_stat(stats["last_hour"]),
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"",
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self._format_model_classified_stat(stats["last_hour"]),
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"",
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self._format_chat_stat(stats["last_hour"]),
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self.SEP_LINE,
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"",
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]
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logger.info("\n" + "\n".join(output))
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async def run(self):
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try:
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now = datetime.now()
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# 收集统计数据
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stats = self._collect_all_statistics(now)
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# 输出统计数据到控制台
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self._statistic_console_output(stats, now)
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# 输出统计数据到html文件
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self._generate_html_report(stats, now)
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except Exception as e:
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logger.exception(f"输出统计数据过程中发生异常，错误信息：{e}")
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# -- 以下为统计数据收集方法 --
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@staticmethod
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def _collect_model_request_for_period(collect_period: List[Tuple[str, datetime]]) -> Dict[str, Any]:
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"""
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收集指定时间段的LLM请求统计数据
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:param collect_period: 统计时间段
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"""
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if not collect_period:
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return {}
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# 排序-按照时间段开始时间降序排列（最晚的时间段在前）
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collect_period.sort(key=lambda x: x[1], reverse=True)
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stats = {
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period_key: {
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TOTAL_REQ_CNT: 0,
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REQ_CNT_BY_TYPE: defaultdict(int),
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REQ_CNT_BY_USER: defaultdict(int),
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REQ_CNT_BY_MODEL: defaultdict(int),
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IN_TOK_BY_TYPE: defaultdict(int),
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IN_TOK_BY_USER: defaultdict(int),
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IN_TOK_BY_MODEL: defaultdict(int),
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OUT_TOK_BY_TYPE: defaultdict(int),
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OUT_TOK_BY_USER: defaultdict(int),
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OUT_TOK_BY_MODEL: defaultdict(int),
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TOTAL_TOK_BY_TYPE: defaultdict(int),
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TOTAL_TOK_BY_USER: defaultdict(int),
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TOTAL_TOK_BY_MODEL: defaultdict(int),
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TOTAL_COST: 0.0,
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COST_BY_TYPE: defaultdict(float),
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COST_BY_USER: defaultdict(float),
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COST_BY_MODEL: defaultdict(float),
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}
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for period_key, _ in collect_period
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}
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# 以最早的时间戳为起始时间获取记录
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# Assuming LLMUsage.timestamp is a DateTimeField
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query_start_time = collect_period[-1][1]
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for record in LLMUsage.select().where(LLMUsage.timestamp >= query_start_time):
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record_timestamp = record.timestamp # This is already a datetime object
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for idx, (_, period_start) in enumerate(collect_period):
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if record_timestamp >= period_start:
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for period_key, _ in collect_period[idx:]:
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stats[period_key][TOTAL_REQ_CNT] += 1
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request_type = record.request_type or "unknown"
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user_id = record.user_id or "unknown" # user_id is TextField, already string
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model_name = record.model_name or "unknown"
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stats[period_key][REQ_CNT_BY_TYPE][request_type] += 1
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stats[period_key][REQ_CNT_BY_USER][user_id] += 1
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stats[period_key][REQ_CNT_BY_MODEL][model_name] += 1
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prompt_tokens = record.prompt_tokens or 0
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completion_tokens = record.completion_tokens or 0
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total_tokens = prompt_tokens + completion_tokens
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stats[period_key][IN_TOK_BY_TYPE][request_type] += prompt_tokens
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stats[period_key][IN_TOK_BY_USER][user_id] += prompt_tokens
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stats[period_key][IN_TOK_BY_MODEL][model_name] += prompt_tokens
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stats[period_key][OUT_TOK_BY_TYPE][request_type] += completion_tokens
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stats[period_key][OUT_TOK_BY_USER][user_id] += completion_tokens
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stats[period_key][OUT_TOK_BY_MODEL][model_name] += completion_tokens
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stats[period_key][TOTAL_TOK_BY_TYPE][request_type] += total_tokens
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stats[period_key][TOTAL_TOK_BY_USER][user_id] += total_tokens
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stats[period_key][TOTAL_TOK_BY_MODEL][model_name] += total_tokens
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cost = record.cost or 0.0
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stats[period_key][TOTAL_COST] += cost
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stats[period_key][COST_BY_TYPE][request_type] += cost
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stats[period_key][COST_BY_USER][user_id] += cost
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stats[period_key][COST_BY_MODEL][model_name] += cost
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break
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return stats
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@staticmethod
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def _collect_online_time_for_period(collect_period: List[Tuple[str, datetime]], now: datetime) -> Dict[str, Any]:
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"""
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收集指定时间段的在线时间统计数据
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:param collect_period: 统计时间段
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"""
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if not collect_period:
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return {}
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collect_period.sort(key=lambda x: x[1], reverse=True)
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stats = {
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period_key: {
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ONLINE_TIME: 0.0,
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}
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for period_key, _ in collect_period
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}
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query_start_time = collect_period[-1][1]
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# Assuming OnlineTime.end_timestamp is a DateTimeField
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for record in OnlineTime.select().where(OnlineTime.end_timestamp >= query_start_time):
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# record.end_timestamp and record.start_timestamp are datetime objects
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record_end_timestamp = record.end_timestamp
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record_start_timestamp = record.start_timestamp
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for idx, (_, period_boundary_start) in enumerate(collect_period):
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if record_end_timestamp >= period_boundary_start:
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# Calculate effective end time for this record in relation to 'now'
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effective_end_time = min(record_end_timestamp, now)
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for period_key, current_period_start_time in collect_period[idx:]:
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# Determine the portion of the record that falls within this specific statistical period
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overlap_start = max(record_start_timestamp, current_period_start_time)
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overlap_end = effective_end_time # Already capped by 'now' and record's own end
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if overlap_end > overlap_start:
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stats[period_key][ONLINE_TIME] += (overlap_end - overlap_start).total_seconds()
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break
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return stats
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def _collect_message_count_for_period(self, collect_period: List[Tuple[str, datetime]]) -> Dict[str, Any]:
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"""
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收集指定时间段的消息统计数据
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:param collect_period: 统计时间段
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"""
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if not collect_period:
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return {}
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collect_period.sort(key=lambda x: x[1], reverse=True)
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stats = {
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period_key: {
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TOTAL_MSG_CNT: 0,
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MSG_CNT_BY_CHAT: defaultdict(int),
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}
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for period_key, _ in collect_period
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}
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query_start_timestamp = collect_period[-1][1].timestamp() # Messages.time is a DoubleField (timestamp)
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for message in Messages.select().where(Messages.time >= query_start_timestamp):
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message_time_ts = message.time # This is a float timestamp
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chat_id = None
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chat_name = None
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# Logic based on Peewee model structure, aiming to replicate original intent
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if message.chat_info_group_id:
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chat_id = f"g{message.chat_info_group_id}"
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chat_name = message.chat_info_group_name or f"群{message.chat_info_group_id}"
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elif message.user_id: # Fallback to sender's info for chat_id if not a group_info based chat
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# This uses the message SENDER's ID as per original logic's fallback
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chat_id = f"u{message.user_id}" # SENDER's user_id
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chat_name = message.user_nickname # SENDER's nickname
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else:
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# If neither group_id nor sender_id is available for chat identification
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logger.warning(
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f"Message (PK: {message.id if hasattr(message, 'id') else 'N/A'}) lacks group_id and user_id for chat stats."
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)
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continue
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if not chat_id: # Should not happen if above logic is correct
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continue
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# Update name_mapping
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if chat_id in self.name_mapping:
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if chat_name != self.name_mapping[chat_id][0] and message_time_ts > self.name_mapping[chat_id][1]:
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self.name_mapping[chat_id] = (chat_name, message_time_ts)
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else:
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self.name_mapping[chat_id] = (chat_name, message_time_ts)
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for idx, (_, period_start_dt) in enumerate(collect_period):
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if message_time_ts >= period_start_dt.timestamp():
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for period_key, _ in collect_period[idx:]:
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stats[period_key][TOTAL_MSG_CNT] += 1
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stats[period_key][MSG_CNT_BY_CHAT][chat_id] += 1
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break
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return stats
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def _collect_all_statistics(self, now: datetime) -> Dict[str, Dict[str, Any]]:
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"""
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收集各时间段的统计数据
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:param now: 基准当前时间
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"""
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last_all_time_stat = None
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if "last_full_statistics" in local_storage:
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# 如果存在上次完整统计数据，则使用该数据进行增量统计
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last_stat = local_storage["last_full_statistics"] # 上次完整统计数据
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self.name_mapping = last_stat["name_mapping"] # 上次完整统计数据的名称映射
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last_all_time_stat = last_stat["stat_data"] # 上次完整统计的统计数据
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last_stat_timestamp = datetime.fromtimestamp(last_stat["timestamp"]) # 上次完整统计数据的时间戳
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self.stat_period = [item for item in self.stat_period if item[0] != "all_time"] # 删除"所有时间"的统计时段
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self.stat_period.append(("all_time", now - last_stat_timestamp, "自部署以来的"))
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stat_start_timestamp = [(period[0], now - period[1]) for period in self.stat_period]
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stat = {item[0]: {} for item in self.stat_period}
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model_req_stat = self._collect_model_request_for_period(stat_start_timestamp)
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online_time_stat = self._collect_online_time_for_period(stat_start_timestamp, now)
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message_count_stat = self._collect_message_count_for_period(stat_start_timestamp)
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# 统计数据合并
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# 合并三类统计数据
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for period_key, _ in stat_start_timestamp:
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stat[period_key].update(model_req_stat[period_key])
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stat[period_key].update(online_time_stat[period_key])
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stat[period_key].update(message_count_stat[period_key])
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if last_all_time_stat:
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# 若存在上次完整统计数据，则将其与当前统计数据合并
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for key, val in last_all_time_stat.items():
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if isinstance(val, dict):
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# 是字典类型，则进行合并
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for sub_key, sub_val in val.items():
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stat["all_time"][key][sub_key] += sub_val
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else:
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# 直接合并
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stat["all_time"][key] += val
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# 更新上次完整统计数据的时间戳
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local_storage["last_full_statistics"] = {
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"name_mapping": self.name_mapping,
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"stat_data": stat["all_time"],
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"timestamp": now.timestamp(),
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||
}
|
||
|
||
return stat
|
||
|
||
# -- 以下为统计数据格式化方法 --
|
||
|
||
@staticmethod
|
||
def _format_total_stat(stats: Dict[str, Any]) -> str:
|
||
"""
|
||
格式化总统计数据
|
||
"""
|
||
|
||
output = [
|
||
f"总在线时间: {_format_online_time(stats[ONLINE_TIME])}",
|
||
f"总消息数: {stats[TOTAL_MSG_CNT]}",
|
||
f"总请求数: {stats[TOTAL_REQ_CNT]}",
|
||
f"总花费: {stats[TOTAL_COST]:.4f}¥",
|
||
"",
|
||
]
|
||
|
||
return "\n".join(output)
|
||
|
||
@staticmethod
|
||
def _format_model_classified_stat(stats: Dict[str, Any]) -> str:
|
||
"""
|
||
格式化按模型分类的统计数据
|
||
"""
|
||
if stats[TOTAL_REQ_CNT] <= 0:
|
||
return ""
|
||
data_fmt = "{:<32} {:>10} {:>12} {:>12} {:>12} {:>9.4f}¥"
|
||
|
||
output = [
|
||
"按模型分类统计:",
|
||
" 模型名称 调用次数 输入Token 输出Token Token总量 累计花费",
|
||
]
|
||
for model_name, count in sorted(stats[REQ_CNT_BY_MODEL].items()):
|
||
name = f"{model_name[:29]}..." if len(model_name) > 32 else model_name
|
||
in_tokens = stats[IN_TOK_BY_MODEL][model_name]
|
||
out_tokens = stats[OUT_TOK_BY_MODEL][model_name]
|
||
tokens = stats[TOTAL_TOK_BY_MODEL][model_name]
|
||
cost = stats[COST_BY_MODEL][model_name]
|
||
output.append(data_fmt.format(name, count, in_tokens, out_tokens, tokens, cost))
|
||
|
||
output.append("")
|
||
return "\n".join(output)
|
||
|
||
def _format_chat_stat(self, stats: Dict[str, Any]) -> str:
|
||
"""
|
||
格式化聊天统计数据
|
||
"""
|
||
if stats[TOTAL_MSG_CNT] <= 0:
|
||
return ""
|
||
output = ["聊天消息统计:", " 联系人/群组名称 消息数量"]
|
||
output.extend(
|
||
f"{self.name_mapping[chat_id][0][:32]:<32} {count:>10}"
|
||
for chat_id, count in sorted(stats[MSG_CNT_BY_CHAT].items())
|
||
)
|
||
output.append("")
|
||
return "\n".join(output)
|
||
|
||
def _generate_html_report(self, stat: dict[str, Any], now: datetime):
|
||
"""
|
||
生成HTML格式的统计报告
|
||
:param stat: 统计数据
|
||
:param now: 基准当前时间
|
||
:return: HTML格式的统计报告
|
||
"""
|
||
|
||
tab_list = [
|
||
f'<button class="tab-link" onclick="showTab(event, \'{period[0]}\')">{period[2]}</button>'
|
||
for period in self.stat_period
|
||
]
|
||
|
||
def _format_stat_data(stat_data: dict[str, Any], div_id: str, start_time: datetime) -> str:
|
||
"""
|
||
格式化一个时间段的统计数据到html div块
|
||
:param stat_data: 统计数据
|
||
:param div_id: div的ID
|
||
:param start_time: 统计时间段开始时间
|
||
"""
|
||
# format总在线时间
|
||
|
||
# 按模型分类统计
|
||
model_rows = "\n".join(
|
||
[
|
||
f"<tr>"
|
||
f"<td>{model_name}</td>"
|
||
f"<td>{count}</td>"
|
||
f"<td>{stat_data[IN_TOK_BY_MODEL][model_name]}</td>"
|
||
f"<td>{stat_data[OUT_TOK_BY_MODEL][model_name]}</td>"
|
||
f"<td>{stat_data[TOTAL_TOK_BY_MODEL][model_name]}</td>"
|
||
f"<td>{stat_data[COST_BY_MODEL][model_name]:.4f} ¥</td>"
|
||
f"</tr>"
|
||
for model_name, count in sorted(stat_data[REQ_CNT_BY_MODEL].items())
|
||
]
|
||
)
|
||
# 按请求类型分类统计
|
||
type_rows = "\n".join(
|
||
[
|
||
f"<tr>"
|
||
f"<td>{req_type}</td>"
|
||
f"<td>{count}</td>"
|
||
f"<td>{stat_data[IN_TOK_BY_TYPE][req_type]}</td>"
|
||
f"<td>{stat_data[OUT_TOK_BY_TYPE][req_type]}</td>"
|
||
f"<td>{stat_data[TOTAL_TOK_BY_TYPE][req_type]}</td>"
|
||
f"<td>{stat_data[COST_BY_TYPE][req_type]:.4f} ¥</td>"
|
||
f"</tr>"
|
||
for req_type, count in sorted(stat_data[REQ_CNT_BY_TYPE].items())
|
||
]
|
||
)
|
||
# 按用户分类统计
|
||
user_rows = "\n".join(
|
||
[
|
||
f"<tr>"
|
||
f"<td>{user_id}</td>"
|
||
f"<td>{count}</td>"
|
||
f"<td>{stat_data[IN_TOK_BY_USER][user_id]}</td>"
|
||
f"<td>{stat_data[OUT_TOK_BY_USER][user_id]}</td>"
|
||
f"<td>{stat_data[TOTAL_TOK_BY_USER][user_id]}</td>"
|
||
f"<td>{stat_data[COST_BY_USER][user_id]:.4f} ¥</td>"
|
||
f"</tr>"
|
||
for user_id, count in sorted(stat_data[REQ_CNT_BY_USER].items())
|
||
]
|
||
)
|
||
# 聊天消息统计
|
||
chat_rows = "\n".join(
|
||
[
|
||
f"<tr><td>{self.name_mapping[chat_id][0]}</td><td>{count}</td></tr>"
|
||
for chat_id, count in sorted(stat_data[MSG_CNT_BY_CHAT].items())
|
||
]
|
||
)
|
||
# 生成HTML
|
||
return f"""
|
||
<div id=\"{div_id}\" class=\"tab-content\">
|
||
<p class=\"info-item\">
|
||
<strong>统计时段: </strong>
|
||
{start_time.strftime("%Y-%m-%d %H:%M:%S")} ~ {now.strftime("%Y-%m-%d %H:%M:%S")}
|
||
</p>
|
||
<p class=\"info-item\"><strong>总在线时间: </strong>{_format_online_time(stat_data[ONLINE_TIME])}</p>
|
||
<p class=\"info-item\"><strong>总消息数: </strong>{stat_data[TOTAL_MSG_CNT]}</p>
|
||
<p class=\"info-item\"><strong>总请求数: </strong>{stat_data[TOTAL_REQ_CNT]}</p>
|
||
<p class=\"info-item\"><strong>总花费: </strong>{stat_data[TOTAL_COST]:.4f} ¥</p>
|
||
|
||
<h2>按模型分类统计</h2>
|
||
<table>
|
||
<thead><tr><th>模型名称</th><th>调用次数</th><th>输入Token</th><th>输出Token</th><th>Token总量</th><th>累计花费</th></tr></thead>
|
||
<tbody>
|
||
{model_rows}
|
||
</tbody>
|
||
</table>
|
||
|
||
<h2>按请求类型分类统计</h2>
|
||
<table>
|
||
<thead>
|
||
<tr><th>请求类型</th><th>调用次数</th><th>输入Token</th><th>输出Token</th><th>Token总量</th><th>累计花费</th></tr>
|
||
</thead>
|
||
<tbody>
|
||
{type_rows}
|
||
</tbody>
|
||
</table>
|
||
|
||
<h2>按用户分类统计</h2>
|
||
<table>
|
||
<thead>
|
||
<tr><th>用户名称</th><th>调用次数</th><th>输入Token</th><th>输出Token</th><th>Token总量</th><th>累计花费</th></tr>
|
||
</thead>
|
||
<tbody>
|
||
{user_rows}
|
||
</tbody>
|
||
</table>
|
||
|
||
<h2>聊天消息统计</h2>
|
||
<table>
|
||
<thead>
|
||
<tr><th>联系人/群组名称</th><th>消息数量</th></tr>
|
||
</thead>
|
||
<tbody>
|
||
{chat_rows}
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
"""
|
||
|
||
tab_content_list = [
|
||
_format_stat_data(stat[period[0]], period[0], now - period[1])
|
||
for period in self.stat_period
|
||
if period[0] != "all_time"
|
||
]
|
||
|
||
tab_content_list.append(
|
||
_format_stat_data(stat["all_time"], "all_time", datetime.fromtimestamp(local_storage["deploy_time"]))
|
||
)
|
||
|
||
joined_tab_list = "\n".join(tab_list)
|
||
joined_tab_content = "\n".join(tab_content_list)
|
||
|
||
html_template = (
|
||
"""
|
||
<!DOCTYPE html>
|
||
<html lang="zh-CN">
|
||
<head>
|
||
<meta charset="UTF-8">
|
||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||
<title>MaiBot运行统计报告</title>
|
||
<style>
|
||
body {
|
||
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif;
|
||
margin: 0;
|
||
padding: 20px;
|
||
background-color: #f4f7f6;
|
||
color: #333;
|
||
line-height: 1.6;
|
||
}
|
||
.container {
|
||
max-width: 900px;
|
||
margin: 20px auto;
|
||
background-color: #fff;
|
||
padding: 25px;
|
||
border-radius: 8px;
|
||
box-shadow: 0 2px 10px rgba(0,0,0,0.1);
|
||
}
|
||
h1, h2 {
|
||
color: #2c3e50;
|
||
border-bottom: 2px solid #3498db;
|
||
padding-bottom: 10px;
|
||
margin-top: 0;
|
||
}
|
||
h1 {
|
||
text-align: center;
|
||
font-size: 2em;
|
||
}
|
||
h2 {
|
||
font-size: 1.5em;
|
||
margin-top: 30px;
|
||
}
|
||
p {
|
||
margin-bottom: 10px;
|
||
}
|
||
.info-item {
|
||
background-color: #ecf0f1;
|
||
padding: 8px 12px;
|
||
border-radius: 4px;
|
||
margin-bottom: 8px;
|
||
font-size: 0.95em;
|
||
}
|
||
.info-item strong {
|
||
color: #2980b9;
|
||
}
|
||
table {
|
||
width: 100%;
|
||
border-collapse: collapse;
|
||
margin-top: 15px;
|
||
font-size: 0.9em;
|
||
}
|
||
th, td {
|
||
border: 1px solid #ddd;
|
||
padding: 10px;
|
||
text-align: left;
|
||
}
|
||
th {
|
||
background-color: #3498db;
|
||
color: white;
|
||
font-weight: bold;
|
||
}
|
||
tr:nth-child(even) {
|
||
background-color: #f9f9f9;
|
||
}
|
||
.footer {
|
||
text-align: center;
|
||
margin-top: 30px;
|
||
font-size: 0.8em;
|
||
color: #7f8c8d;
|
||
}
|
||
.tabs {
|
||
overflow: hidden;
|
||
background: #ecf0f1;
|
||
display: flex;
|
||
}
|
||
.tabs button {
|
||
background: inherit; border: none; outline: none;
|
||
padding: 14px 16px; cursor: pointer;
|
||
transition: 0.3s; font-size: 16px;
|
||
}
|
||
.tabs button:hover {
|
||
background-color: #d4dbdc;
|
||
}
|
||
.tabs button.active {
|
||
background-color: #b3bbbd;
|
||
}
|
||
.tab-content {
|
||
display: none;
|
||
padding: 20px;
|
||
background-color: #fff;
|
||
border: 1px solid #ccc;
|
||
}
|
||
.tab-content.active {
|
||
display: block;
|
||
}
|
||
</style>
|
||
</head>
|
||
<body>
|
||
"""
|
||
+ f"""
|
||
<div class="container">
|
||
<h1>MaiBot运行统计报告</h1>
|
||
<p class="info-item"><strong>统计截止时间:</strong> {now.strftime("%Y-%m-%d %H:%M:%S")}</p>
|
||
|
||
<div class="tabs">
|
||
{joined_tab_list}
|
||
</div>
|
||
|
||
{joined_tab_content}
|
||
</div>
|
||
"""
|
||
+ """
|
||
<script>
|
||
let i, tab_content, tab_links;
|
||
tab_content = document.getElementsByClassName("tab-content");
|
||
tab_links = document.getElementsByClassName("tab-link");
|
||
|
||
tab_content[0].classList.add("active");
|
||
tab_links[0].classList.add("active");
|
||
|
||
function showTab(evt, tabName) {{
|
||
for (i = 0; i < tab_content.length; i++) tab_content[i].classList.remove("active");
|
||
for (i = 0; i < tab_links.length; i++) tab_links[i].classList.remove("active");
|
||
document.getElementById(tabName).classList.add("active");
|
||
evt.currentTarget.classList.add("active");
|
||
}}
|
||
</script>
|
||
</body>
|
||
</html>
|
||
"""
|
||
)
|
||
|
||
with open(self.record_file_path, "w", encoding="utf-8") as f:
|
||
f.write(html_template)
|