diff --git a/src/webui/annual_report_routes.py b/src/webui/annual_report_routes.py index bca1e188..08a4288f 100644 --- a/src/webui/annual_report_routes.py +++ b/src/webui/annual_report_routes.py @@ -54,7 +54,6 @@ class SocialNetworkData(BaseModel): """社交网络数据""" total_groups: int = Field(0, description="加入的群组总数") - new_friends_count: int = Field(0, description="今年新认识的朋友数") top_groups: List[Dict[str, Any]] = Field(default_factory=list, description="话痨群组TOP3") top_users: List[Dict[str, Any]] = Field(default_factory=list, description="互动最多的用户TOP3") at_count: int = Field(0, description="被@次数") @@ -71,6 +70,7 @@ class BrainPowerData(BaseModel): favorite_model: Optional[str] = Field(None, description="最爱用的模型") favorite_model_count: int = Field(0, description="最爱模型的调用次数") model_distribution: List[Dict[str, Any]] = Field(default_factory=list, description="模型使用分布") + top_reply_models: List[Dict[str, Any]] = Field(default_factory=list, description="最喜欢的回复模型TOP5") most_expensive_cost: float = Field(0.0, description="最昂贵的一次思考花费") most_expensive_time: Optional[str] = Field(None, description="最昂贵思考的时间") top_token_consumers: List[Dict[str, Any]] = Field(default_factory=list, description="烧钱大户TOP3") @@ -89,13 +89,15 @@ class ExpressionVibeData(BaseModel): """个性与表达数据""" top_emoji: Optional[Dict[str, Any]] = Field(None, description="表情包之王") - top_emojis: List[Dict[str, Any]] = Field(default_factory=list, description="TOP5表情包") - top_expressions: List[Dict[str, Any]] = Field(default_factory=list, description="最常用的表达风格") + top_emojis: List[Dict[str, Any]] = Field(default_factory=list, description="TOP3表情包") + top_expressions: List[Dict[str, Any]] = Field(default_factory=list, description="印象最深刻的表达风格") rejected_expression_count: int = Field(0, description="被拒绝的表达次数") checked_expression_count: int = Field(0, description="已检查的表达次数") total_expressions: int = Field(0, description="表达总数") action_types: List[Dict[str, Any]] = Field(default_factory=list, description="动作类型分布") image_processed_count: int = Field(0, description="处理的图片数量") + late_night_reply: Optional[Dict[str, Any]] = Field(None, description="深夜还在回复") + favorite_reply: Optional[Dict[str, Any]] = Field(None, description="最喜欢的回复") class AchievementData(BaseModel): @@ -231,25 +233,19 @@ async def get_time_footprint(year: int = 2025) -> TimeFootprintData: async def get_social_network(year: int = 2025) -> SocialNetworkData: """获取社交网络数据""" + from src.config.config import global_config + data = SocialNetworkData() start_ts, end_ts = get_year_time_range(year) + + # 获取 bot 自身的 QQ 账号，用于过滤 + bot_qq = str(global_config.bot.qq_account or "") try: # 1. 加入的群组总数 data.total_groups = ChatStreams.select().where(ChatStreams.group_id.is_null(False)).count() - # 2. 今年新认识的朋友数 - data.new_friends_count = ( - PersonInfo.select() - .where( - (PersonInfo.know_times.is_null(False)) - & (PersonInfo.know_times >= start_ts) - & (PersonInfo.know_times <= end_ts) - ) - .count() - ) - - # 3. 话痨群组 TOP3 + # 2. 话痨群组 TOP3 top_groups_query = ( Messages.select( Messages.chat_info_group_id, @@ -274,7 +270,7 @@ async def get_social_network(year: int = 2025) -> SocialNetworkData: for row in top_groups_query.dicts() ] - # 4. 互动最多的用户 TOP3 + # 3. 互动最多的用户 TOP3（过滤 bot 自身） top_users_query = ( Messages.select( Messages.user_id, @@ -285,6 +281,7 @@ async def get_social_network(year: int = 2025) -> SocialNetworkData: (Messages.time >= start_ts) & (Messages.time <= end_ts) & (Messages.user_id.is_null(False)) + & (Messages.user_id != bot_qq) # 过滤 bot 自身 ) .group_by(Messages.user_id) .order_by(fn.COUNT(Messages.id).desc()) @@ -299,7 +296,7 @@ async def get_social_network(year: int = 2025) -> SocialNetworkData: for row in top_users_query.dicts() ] - # 5. 被@次数 + # 4. 被@次数 data.at_count = ( Messages.select() .where( @@ -310,7 +307,7 @@ async def get_social_network(year: int = 2025) -> SocialNetworkData: .count() ) - # 6. 被提及次数 + # 5. 被提及次数 data.mentioned_count = ( Messages.select() .where( @@ -321,15 +318,17 @@ async def get_social_network(year: int = 2025) -> SocialNetworkData: .count() ) - # 7. 最长情陪伴的用户 - # 找出跨度时间最长的用户 + # 6. 最长情陪伴的用户（过滤 bot 自身） companion_query = ( ChatStreams.select( ChatStreams.user_id, ChatStreams.user_nickname, (ChatStreams.last_active_time - ChatStreams.create_time).alias("duration"), ) - .where(ChatStreams.user_id.is_null(False)) + .where( + (ChatStreams.user_id.is_null(False)) + & (ChatStreams.user_id != bot_qq) # 过滤 bot 自身 + ) .order_by((ChatStreams.last_active_time - ChatStreams.create_time).desc()) .limit(1) ) @@ -403,14 +402,19 @@ async def get_brain_power(year: int = 2025) -> BrainPowerData: data.most_expensive_cost = round(expensive_result.cost or 0, 4) data.most_expensive_time = expensive_result.timestamp.strftime("%Y-%m-%d %H:%M:%S") - # 4. 烧钱大户 TOP3 (按用户) + # 4. 烧钱大户 TOP3 (按用户，过滤 system) consumer_query = ( LLMUsage.select( LLMUsage.user_id, fn.COALESCE(fn.SUM(LLMUsage.cost), 0).alias("cost"), fn.COALESCE(fn.SUM(LLMUsage.total_tokens), 0).alias("tokens"), ) - .where((LLMUsage.timestamp >= start_dt) & (LLMUsage.timestamp <= end_dt)) + .where( + (LLMUsage.timestamp >= start_dt) + & (LLMUsage.timestamp <= end_dt) + & (LLMUsage.user_id != "system") # 过滤 system 用户 + & (LLMUsage.user_id.is_null(False)) + ) .group_by(LLMUsage.user_id) .order_by(fn.SUM(LLMUsage.cost).desc()) .limit(3) @@ -424,7 +428,32 @@ async def get_brain_power(year: int = 2025) -> BrainPowerData: for row in consumer_query.dicts() ] - # 5. 高冷指数 (沉默率) - 基于 ActionRecords + # 5. 最喜欢的回复模型 TOP5（按模型的回复次数统计，只统计 replyer 调用） + # 假设 replyer 调用有特定的 model_assign_name 格式或可以通过某种方式识别 + reply_model_query = ( + LLMUsage.select( + fn.COALESCE(LLMUsage.model_assign_name, LLMUsage.model_name).alias("model"), + fn.COUNT(LLMUsage.id).alias("count"), + ) + .where( + (LLMUsage.timestamp >= start_dt) + & (LLMUsage.timestamp <= end_dt) + & ( + LLMUsage.model_assign_name.contains("replyer") + | LLMUsage.model_assign_name.contains("回复") + | LLMUsage.model_assign_name.is_null(True) # 包含没有 assign_name 的情况 + ) + ) + .group_by(fn.COALESCE(LLMUsage.model_assign_name, LLMUsage.model_name)) + .order_by(fn.COUNT(LLMUsage.id).desc()) + .limit(5) + ) + data.top_reply_models = [ + {"model": row["model"], "count": row["count"]} + for row in reply_model_query.dicts() + ] + + # 6. 高冷指数 (沉默率) - 基于 ActionRecords total_actions = ActionRecords.select().where( (ActionRecords.time >= start_ts) & (ActionRecords.time <= end_ts) ).count() @@ -586,7 +615,12 @@ async def get_expression_vibe(year: int = 2025) -> ExpressionVibeData: .count() ) - # 6. 动作类型分布 (非 reply 动作) + # 6. 动作类型分布 (过滤无意义的动作) + # 过滤掉: no_reply_until_call, make_question, no_action, wait, complete_talk, listening, block_and_ignore + excluded_actions = [ + "reply", "no_reply", "no_reply_until_call", "make_question", + "no_action", "wait", "complete_talk", "listening", "block_and_ignore" + ] action_query = ( ActionRecords.select( ActionRecords.action_name, @@ -595,8 +629,7 @@ async def get_expression_vibe(year: int = 2025) -> ExpressionVibeData: .where( (ActionRecords.time >= start_ts) & (ActionRecords.time <= end_ts) - & (ActionRecords.action_name != "reply") - & (ActionRecords.action_name != "no_reply") + & (ActionRecords.action_name.not_in(excluded_actions)) ) .group_by(ActionRecords.action_name) .order_by(fn.COUNT(ActionRecords.id).desc()) @@ -618,6 +651,96 @@ async def get_expression_vibe(year: int = 2025) -> ExpressionVibeData: .count() ) + # 8. 深夜还在回复 (0-6点最晚的10条消息中随机抽取一条) + import random + late_night_messages = list( + Messages.select( + Messages.time, + Messages.processed_plain_text, + Messages.display_message, + ) + .where( + (Messages.time >= start_ts) + & (Messages.time <= end_ts) + & (Messages.is_self == True) # bot 发送的消息 + ) + .order_by(Messages.time.desc()) + ) + # 筛选出0-6点的消息 + late_night_filtered = [] + for msg in late_night_messages: + msg_dt = datetime.fromtimestamp(msg.time) + hour = msg_dt.hour + if 0 <= hour < 6: # 0点到6点 + late_night_filtered.append({ + "time": msg.time, + "hour": hour, + "minute": msg_dt.minute, + "content": msg.processed_plain_text or msg.display_message or "", + "datetime_str": msg_dt.strftime("%H:%M"), + }) + if len(late_night_filtered) >= 10: + break + + if late_night_filtered: + selected = random.choice(late_night_filtered) + content = selected["content"][:50] + "..." if len(selected["content"]) > 50 else selected["content"] + data.late_night_reply = { + "time": selected["datetime_str"], + "content": content, + } + + # 9. 最喜欢的回复（按 action_data 统计回复内容出现次数） + from collections import Counter + import json as json_lib + + reply_records = ( + ActionRecords.select(ActionRecords.action_data) + .where( + (ActionRecords.time >= start_ts) + & (ActionRecords.time <= end_ts) + & (ActionRecords.action_name == "reply") + & (ActionRecords.action_data.is_null(False)) + & (ActionRecords.action_data != "") + ) + ) + + reply_contents = [] + for record in reply_records: + try: + # action_data 可能是 JSON 格式 + action_data = record.action_data + if action_data: + # 尝试解析 JSON + try: + parsed = json_lib.loads(action_data) + if isinstance(parsed, dict) and "content" in parsed: + content = parsed["content"] + elif isinstance(parsed, str): + content = parsed + else: + content = str(parsed) + except (json_lib.JSONDecodeError, TypeError): + content = action_data + + # 只统计有意义的回复（长度大于2） + if content and len(content) > 2: + reply_contents.append(content) + except Exception: + continue + + if reply_contents: + content_counter = Counter(reply_contents) + most_common = content_counter.most_common(1) + if most_common: + fav_content, fav_count = most_common[0] + # 截断过长的内容 + display_content = fav_content[:50] + "..." if len(fav_content) > 50 else fav_content + data.favorite_reply = { + "content": display_content, + "count": fav_count, + } + except Exception as e: logger.error(f"获取个性与表达数据失败: {e}")