diff --git a/src/plugins/chat/utils.py b/src/plugins/chat/utils.py index 5b279f16..148a2f69 100644 --- a/src/plugins/chat/utils.py +++ b/src/plugins/chat/utils.py @@ -251,7 +251,7 @@ def split_into_sentences_w_remove_punctuation(text: str) -> list[str]: if len_text < 12: split_strength = 0.2 elif len_text < 32: - split_strength = 0.6 + split_strength = 0.5 else: split_strength = 0.7 # 合并概率与分割强度相反 @@ -370,7 +370,7 @@ def process_llm_response(text: str) -> list[str]: else: sentences.append(sentence) - if len(sentences) > max_sentence_num: + if len(sentences) > (max_sentence_num * 2): logger.warning(f"分割后消息数量过多 ({len(sentences)} 条)，返回默认回复") return [f"{global_config.BOT_NICKNAME}不知道哦"] @@ -417,8 +417,8 @@ def calculate_typing_time( if chinese_chars == 1 and len(input_string.strip()) == 1: return chinese_time * 3 + 0.3 # 加上回车时间 + total_time = 0 # 正常计算所有字符的输入时间 - total_time = 0.0 for char in input_string: if "\u4e00" <= char <= "\u9fff": # 判断是否为中文字符 total_time += chinese_time diff --git a/src/plugins/utils/statistic.py b/src/plugins/utils/statistic.py index 4c11ba3d..f3d40f7e 100644 --- a/src/plugins/utils/statistic.py +++ b/src/plugins/utils/statistic.py @@ -102,7 +102,7 @@ def _format_online_time(online_seconds: int) -> str: :param online_seconds: 在线时间（秒） :return: 格式化后的在线时间字符串 """ - total_oneline_time = timedelta(seconds=online_seconds) + total_oneline_time = timedelta(seconds=int(online_seconds)) #确保是整数 days = total_oneline_time.days hours = total_oneline_time.seconds // 3600 @@ -110,7 +110,7 @@ def _format_online_time(online_seconds: int) -> str: seconds = total_oneline_time.seconds % 60 if days > 0: # 如果在线时间超过1天，则格式化为“X天X小时X分钟” - total_oneline_time_str = f"{total_oneline_time.days}天{hours}小时{minutes}分钟{seconds}秒" + total_oneline_time_str = f"{days}天{hours}小时{minutes}分钟{seconds}秒" elif hours > 0: # 如果在线时间超过1小时，则格式化为“X小时X分钟X秒” total_oneline_time_str = f"{hours}小时{minutes}分钟{seconds}秒" @@ -141,17 +141,17 @@ class StatisticOutputTask(AsyncTask): 记录文件路径 """ - now = datetime.now() + now_init = datetime.now() # Renamed to avoid conflict with 'now' in methods if "deploy_time" in local_storage: # 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 - deploy_time = datetime.fromtimestamp(local_storage["deploy_time"]) + deploy_time_init = datetime.fromtimestamp(local_storage["deploy_time"]) else: # 否则，使用最大时间范围，并记录部署时间为当前时间 - deploy_time = datetime(2000, 1, 1) - local_storage["deploy_time"] = now.timestamp() + deploy_time_init = datetime(2000, 1, 1) + local_storage["deploy_time"] = now_init.timestamp() self.stat_period: List[Tuple[str, timedelta, str]] = [ - ("all_time", now - deploy_time, "自部署以来"), # 必须保留“all_time” + ("all_time", now_init - deploy_time_init, "自部署以来"), # 必须保留“all_time” ("last_7_days", timedelta(days=7), "最近7天"), ("last_24_hours", timedelta(days=1), "最近24小时"), ("last_hour", timedelta(hours=1), "最近1小时"), @@ -167,16 +167,17 @@ class StatisticOutputTask(AsyncTask): :param now: 基准当前时间 """ # 输出最近一小时的统计数据 + last_hour_stats = stats.get("last_hour", {}) # Ensure 'last_hour' key exists output = [ self.SEP_LINE, 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})", self.SEP_LINE, - self._format_total_stat(stats["last_hour"]), + self._format_total_stat(last_hour_stats), "", - self._format_model_classified_stat(stats["last_hour"]), + self._format_model_classified_stat(last_hour_stats), "", - self._format_chat_stat(stats["last_hour"]), + self._format_chat_stat(last_hour_stats), self.SEP_LINE, "", ] @@ -190,7 +191,10 @@ class StatisticOutputTask(AsyncTask): stats = self._collect_all_statistics(now) # 输出统计数据到控制台 - self._statistic_console_output(stats, now) + if "last_hour" in stats: # Check if stats for last_hour were successfully collected + self._statistic_console_output(stats, now) + else: + logger.warning("无法输出最近一小时统计数据到控制台，因为数据缺失。") # 输出统计数据到html文件 self._generate_html_report(stats, now) except Exception as e: @@ -203,84 +207,85 @@ class StatisticOutputTask(AsyncTask): """ 收集指定时间段的LLM请求统计数据 - :param collect_period: 统计时间段 + :param collect_period: 统计时间段 [(period_key, start_datetime), ...] """ - if len(collect_period) <= 0: + if not collect_period: return {} - else: - # 排序-按照时间段开始时间降序排列（最晚的时间段在前） - collect_period.sort(key=lambda x: x[1], reverse=True) + + collect_period.sort(key=lambda x: x[1], reverse=True) stats = { period_key: { - # 总LLM请求数 TOTAL_REQ_CNT: 0, - # 请求次数统计 REQ_CNT_BY_TYPE: defaultdict(int), REQ_CNT_BY_USER: defaultdict(int), REQ_CNT_BY_MODEL: defaultdict(int), - # 输入Token数 IN_TOK_BY_TYPE: defaultdict(int), IN_TOK_BY_USER: defaultdict(int), IN_TOK_BY_MODEL: defaultdict(int), - # 输出Token数 OUT_TOK_BY_TYPE: defaultdict(int), OUT_TOK_BY_USER: defaultdict(int), OUT_TOK_BY_MODEL: defaultdict(int), - # 总Token数 TOTAL_TOK_BY_TYPE: defaultdict(int), TOTAL_TOK_BY_USER: defaultdict(int), TOTAL_TOK_BY_MODEL: defaultdict(int), - # 总开销 TOTAL_COST: 0.0, - # 请求开销统计 COST_BY_TYPE: defaultdict(float), COST_BY_USER: defaultdict(float), COST_BY_MODEL: defaultdict(float), } for period_key, _ in collect_period } + + # Determine the overall earliest start time for the database query + # This assumes collect_period is not empty, which is checked at the beginning. + overall_earliest_start_time = min(p[1] for p in collect_period) - # 以最早的时间戳为起始时间获取记录 - for record in db.llm_usage.find({"timestamp": {"$gte": collect_period[-1][1]}}): + for record in db.llm_usage.find({"timestamp": {"$gte": overall_earliest_start_time}}): record_timestamp = record.get("timestamp") - for idx, (_, period_start) in enumerate(collect_period): - if record_timestamp >= period_start: - # 如果记录时间在当前时间段内，则它一定在更早的时间段内 - # 因此，我们可以直接跳过更早的时间段的判断，直接更新当前以及更早时间段的统计数据 - for period_key, _ in collect_period[idx:]: - stats[period_key][TOTAL_REQ_CNT] += 1 + if not isinstance(record_timestamp, datetime): # Ensure timestamp is a datetime object + try: # Attempt conversion if it's a number (e.g. Unix timestamp) + record_timestamp = datetime.fromtimestamp(float(record_timestamp)) + except (ValueError, TypeError): + logger.warning(f"Skipping LLM usage record with invalid timestamp: {record.get('_id')}") + continue - request_type = record.get("request_type", "unknown") # 请求类型 - user_id = str(record.get("user_id", "unknown")) # 用户ID - model_name = record.get("model_name", "unknown") # 模型名称 - stats[period_key][REQ_CNT_BY_TYPE][request_type] += 1 - stats[period_key][REQ_CNT_BY_USER][user_id] += 1 - stats[period_key][REQ_CNT_BY_MODEL][model_name] += 1 + for idx, (current_period_key, period_start_time) in enumerate(collect_period): + if record_timestamp >= period_start_time: + for period_key_to_update, _ in collect_period[idx:]: + stats[period_key_to_update][TOTAL_REQ_CNT] += 1 - prompt_tokens = record.get("prompt_tokens", 0) # 输入Token数 - completion_tokens = record.get("completion_tokens", 0) # 输出Token数 - total_tokens = prompt_tokens + completion_tokens # Token总数 = 输入Token数 + 输出Token数 + request_type = record.get("request_type", "unknown") + user_id = str(record.get("user_id", "unknown")) + model_name = record.get("model_name", "unknown") - stats[period_key][IN_TOK_BY_TYPE][request_type] += prompt_tokens - stats[period_key][IN_TOK_BY_USER][user_id] += prompt_tokens - stats[period_key][IN_TOK_BY_MODEL][model_name] += prompt_tokens + stats[period_key_to_update][REQ_CNT_BY_TYPE][request_type] += 1 + stats[period_key_to_update][REQ_CNT_BY_USER][user_id] += 1 + stats[period_key_to_update][REQ_CNT_BY_MODEL][model_name] += 1 - stats[period_key][OUT_TOK_BY_TYPE][request_type] += completion_tokens - stats[period_key][OUT_TOK_BY_USER][user_id] += completion_tokens - stats[period_key][OUT_TOK_BY_MODEL][model_name] += completion_tokens + prompt_tokens = record.get("prompt_tokens", 0) + completion_tokens = record.get("completion_tokens", 0) + total_tokens = prompt_tokens + completion_tokens - stats[period_key][TOTAL_TOK_BY_TYPE][request_type] += total_tokens - stats[period_key][TOTAL_TOK_BY_USER][user_id] += total_tokens - stats[period_key][TOTAL_TOK_BY_MODEL][model_name] += total_tokens + stats[period_key_to_update][IN_TOK_BY_TYPE][request_type] += prompt_tokens + stats[period_key_to_update][IN_TOK_BY_USER][user_id] += prompt_tokens + stats[period_key_to_update][IN_TOK_BY_MODEL][model_name] += prompt_tokens + + stats[period_key_to_update][OUT_TOK_BY_TYPE][request_type] += completion_tokens + stats[period_key_to_update][OUT_TOK_BY_USER][user_id] += completion_tokens + stats[period_key_to_update][OUT_TOK_BY_MODEL][model_name] += completion_tokens + + stats[period_key_to_update][TOTAL_TOK_BY_TYPE][request_type] += total_tokens + stats[period_key_to_update][TOTAL_TOK_BY_USER][user_id] += total_tokens + stats[period_key_to_update][TOTAL_TOK_BY_MODEL][model_name] += total_tokens cost = record.get("cost", 0.0) - stats[period_key][TOTAL_COST] += cost - stats[period_key][COST_BY_TYPE][request_type] += cost - stats[period_key][COST_BY_USER][user_id] += cost - stats[period_key][COST_BY_MODEL][model_name] += cost - break # 取消更早时间段的判断 + stats[period_key_to_update][TOTAL_COST] += cost + stats[period_key_to_update][COST_BY_TYPE][request_type] += cost + stats[period_key_to_update][COST_BY_USER][user_id] += cost + stats[period_key_to_update][COST_BY_MODEL][model_name] += cost + break return stats @@ -289,41 +294,43 @@ class StatisticOutputTask(AsyncTask): """ 收集指定时间段的在线时间统计数据 - :param collect_period: 统计时间段 + :param collect_period: 统计时间段 [(period_key, start_datetime), ...] + :param now: 当前时间，用于校准end_timestamp """ - if len(collect_period) <= 0: + if not collect_period: return {} - else: - # 排序-按照时间段开始时间降序排列（最晚的时间段在前） - collect_period.sort(key=lambda x: x[1], reverse=True) + + collect_period.sort(key=lambda x: x[1], reverse=True) stats = { period_key: { - # 在线时间统计 ONLINE_TIME: 0.0, } for period_key, _ in collect_period } + + overall_earliest_start_time = min(p[1] for p in collect_period) - # 统计在线时间 - for record in db.online_time.find({"end_timestamp": {"$gte": collect_period[-1][1]}}): - end_timestamp: datetime = record.get("end_timestamp") - for idx, (_, period_start) in enumerate(collect_period): - if end_timestamp >= period_start: - # 由于end_timestamp会超前标记时间，所以我们需要判断是否晚于当前时间，如果是，则使用当前时间作为结束时间 - if end_timestamp > now: - end_timestamp = now - # 如果记录时间在当前时间段内，则它一定在更早的时间段内 - # 因此，我们可以直接跳过更早的时间段的判断，直接更新当前以及更早时间段的统计数据 - for period_key, _period_start in collect_period[idx:]: - start_timestamp: datetime = record.get("start_timestamp") - if start_timestamp < _period_start: - # 如果开始时间在查询边界之前，则使用开始时间 - stats[period_key][ONLINE_TIME] += (end_timestamp - _period_start).total_seconds() - else: - # 否则，使用开始时间 - stats[period_key][ONLINE_TIME] += (end_timestamp - start_timestamp).total_seconds() - break # 取消更早时间段的判断 + for record in db.online_time.find({"end_timestamp": {"$gte": overall_earliest_start_time}}): + record_end_timestamp: datetime = record.get("end_timestamp") + record_start_timestamp: datetime = record.get("start_timestamp") + + if not isinstance(record_end_timestamp, datetime) or not isinstance(record_start_timestamp, datetime): + logger.warning(f"Skipping online_time record with invalid timestamps: {record.get('_id')}") + continue + + actual_end_timestamp = min(record_end_timestamp, now) + + for idx, (current_period_key, period_start_time) in enumerate(collect_period): + if record_start_timestamp < now and actual_end_timestamp > period_start_time: + overlap_start = max(record_start_timestamp, period_start_time) + overlap_end = min(actual_end_timestamp, now) + + if overlap_end > overlap_start: + duration_seconds = (overlap_end - overlap_start).total_seconds() + for period_key_to_update, _ in collect_period[idx:]: + stats[period_key_to_update][ONLINE_TIME] += duration_seconds + break return stats @@ -331,56 +338,69 @@ class StatisticOutputTask(AsyncTask): """ 收集指定时间段的消息统计数据 - :param collect_period: 统计时间段 + :param collect_period: 统计时间段 [(period_key, start_datetime), ...] """ - if len(collect_period) <= 0: + if not collect_period: return {} - else: - # 排序-按照时间段开始时间降序排列（最晚的时间段在前） - collect_period.sort(key=lambda x: x[1], reverse=True) + + collect_period.sort(key=lambda x: x[1], reverse=True) stats = { period_key: { - # 消息统计 TOTAL_MSG_CNT: 0, MSG_CNT_BY_CHAT: defaultdict(int), } for period_key, _ in collect_period } - # 统计消息量 - for message in db.messages.find({"time": {"$gte": collect_period[-1][1].timestamp()}}): - chat_info = message.get("chat_info", None) # 聊天信息 - user_info = message.get("user_info", None) # 用户信息（消息发送人） - message_time = message.get("time", 0) # 消息时间 + overall_earliest_start_timestamp_float = min(p[1].timestamp() for p in collect_period) + + for message in db.messages.find({"time": {"$gte": overall_earliest_start_timestamp_float}}): + chat_info = message.get("chat_info", {}) + user_info = message.get("user_info", {}) + message_time_ts = message.get("time") - group_info = chat_info.get("group_info") if chat_info else None # 尝试获取群聊信息 - if group_info is not None: - # 若有群聊信息 - chat_id = f"g{group_info.get('group_id')}" - chat_name = group_info.get("group_name", f"群{group_info.get('group_id')}") - elif user_info: - # 若没有群聊信息，则尝试获取用户信息 - chat_id = f"u{user_info['user_id']}" - chat_name = user_info["user_nickname"] + if message_time_ts is None: + logger.warning(f"Skipping message record with no timestamp: {message.get('_id')}") + continue + + try: + message_datetime = datetime.fromtimestamp(float(message_time_ts)) + except (ValueError, TypeError): + logger.warning(f"Skipping message record with invalid time format: {message.get('_id')}") + continue + + + group_info = chat_info.get("group_info") + chat_id = None + chat_name = None + + if group_info and group_info.get("group_id"): + gid = group_info.get('group_id') + chat_id = f"g{gid}" + chat_name = group_info.get("group_name", f"群聊 {gid}") + elif user_info and user_info.get("user_id"): + uid = user_info['user_id'] + chat_id = f"u{uid}" + chat_name = user_info.get("user_nickname", f"用户 {uid}") + + if not chat_id: + continue + + current_mapping = self.name_mapping.get(chat_id) + if current_mapping: + if chat_name != current_mapping[0] and message_time_ts > current_mapping[1]: + self.name_mapping[chat_id] = (chat_name, message_time_ts) else: - continue # 如果没有群组信息也没有用户信息，则跳过 + self.name_mapping[chat_id] = (chat_name, message_time_ts) - if chat_id in self.name_mapping: - if chat_name != self.name_mapping[chat_id][0] and message_time > self.name_mapping[chat_id][1]: - # 如果用户名称不同，且新消息时间晚于之前记录的时间，则更新用户名称 - self.name_mapping[chat_id] = (chat_name, message_time) - else: - self.name_mapping[chat_id] = (chat_name, message_time) - for idx, (_, period_start) in enumerate(collect_period): - if message_time >= period_start.timestamp(): - # 如果记录时间在当前时间段内，则它一定在更早的时间段内 - # 因此，我们可以直接跳过更早的时间段的判断，直接更新当前以及更早时间段的统计数据 - for period_key, _ in collect_period[idx:]: - stats[period_key][TOTAL_MSG_CNT] += 1 - stats[period_key][MSG_CNT_BY_CHAT][chat_id] += 1 - break + for idx, (current_period_key, period_start_time) in enumerate(collect_period): + if message_datetime >= period_start_time: + for period_key_to_update, _ in collect_period[idx:]: + stats[period_key_to_update][TOTAL_MSG_CNT] += 1 + stats[period_key_to_update][MSG_CNT_BY_CHAT][chat_id] += 1 + break return stats @@ -389,48 +409,61 @@ class StatisticOutputTask(AsyncTask): 收集各时间段的统计数据 :param now: 基准当前时间 """ + # Correctly determine deploy_time + if "deploy_time" in local_storage: + try: + deploy_time = datetime.fromtimestamp(local_storage["deploy_time"]) + except (TypeError, ValueError): + logger.error("Invalid deploy_time in local_storage. Resetting.") + deploy_time = datetime(2000, 1, 1) + local_storage["deploy_time"] = now.timestamp() + else: + deploy_time = datetime(2000, 1, 1) + local_storage["deploy_time"] = now.timestamp() - last_all_time_stat = None + # Rebuild stat_period based on the current 'now' and determined 'deploy_time' + current_stat_periods_config = [ + ("all_time", now - deploy_time if now > deploy_time else timedelta(seconds=0), "自部署以来"), + ("last_7_days", timedelta(days=7), "最近7天"), + ("last_24_hours", timedelta(days=1), "最近24小时"), + ("last_hour", timedelta(hours=1), "最近1小时"), + ] + self.stat_period = current_stat_periods_config # Update instance's stat_period if needed elsewhere - if "last_full_statistics_timestamp" in local_storage and "last_full_statistics" in local_storage: - # 若存有上次完整统计的时间戳，则使用该时间戳作为"所有时间"的起始时间，进行增量统计 - last_full_stat_ts: float = local_storage["last_full_statistics_timestamp"] - last_all_time_stat = local_storage["last_full_statistics"] - 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 - datetime.fromtimestamp(last_full_stat_ts), "自部署以来的")) + stat_start_timestamp_config = [] + for period_name, delta, _ in current_stat_periods_config: + start_dt = deploy_time if period_name == "all_time" else now - delta + stat_start_timestamp_config.append((period_name, start_dt)) - stat_start_timestamp = [(period[0], now - period[1]) for period in self.stat_period] + # 收集各类数据 + model_req_stat = self._collect_model_request_for_period(stat_start_timestamp_config) + online_time_stat = self._collect_online_time_for_period(stat_start_timestamp_config, now) + message_count_stat = self._collect_message_count_for_period(stat_start_timestamp_config) - stat = {item[0]: {} for item in self.stat_period} - - model_req_stat = self._collect_model_request_for_period(stat_start_timestamp) - online_time_stat = self._collect_online_time_for_period(stat_start_timestamp, now) - message_count_stat = self._collect_message_count_for_period(stat_start_timestamp) - - # 统计数据合并 - # 合并三类统计数据 - for period_key, _ in stat_start_timestamp: - stat[period_key].update(model_req_stat[period_key]) - stat[period_key].update(online_time_stat[period_key]) - stat[period_key].update(message_count_stat[period_key]) - - if last_all_time_stat: - # 若存在上次完整统计数据，则将其与当前统计数据合并 - for key, val in last_all_time_stat.items(): - if isinstance(val, dict): - # 是字典类型，则进行合并 - for sub_key, sub_val in val.items(): - stat["all_time"][key][sub_key] += sub_val - else: - # 直接合并 - stat["all_time"][key] += val - - # 更新上次完整统计数据的时间戳 - local_storage["last_full_statistics_timestamp"] = now.timestamp() - # 更新上次完整统计数据 - local_storage["last_full_statistics"] = stat["all_time"] - - return stat + final_stats = {} + for period_key, _ in stat_start_timestamp_config: + final_stats[period_key] = {} + final_stats[period_key].update(model_req_stat.get(period_key, {})) + final_stats[period_key].update(online_time_stat.get(period_key, {})) + final_stats[period_key].update(message_count_stat.get(period_key, {})) + + for stat_field_key in [ + TOTAL_REQ_CNT, REQ_CNT_BY_TYPE, REQ_CNT_BY_USER, REQ_CNT_BY_MODEL, + IN_TOK_BY_TYPE, IN_TOK_BY_USER, IN_TOK_BY_MODEL, + OUT_TOK_BY_TYPE, OUT_TOK_BY_USER, OUT_TOK_BY_MODEL, + TOTAL_TOK_BY_TYPE, TOTAL_TOK_BY_USER, TOTAL_TOK_BY_MODEL, + TOTAL_COST, COST_BY_TYPE, COST_BY_USER, COST_BY_MODEL, + ONLINE_TIME, TOTAL_MSG_CNT, MSG_CNT_BY_CHAT + ]: + if stat_field_key not in final_stats[period_key]: + # Initialize with appropriate default type if key is missing + if "BY_" in stat_field_key: # These are usually defaultdicts + final_stats[period_key][stat_field_key] = defaultdict(int if "CNT" in stat_field_key or "TOK" in stat_field_key else float) + elif "CNT" in stat_field_key or "TOK" in stat_field_key : + final_stats[period_key][stat_field_key] = 0 + elif "COST" in stat_field_key or ONLINE_TIME == stat_field_key: + final_stats[period_key][stat_field_key] = 0.0 + return final_stats # -- 以下为统计数据格式化方法 -- @@ -439,15 +472,13 @@ class StatisticOutputTask(AsyncTask): """ 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}¥", + f"总在线时间: {_format_online_time(stats.get(ONLINE_TIME, 0))}", + f"总消息数: {stats.get(TOTAL_MSG_CNT, 0)}", + f"总请求数: {stats.get(TOTAL_REQ_CNT, 0)}", + f"总花费: {stats.get(TOTAL_COST, 0.0):.4f}¥", "", ] - return "\n".join(output) @staticmethod @@ -455,19 +486,24 @@ class StatisticOutputTask(AsyncTask): """ 格式化按模型分类的统计数据 """ - if stats[TOTAL_REQ_CNT] > 0: + if stats.get(TOTAL_REQ_CNT, 0) > 0: 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()): + req_cnt_by_model = stats.get(REQ_CNT_BY_MODEL, {}) + in_tok_by_model = stats.get(IN_TOK_BY_MODEL, defaultdict(int)) + out_tok_by_model = stats.get(OUT_TOK_BY_MODEL, defaultdict(int)) + total_tok_by_model = stats.get(TOTAL_TOK_BY_MODEL, defaultdict(int)) + cost_by_model = stats.get(COST_BY_MODEL, defaultdict(float)) + + for model_name, count in sorted(req_cnt_by_model.items()): name = 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] + in_tokens = in_tok_by_model[model_name] + out_tokens = out_tok_by_model[model_name] + tokens = total_tok_by_model[model_name] + cost = cost_by_model[model_name] output.append(data_fmt.format(name, count, in_tokens, out_tokens, tokens, cost)) output.append("") @@ -479,148 +515,148 @@ class StatisticOutputTask(AsyncTask): """ 格式化聊天统计数据 """ - if stats[TOTAL_MSG_CNT] > 0: + if stats.get(TOTAL_MSG_CNT, 0) > 0: output = ["聊天消息统计:", " 联系人/群组名称 消息数量"] - for chat_id, count in sorted(stats[MSG_CNT_BY_CHAT].items()): - output.append(f"{self.name_mapping[chat_id][0][:32]:<32} {count:>10}") + msg_cnt_by_chat = stats.get(MSG_CNT_BY_CHAT, {}) + for chat_id, count in sorted(msg_cnt_by_chat.items()): + chat_name_display = self.name_mapping.get(chat_id, (f"未知 ({chat_id})", None))[0] + output.append(f"{chat_name_display[:32]:<32} {count:>10}") output.append("") return "\n".join(output) else: return "" - def _generate_html_report(self, stat: dict[str, Any], now: datetime): + def _generate_html_report(self, stat_collection: dict[str, Any], now: datetime): """ 生成HTML格式的统计报告 - :param stat: 统计数据 + :param stat_collection: 包含所有时间段统计数据的字典 {period_key: stats_dict} :param now: 基准当前时间 - :return: HTML格式的统计报告 """ + # Correctly get deploy_time_dt for display purposes + if "deploy_time" in local_storage: + try: + deploy_time_dt = datetime.fromtimestamp(local_storage["deploy_time"]) + except (TypeError, ValueError): + logger.error("Invalid deploy_time in local_storage for HTML report. Using default.") + deploy_time_dt = datetime(2000,1,1) # Fallback + else: + # This should ideally not happen if __init__ or _collect_all_statistics ran + logger.warning("deploy_time not found in local_storage for HTML report. Using default.") + deploy_time_dt = datetime(2000, 1, 1) # Fallback - tab_list = [ - f'' - for period in self.stat_period - ] + tab_list_html = [] + tab_content_html_list = [] - 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 + for period_key, period_delta, period_display_name in self.stat_period: # Use self.stat_period as defined by _collect_all_statistics + tab_list_html.append( + f'' + ) - # 生成HTML - return f""" -
+ current_period_stats = stat_collection.get(period_key, {}) + + if period_key == "all_time": + start_time_dt_for_period = deploy_time_dt + else: + # Ensure period_delta is a timedelta object + if isinstance(period_delta, timedelta): + start_time_dt_for_period = now - period_delta + else: # Fallback if period_delta is not as expected (e.g. from old self.stat_period) + logger.warning(f"period_delta for {period_key} is not a timedelta. Using 'now'. Type: {type(period_delta)}") + start_time_dt_for_period = now + + + html_content_for_tab = f""" +

统计时段: - {start_time.strftime("%Y-%m-%d %H:%M:%S")} ~ {now.strftime("%Y-%m-%d %H:%M:%S")} + {start_time_dt_for_period.strftime("%Y-%m-%d %H:%M:%S")} ~ {now.strftime("%Y-%m-%d %H:%M:%S")}

-

总在线时间: {_format_online_time(stat_data[ONLINE_TIME])}

-

总消息数: {stat_data[TOTAL_MSG_CNT]}

-

总请求数: {stat_data[TOTAL_REQ_CNT]}

-

总花费: {stat_data[TOTAL_COST]:.4f} ¥

- -

按模型分类统计

- - - - { - "\n".join( - [ +

总在线时间: {_format_online_time(current_period_stats.get(ONLINE_TIME, 0))}

+

总消息数: {current_period_stats.get(TOTAL_MSG_CNT, 0)}

+

总请求数: {current_period_stats.get(TOTAL_REQ_CNT, 0)}

+

总花费: {current_period_stats.get(TOTAL_COST, 0.0):.4f} ¥

+ """ + + html_content_for_tab += "

按模型分类统计

模型名称调用次数输入Token输出TokenToken总量累计花费
" + req_cnt_by_model = current_period_stats.get(REQ_CNT_BY_MODEL, {}) + in_tok_by_model = current_period_stats.get(IN_TOK_BY_MODEL, defaultdict(int)) + out_tok_by_model = current_period_stats.get(OUT_TOK_BY_MODEL, defaultdict(int)) + total_tok_by_model = current_period_stats.get(TOTAL_TOK_BY_MODEL, defaultdict(int)) + cost_by_model = current_period_stats.get(COST_BY_MODEL, defaultdict(float)) + if req_cnt_by_model: + for model_name, count in sorted(req_cnt_by_model.items()): + html_content_for_tab += ( f"" f"" f"" - f"" - f"" - f"" - f"" + f"" + f"" + f"" + f"" f"" - for model_name, count in sorted(stat_data[REQ_CNT_BY_MODEL].items()) - ] - ) - } - -
模型名称调用次数输入Token输出TokenToken总量累计花费
{model_name}{count}{stat_data[IN_TOK_BY_MODEL][model_name]}{stat_data[OUT_TOK_BY_MODEL][model_name]}{stat_data[TOTAL_TOK_BY_MODEL][model_name]}{stat_data[COST_BY_MODEL][model_name]:.4f} ¥{in_tok_by_model[model_name]}{out_tok_by_model[model_name]}{total_tok_by_model[model_name]}{cost_by_model[model_name]:.4f} ¥
- -

按请求类型分类统计

- - - - - - { - "\n".join( - [ + ) + else: + html_content_for_tab += "" + html_content_for_tab += "
请求类型调用次数输入Token输出TokenToken总量累计花费
无数据
" + + html_content_for_tab += "

按请求类型分类统计

" + req_cnt_by_type = current_period_stats.get(REQ_CNT_BY_TYPE, {}) + in_tok_by_type = current_period_stats.get(IN_TOK_BY_TYPE, defaultdict(int)) + out_tok_by_type = current_period_stats.get(OUT_TOK_BY_TYPE, defaultdict(int)) + total_tok_by_type = current_period_stats.get(TOTAL_TOK_BY_TYPE, defaultdict(int)) + cost_by_type = current_period_stats.get(COST_BY_TYPE, defaultdict(float)) + if req_cnt_by_type: + for req_type, count in sorted(req_cnt_by_type.items()): + html_content_for_tab += ( f"" f"" f"" - f"" - f"" - f"" - f"" + f"" + f"" + f"" + f"" f"" - for req_type, count in sorted(stat_data[REQ_CNT_BY_TYPE].items()) - ] - ) - } - -
请求类型调用次数输入Token输出TokenToken总量累计花费
{req_type}{count}{stat_data[IN_TOK_BY_TYPE][req_type]}{stat_data[OUT_TOK_BY_TYPE][req_type]}{stat_data[TOTAL_TOK_BY_TYPE][req_type]}{stat_data[COST_BY_TYPE][req_type]:.4f} ¥{in_tok_by_type[req_type]}{out_tok_by_type[req_type]}{total_tok_by_type[req_type]}{cost_by_type[req_type]:.4f} ¥
- -

按用户分类统计

- - - - - - { - "\n".join( - [ + ) + else: + html_content_for_tab += "" + html_content_for_tab += "
用户名称调用次数输入Token输出TokenToken总量累计花费
无数据
" + + html_content_for_tab += "

按用户分类统计

" + req_cnt_by_user = current_period_stats.get(REQ_CNT_BY_USER, {}) + in_tok_by_user = current_period_stats.get(IN_TOK_BY_USER, defaultdict(int)) + out_tok_by_user = current_period_stats.get(OUT_TOK_BY_USER, defaultdict(int)) + total_tok_by_user = current_period_stats.get(TOTAL_TOK_BY_USER, defaultdict(int)) + cost_by_user = current_period_stats.get(COST_BY_USER, defaultdict(float)) + if req_cnt_by_user: + for user_id, count in sorted(req_cnt_by_user.items()): + user_display_name = self.name_mapping.get(user_id, (user_id, None))[0] + html_content_for_tab += ( f"" - f"" + f"" f"" - f"" - f"" - f"" - f"" + f"" + f"" + f"" + f"" f"" - for user_id, count in sorted(stat_data[REQ_CNT_BY_USER].items()) - ] - ) - } - -
用户ID/名称调用次数输入Token输出TokenToken总量累计花费
{user_id}{user_display_name}{count}{stat_data[IN_TOK_BY_USER][user_id]}{stat_data[OUT_TOK_BY_USER][user_id]}{stat_data[TOTAL_TOK_BY_USER][user_id]}{stat_data[COST_BY_USER][user_id]:.4f} ¥{in_tok_by_user[user_id]}{out_tok_by_user[user_id]}{total_tok_by_user[user_id]}{cost_by_user[user_id]:.4f} ¥
- -

聊天消息统计

- - - - - - { - "\n".join( - [ - f"" - for chat_id, count in sorted(stat_data[MSG_CNT_BY_CHAT].items()) - ] - ) - } - -
联系人/群组名称消息数量
{self.name_mapping[chat_id][0]}{count}
-
- """ + ) + else: + html_content_for_tab += "无数据" + html_content_for_tab += "" - 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" - ] + html_content_for_tab += "

聊天消息统计

" + msg_cnt_by_chat = current_period_stats.get(MSG_CNT_BY_CHAT, {}) + if msg_cnt_by_chat: + for chat_id, count in sorted(msg_cnt_by_chat.items()): + chat_name_display = self.name_mapping.get(chat_id, (f"未知/归档聊天 ({chat_id})", None))[0] + html_content_for_tab += f"" + else: + html_content_for_tab += "" + html_content_for_tab += "
联系人/群组名称消息数量
{chat_name_display}{count}
无数据
" + + tab_content_html_list.append(html_content_for_tab) - tab_content_list.append( - _format_stat_data(stat["all_time"], "all_time", datetime.fromtimestamp(local_storage["deploy_time"])) - ) html_template = ( """ @@ -684,6 +720,7 @@ class StatisticOutputTask(AsyncTask): border: 1px solid #ddd; padding: 10px; text-align: left; + word-break: break-all; } th { background-color: #3498db; @@ -702,24 +739,38 @@ class StatisticOutputTask(AsyncTask): .tabs { overflow: hidden; background: #ecf0f1; - display: flex; + display: flex; + flex-wrap: wrap; + margin-bottom: -1px; } .tabs button { - background: inherit; border: none; outline: none; - padding: 14px 16px; cursor: pointer; - transition: 0.3s; font-size: 16px; + background: inherit; + border: 1px solid #ccc; + border-bottom: none; + outline: none; + padding: 14px 16px; + cursor: pointer; + transition: 0.3s; + font-size: 16px; + margin-right: 2px; + border-radius: 4px 4px 0 0; } .tabs button:hover { background-color: #d4dbdc; } .tabs button.active { - background-color: #b3bbbd; + background-color: #fff; + border-color: #ccc; + border-bottom: 1px solid #fff; + position: relative; + z-index: 1; } .tab-content { display: none; padding: 20px; background-color: #fff; border: 1px solid #ccc; + border-top: none; } .tab-content.active { display: block; @@ -734,10 +785,14 @@ class StatisticOutputTask(AsyncTask):

统计截止时间: {now.strftime("%Y-%m-%d %H:%M:%S")}

- {"\n".join(tab_list)} + {"".join(tab_list_html)}
- {"\n".join(tab_content_list)} + {"".join(tab_content_html_list)} + +
+

Generated by MaiBot Statistics Module

+
""" + """ @@ -746,20 +801,36 @@ class StatisticOutputTask(AsyncTask): tab_content = document.getElementsByClassName("tab-content"); tab_links = document.getElementsByClassName("tab-link"); - tab_content[0].classList.add("active"); - tab_links[0].classList.add("active"); + if (tab_content.length > 0 && tab_links.length > 0) { + 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"); - }} + 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"); + } + const currentTabContent = document.getElementById(tabName); + if (currentTabContent) { + currentTabContent.classList.add("active"); + } + if (evt.currentTarget) { + evt.currentTarget.classList.add("active"); + } + } """ ) - with open(self.record_file_path, "w", encoding="utf-8") as f: - f.write(html_template) + try: + with open(self.record_file_path, "w", encoding="utf-8") as f: + f.write(html_template) + logger.info(f"统计报告已生成: {self.record_file_path}") + except IOError as e: + logger.error(f"无法写入统计报告文件 {self.record_file_path}: {e}") +