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Ruff fix

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This commit is contained in:
墨梓柒
2025-11-13 13:24:55 +08:00
parent e78a070fbd
commit 7839acd25d
52 changed files with 1322 additions and 1408 deletions
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137
src/chat/utils/statistic.py
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@@ -153,7 +153,7 @@ def _format_large_number(num: float | int, html: bool = False) -> str:
else:
number_part = f"{value:.1f}"
k_suffix = "K"
if html:
# HTML输出：K着色为主题色并加粗大写
return f"{number_part}<span style='color: #8b5cf6; font-weight: bold;'>K</span>"
@@ -502,9 +502,13 @@ class StatisticOutputTask(AsyncTask):
}
for period_key, _ in collect_period
}
# 获取bot的QQ账号
bot_qq_account = str(global_config.bot.qq_account) if hasattr(global_config, 'bot') and hasattr(global_config.bot, 'qq_account') else ""
bot_qq_account = (
str(global_config.bot.qq_account)
if hasattr(global_config, "bot") and hasattr(global_config.bot, "qq_account")
else ""
)
query_start_timestamp = collect_period[-1][1].timestamp() # Messages.time is a DoubleField (timestamp)
for message in Messages.select().where(Messages.time >= query_start_timestamp): # type: ignore
@@ -547,7 +551,7 @@ class StatisticOutputTask(AsyncTask):
is_bot_reply = False
if bot_qq_account and message.user_id == bot_qq_account:
is_bot_reply = True
for idx, (_, period_start_dt) in enumerate(collect_period):
if message_time_ts >= period_start_dt.timestamp():
for period_key, _ in collect_period[idx:]:
@@ -588,7 +592,9 @@ class StatisticOutputTask(AsyncTask):
continue
last_all_time_stat = last_stat["stat_data"] # 上次完整统计的统计数据
last_stat_timestamp = datetime.fromtimestamp(last_stat["timestamp"]) # 上次完整统计数据的时间戳
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
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
self.stat_period.append(("all_time", now - last_stat_timestamp, "自部署以来的"))
except Exception as e:
logger.warning(f"加载上次完整统计数据失败，进行全量统计，错误信息：{e}")
@@ -640,12 +646,12 @@ class StatisticOutputTask(AsyncTask):
# 更新上次完整统计数据的时间戳
# 将所有defaultdict转换为普通dict以避免类型冲突
clean_stat_data = self._convert_defaultdict_to_dict(stat["all_time"])
# 将 name_mapping 中的元组转换为列表，因为JSON不支持元组
json_safe_name_mapping = {}
for chat_id, (chat_name, timestamp) in self.name_mapping.items():
json_safe_name_mapping[chat_id] = [chat_name, timestamp]
local_storage["last_full_statistics"] = {
"name_mapping": json_safe_name_mapping,
"stat_data": clean_stat_data,
@@ -682,24 +688,28 @@ class StatisticOutputTask(AsyncTask):
"""
# 计算总token数（从所有模型的token数中累加）
total_tokens = sum(stats[TOTAL_TOK_BY_MODEL].values()) if stats[TOTAL_TOK_BY_MODEL] else 0
# 计算花费/消息数量指标（每100条）
cost_per_100_messages = (stats[TOTAL_COST] / stats[TOTAL_MSG_CNT] * 100) if stats[TOTAL_MSG_CNT] > 0 else 0.0
# 计算花费/时间指标（花费/小时）
online_hours = stats[ONLINE_TIME] / 3600.0 if stats[ONLINE_TIME] > 0 else 0.0
cost_per_hour = stats[TOTAL_COST] / online_hours if online_hours > 0 else 0.0
# 计算token/时间指标（token/小时）
tokens_per_hour = (total_tokens / online_hours) if online_hours > 0 else 0.0
# 计算花费/回复数量指标（每100条）
total_replies = stats.get(TOTAL_REPLY_CNT, 0)
cost_per_100_replies = (stats[TOTAL_COST] / total_replies * 100) if total_replies > 0 else 0.0
# 计算花费/消息数量（排除自己回复）指标（每100条）
total_messages_excluding_replies = stats[TOTAL_MSG_CNT] - total_replies
cost_per_100_messages_excluding_replies = (stats[TOTAL_COST] / total_messages_excluding_replies * 100) if total_messages_excluding_replies > 0 else 0.0
cost_per_100_messages_excluding_replies = (
(stats[TOTAL_COST] / total_messages_excluding_replies * 100)
if total_messages_excluding_replies > 0
else 0.0
)
output = [
f"总在线时间: {_format_online_time(stats[ONLINE_TIME])}",
@@ -709,7 +719,9 @@ class StatisticOutputTask(AsyncTask):
f"总Token数: {_format_large_number(total_tokens)}",
f"总花费: {stats[TOTAL_COST]:.2f}¥",
f"花费/消息数量: {cost_per_100_messages:.4f}¥/100条" if stats[TOTAL_MSG_CNT] > 0 else "花费/消息数量: N/A",
f"花费/接受消息数量: {cost_per_100_messages_excluding_replies:.4f}¥/100条" if total_messages_excluding_replies > 0 else "花费/消息数量(排除回复): N/A",
f"花费/接受消息数量: {cost_per_100_messages_excluding_replies:.4f}¥/100条"
if total_messages_excluding_replies > 0
else "花费/消息数量(排除回复): N/A",
f"花费/回复消息数量: {cost_per_100_replies:.4f}¥/100条" if total_replies > 0 else "花费/回复数量: N/A",
f"花费/时间: {cost_per_hour:.2f}¥/小时" if online_hours > 0 else "花费/时间: N/A",
f"Token/时间: {_format_large_number(tokens_per_hour)}/小时" if online_hours > 0 else "Token/时间: N/A",
@@ -745,7 +757,16 @@ class StatisticOutputTask(AsyncTask):
formatted_out_tokens = _format_large_number(out_tokens)
formatted_tokens = _format_large_number(tokens)
output.append(
data_fmt.format(name, formatted_count, formatted_in_tokens, formatted_out_tokens, formatted_tokens, cost, avg_time_cost, std_time_cost)
data_fmt.format(
name,
formatted_count,
formatted_in_tokens,
formatted_out_tokens,
formatted_tokens,
cost,
avg_time_cost,
std_time_cost,
)
)
output.append("")
@@ -891,8 +912,12 @@ class StatisticOutputTask(AsyncTask):
except (IndexError, TypeError) as e:
logger.warning(f"生成HTML聊天统计时发生错误，chat_id: {chat_id}, 错误: {e}")
chat_rows.append(f"<tr><td>未知聊天</td><td>{_format_large_number(count, html=True)}</td></tr>")
chat_rows_html = "\n".join(chat_rows) if chat_rows else "<tr><td colspan='2' style='text-align: center; color: #999;'>暂无数据</td></tr>"
chat_rows_html = (
"\n".join(chat_rows)
if chat_rows
else "<tr><td colspan='2' style='text-align: center; color: #999;'>暂无数据</td></tr>"
)
# 生成HTML
return f"""
<div id=\"{div_id}\" class=\"tab-content\">
@@ -1197,7 +1222,7 @@ class StatisticOutputTask(AsyncTask):
# 添加图表内容
chart_data = self._generate_chart_data(stat)
tab_content_list.append(self._generate_chart_tab(chart_data))
# 添加指标趋势图表
metrics_data = self._generate_metrics_data(now)
tab_content_list.append(self._generate_metrics_tab(metrics_data))
@@ -1772,121 +1797,125 @@ class StatisticOutputTask(AsyncTask):
def _generate_metrics_data(self, now: datetime) -> dict:
"""生成指标趋势数据"""
metrics_data = {}
# 24小时尺度：1小时为单位
metrics_data["24h"] = self._collect_metrics_interval_data(now, hours=24, interval_hours=1)
# 7天尺度：1天为单位
metrics_data["7d"] = self._collect_metrics_interval_data(now, hours=24*7, interval_hours=24)
metrics_data["7d"] = self._collect_metrics_interval_data(now, hours=24 * 7, interval_hours=24)
# 30天尺度：1天为单位
metrics_data["30d"] = self._collect_metrics_interval_data(now, hours=24*30, interval_hours=24)
metrics_data["30d"] = self._collect_metrics_interval_data(now, hours=24 * 30, interval_hours=24)
return metrics_data
def _collect_metrics_interval_data(self, now: datetime, hours: int, interval_hours: int) -> dict:
"""收集指定时间范围内每个间隔的指标数据"""
start_time = now - timedelta(hours=hours)
time_points = []
current_time = start_time
# 生成时间点
while current_time <= now:
time_points.append(current_time)
current_time += timedelta(hours=interval_hours)
# 初始化数据结构
cost_per_100_messages = [0.0] * len(time_points) # 花费/消息数量（每100条）
cost_per_hour = [0.0] * len(time_points) # 花费/时间（每小时）
tokens_per_hour = [0.0] * len(time_points) # Token/时间（每小时）
cost_per_100_replies = [0.0] * len(time_points) # 花费/回复数量（每100条）
# 每个时间点的累计数据
total_costs = [0.0] * len(time_points)
total_tokens = [0] * len(time_points)
total_messages = [0] * len(time_points)
total_replies = [0] * len(time_points)
total_online_hours = [0.0] * len(time_points)
# 获取bot的QQ账号
bot_qq_account = str(global_config.bot.qq_account) if hasattr(global_config, 'bot') and hasattr(global_config.bot, 'qq_account') else ""
bot_qq_account = (
str(global_config.bot.qq_account)
if hasattr(global_config, "bot") and hasattr(global_config.bot, "qq_account")
else ""
)
interval_seconds = interval_hours * 3600
# 查询LLM使用记录
query_start_time = start_time
for record in LLMUsage.select().where(LLMUsage.timestamp >= query_start_time): # type: ignore
record_time = record.timestamp
# 找到对应的时间间隔索引
time_diff = (record_time - start_time).total_seconds()
interval_index = int(time_diff // interval_seconds)
if 0 <= interval_index < len(time_points):
cost = record.cost or 0.0
prompt_tokens = record.prompt_tokens or 0
completion_tokens = record.completion_tokens or 0
total_token = prompt_tokens + completion_tokens
total_costs[interval_index] += cost
total_tokens[interval_index] += total_token
# 查询消息记录
query_start_timestamp = start_time.timestamp()
for message in Messages.select().where(Messages.time >= query_start_timestamp): # type: ignore
message_time_ts = message.time
time_diff = message_time_ts - query_start_timestamp
interval_index = int(time_diff // interval_seconds)
if 0 <= interval_index < len(time_points):
total_messages[interval_index] += 1
# 检查是否是bot发送的消息（回复）
if bot_qq_account and message.user_id == bot_qq_account:
total_replies[interval_index] += 1
# 查询在线时间记录
for record in OnlineTime.select().where(OnlineTime.end_timestamp >= start_time): # type: ignore
record_start = record.start_timestamp
record_end = record.end_timestamp
# 找到记录覆盖的所有时间间隔
for idx, time_point in enumerate(time_points):
interval_start = time_point
interval_end = time_point + timedelta(hours=interval_hours)
# 计算重叠部分
overlap_start = max(record_start, interval_start)
overlap_end = min(record_end, interval_end)
if overlap_end > overlap_start:
overlap_hours = (overlap_end - overlap_start).total_seconds() / 3600.0
total_online_hours[idx] += overlap_hours
# 计算指标
for idx in range(len(time_points)):
# 花费/消息数量（每100条）
if total_messages[idx] > 0:
cost_per_100_messages[idx] = (total_costs[idx] / total_messages[idx] * 100)
cost_per_100_messages[idx] = total_costs[idx] / total_messages[idx] * 100
# 花费/时间（每小时）
if total_online_hours[idx] > 0:
cost_per_hour[idx] = (total_costs[idx] / total_online_hours[idx])
cost_per_hour[idx] = total_costs[idx] / total_online_hours[idx]
# Token/时间（每小时）
if total_online_hours[idx] > 0:
tokens_per_hour[idx] = (total_tokens[idx] / total_online_hours[idx])
tokens_per_hour[idx] = total_tokens[idx] / total_online_hours[idx]
# 花费/回复数量（每100条）
if total_replies[idx] > 0:
cost_per_100_replies[idx] = (total_costs[idx] / total_replies[idx] * 100)
cost_per_100_replies[idx] = total_costs[idx] / total_replies[idx] * 100
# 生成时间标签
if interval_hours == 1:
time_labels = [t.strftime("%H:%M") for t in time_points]
else:
time_labels = [t.strftime("%m-%d") for t in time_points]
return {
"time_labels": time_labels,
"cost_per_100_messages": cost_per_100_messages,
@@ -1894,7 +1923,7 @@ class StatisticOutputTask(AsyncTask):
"tokens_per_hour": tokens_per_hour,
"cost_per_100_replies": cost_per_100_replies,
}
def _generate_metrics_tab(self, metrics_data: dict) -> str:
"""生成指标趋势图表选项卡HTML内容"""
colors = {
@@ -1903,7 +1932,7 @@ class StatisticOutputTask(AsyncTask):
"tokens_per_hour": "#c7bbff",
"cost_per_100_replies": "#d9ceff",
}
return f"""
<div id="metrics" class="tab-content">
<h2>指标趋势图表</h2>