import asyncio import json import time from src.chat.message_receive.message import MessageRecv from src.llm_models.utils_model import LLMRequest from src.common.logger import get_logger from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_by_timestamp_with_chat_inclusive from src.config.config import global_config from src.chat.utils.prompt_builder import Prompt, global_prompt_manager from src.manager.async_task_manager import AsyncTask, async_task_manager from src.plugin_system.apis import send_api """ 面部表情系统使用说明： 1. 预定义的面部表情： - happy: 高兴表情（眼睛微笑 + 眉毛微笑 + 嘴巴大笑） - very_happy: 非常高兴（高兴表情 + 脸红） - sad: 悲伤表情（眼睛哭泣 + 眉毛忧伤 + 嘴巴悲伤） - angry: 生气表情（眉毛生气 + 嘴巴生气） - fear: 恐惧表情（眼睛闭上） - shy: 害羞表情（嘴巴嘟起 + 脸红） - neutral: 中性表情（无表情） 2. 使用方法： # 获取面部表情管理器 facial_expression = mood_manager.get_facial_expression_by_chat_id(chat_id) # 发送指定表情 await facial_expression.send_expression("happy") # 根据情绪值自动选择表情 await facial_expression.send_expression_by_mood(mood_values) # 重置为中性表情 await facial_expression.reset_expression() 3. 自动表情系统： - 当情绪值更新时，系统会自动根据mood_values选择合适的面部表情 - 只有当新表情与当前表情不同时才会发送，避免重复发送 - 支持joy >= 8时显示very_happy，joy >= 6时显示happy等梯度表情 4. amadus表情更新系统： - 每1秒检查一次表情是否有变化，如有变化则发送到amadus - 每次mood更新后立即发送表情更新 - 发送消息类型为"amadus_expression_update"，格式为{"action": "表情名", "data": 1.0} 5. 表情选择逻辑： - 系统会找出最强的情绪（joy, anger, sorrow, fear） - 根据情绪强度选择相应的表情组合 - 默认情况下返回neutral表情 """ logger = get_logger("mood") def init_prompt(): Prompt( """ {chat_talking_prompt} 以上是直播间里正在进行的对话 {indentify_block} 你刚刚的情绪状态是：{mood_state} 现在，发送了消息，引起了你的注意，你对其进行了阅读和思考，请你输出一句话描述你新的情绪状态，不要输出任何其他内容 请只输出情绪状态，不要输出其他内容： """, "change_mood_prompt_vtb", ) Prompt( """ {chat_talking_prompt} 以上是直播间里最近的对话 {indentify_block} 你之前的情绪状态是：{mood_state} 距离你上次关注直播间消息已经过去了一段时间，你冷静了下来，请你输出一句话描述你现在的情绪状态 请只输出情绪状态，不要输出其他内容： """, "regress_mood_prompt_vtb", ) Prompt( """ {chat_talking_prompt} 以上是直播间里正在进行的对话 {indentify_block} 你刚刚的情绪状态是：{mood_state} 具体来说，从1-10分，你的情绪状态是： 喜(Joy): {joy} 怒(Anger): {anger} 哀(Sorrow): {sorrow} 惧(Fear): {fear} 现在，发送了消息，引起了你的注意，你对其进行了阅读和思考。请基于对话内容，评估你新的情绪状态。 请以JSON格式输出你新的情绪状态，包含"喜怒哀惧"四个维度，每个维度的取值范围为1-10。 键值请使用英文: "joy", "anger", "sorrow", "fear". 例如: {{"joy": 5, "anger": 1, "sorrow": 1, "fear": 1}} 不要输出任何其他内容，只输出JSON。 """, "change_mood_numerical_prompt", ) Prompt( """ {chat_talking_prompt} 以上是直播间里最近的对话 {indentify_block} 你之前的情绪状态是：{mood_state} 具体来说，从1-10分，你的情绪状态是： 喜(Joy): {joy} 怒(Anger): {anger} 哀(Sorrow): {sorrow} 惧(Fear): {fear} 距离你上次关注直播间消息已经过去了一段时间，你冷静了下来。请基于此，评估你现在的情绪状态。 请以JSON格式输出你新的情绪状态，包含"喜怒哀惧"四个维度，每个维度的取值范围为1-10。 键值请使用英文: "joy", "anger", "sorrow", "fear". 例如: {{"joy": 5, "anger": 1, "sorrow": 1, "fear": 1}} 不要输出任何其他内容，只输出JSON。 """, "regress_mood_numerical_prompt", ) class FacialExpression: def __init__(self, chat_id: str): self.chat_id: str = chat_id # 预定义面部表情动作 self.expressions = { # 眼睛表情 "eye_smile": {"action": "eye_smile", "data": 1.0}, "eye_cry": {"action": "eye_cry", "data": 1.0}, "eye_close": {"action": "eye_close", "data": 1.0}, "eye_normal": {"action": "eye_normal", "data": 1.0}, # 眉毛表情 "eyebrow_smile": {"action": "eyebrow_smile", "data": 1.0}, "eyebrow_angry": {"action": "eyebrow_angry", "data": 1.0}, "eyebrow_sad": {"action": "eyebrow_sad", "data": 1.0}, "eyebrow_normal": {"action": "eyebrow_normal", "data": 1.0}, # 嘴巴表情 "mouth_sad": {"action": "mouth_sad", "data": 1.0}, "mouth_angry": {"action": "mouth_angry", "data": 1.0}, "mouth_laugh": {"action": "mouth_laugh", "data": 1.0}, "mouth_pout": {"action": "mouth_pout", "data": 1.0}, "mouth_normal": {"action": "mouth_normal", "data": 1.0}, # 脸部表情 "face_blush": {"action": "face_blush", "data": 1.0}, "face_normal": {"action": "face_normal", "data": 1.0}, } # 表情组合模板 self.expression_combinations = { "happy": { "eye": "eye_smile", "eyebrow": "eyebrow_smile", "mouth": "mouth_laugh", "face": "face_normal" }, "very_happy": { "eye": "eye_smile", "eyebrow": "eyebrow_smile", "mouth": "mouth_laugh", "face": "face_blush" }, "sad": { "eye": "eye_cry", "eyebrow": "eyebrow_sad", "mouth": "mouth_sad", "face": "face_normal" }, "angry": { "eye": "eye_normal", "eyebrow": "eyebrow_angry", "mouth": "mouth_angry", "face": "face_normal" }, "fear": { "eye": "eye_close", "eyebrow": "eyebrow_normal", "mouth": "mouth_normal", "face": "face_normal" }, "shy": { "eye": "eye_normal", "eyebrow": "eyebrow_normal", "mouth": "mouth_pout", "face": "face_blush" }, "neutral": { "eye": "eye_normal", "eyebrow": "eyebrow_normal", "mouth": "mouth_normal", "face": "face_normal" } } def select_expression_by_mood(self, mood_values: dict[str, int]) -> str: """根据情绪值选择合适的表情组合""" joy = mood_values.get("joy", 5) anger = mood_values.get("anger", 1) sorrow = mood_values.get("sorrow", 1) fear = mood_values.get("fear", 1) # 找出最强的情绪 emotions = { "joy": joy, "anger": anger, "sorrow": sorrow, "fear": fear } # 获取最强情绪 dominant_emotion = max(emotions, key=emotions.get) dominant_value = emotions[dominant_emotion] # 根据情绪强度和类型选择表情 if dominant_emotion == "joy": if joy >= 8: return "very_happy" elif joy >= 6: return "happy" elif joy >= 4: return "shy" else: return "neutral" elif dominant_emotion == "anger" and anger >= 6: return "angry" elif dominant_emotion == "sorrow" and sorrow >= 6: return "sad" elif dominant_emotion == "fear" and fear >= 6: return "fear" else: return "neutral" async def send_expression(self, expression_name: str): """发送表情组合""" if expression_name not in self.expression_combinations: logger.warning(f"[{self.chat_id}] 未知表情: {expression_name}") return combination = self.expression_combinations[expression_name] # 依次发送各部位表情 for part, expression_key in combination.items(): if expression_key in self.expressions: expression_data = self.expressions[expression_key] await send_api.custom_to_stream( message_type="facial_expression", content=expression_data, stream_id=self.chat_id ) logger.info(f"[{self.chat_id}] 发送面部表情 {part}: {expression_data}") await asyncio.sleep(0.1) # 短暂延迟避免同时发送过多消息 # 通知ChatMood需要更新amadus # 这里需要从mood_manager获取ChatMood实例并标记 chat_mood = mood_manager.get_mood_by_chat_id(self.chat_id) if chat_mood.last_expression != expression_name: chat_mood.last_expression = expression_name chat_mood.expression_needs_update = True async def send_expression_by_mood(self, mood_values: dict[str, int]): """根据情绪值发送相应的面部表情""" expression_name = self.select_expression_by_mood(mood_values) logger.info(f"[{self.chat_id}] 根据情绪值选择表情: {expression_name}, 情绪值: {mood_values}") await self.send_expression(expression_name) async def reset_expression(self): """重置为中性表情""" await self.send_expression("neutral") class ChatMood: def __init__(self, chat_id: str): self.chat_id: str = chat_id self.mood_state: str = "感觉很平静" self.mood_values: dict[str, int] = {"joy": 5, "anger": 1, "sorrow": 1, "fear": 1} self.regression_count: int = 0 self.mood_model = LLMRequest( model=global_config.model.emotion, temperature=0.7, request_type="mood_text", ) self.mood_model_numerical = LLMRequest( model=global_config.model.emotion, temperature=0.4, request_type="mood_numerical", ) self.last_change_time = 0 # 添加面部表情系统 self.facial_expression = FacialExpression(chat_id) self.last_expression = "neutral" # 记录上一次的表情 self.expression_needs_update = False # 标记表情是否需要更新 # 设置初始中性表情 asyncio.create_task(self.facial_expression.reset_expression()) self.expression_needs_update = True # 初始化时也标记需要更新 def _parse_numerical_mood(self, response: str) -> dict[str, int] | None: try: # The LLM might output markdown with json inside if "```json" in response: response = response.split("```json")[1].split("```")[0] elif "```" in response: response = response.split("```")[1].split("```")[0] data = json.loads(response) # Validate required_keys = {"joy", "anger", "sorrow", "fear"} if not required_keys.issubset(data.keys()): logger.warning(f"Numerical mood response missing keys: {response}") return None for key in required_keys: value = data[key] if not isinstance(value, int) or not (1 <= value <= 10): logger.warning(f"Numerical mood response invalid value for {key}: {value} in {response}") return None return {key: data[key] for key in required_keys} except json.JSONDecodeError: logger.warning(f"Failed to parse numerical mood JSON: {response}") return None except Exception as e: logger.error(f"Error parsing numerical mood: {e}, response: {response}") return None async def update_mood_by_message(self, message: MessageRecv): self.regression_count = 0 message_time = message.message_info.time message_list_before_now = get_raw_msg_by_timestamp_with_chat_inclusive( chat_id=self.chat_id, timestamp_start=self.last_change_time, timestamp_end=message_time, limit=15, limit_mode="last", ) chat_talking_prompt = build_readable_messages( message_list_before_now, replace_bot_name=True, merge_messages=False, timestamp_mode="normal_no_YMD", read_mark=0.0, truncate=True, show_actions=True, ) bot_name = global_config.bot.nickname if global_config.bot.alias_names: bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}" else: bot_nickname = "" prompt_personality = global_config.personality.personality_core indentify_block = f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}：" async def _update_text_mood(): prompt = await global_prompt_manager.format_prompt( "change_mood_prompt_vtb", chat_talking_prompt=chat_talking_prompt, indentify_block=indentify_block, mood_state=self.mood_state, ) logger.debug(f"text mood prompt: {prompt}") response, (reasoning_content, model_name) = await self.mood_model.generate_response_async(prompt=prompt) logger.info(f"text mood response: {response}") logger.debug(f"text mood reasoning_content: {reasoning_content}") return response async def _update_numerical_mood(): prompt = await global_prompt_manager.format_prompt( "change_mood_numerical_prompt", chat_talking_prompt=chat_talking_prompt, indentify_block=indentify_block, mood_state=self.mood_state, joy=self.mood_values["joy"], anger=self.mood_values["anger"], sorrow=self.mood_values["sorrow"], fear=self.mood_values["fear"], ) logger.info(f"numerical mood prompt: {prompt}") response, (reasoning_content, model_name) = await self.mood_model_numerical.generate_response_async( prompt=prompt ) logger.info(f"numerical mood response: {response}") logger.debug(f"numerical mood reasoning_content: {reasoning_content}") return self._parse_numerical_mood(response) results = await asyncio.gather(_update_text_mood(), _update_numerical_mood()) text_mood_response, numerical_mood_response = results if text_mood_response: self.mood_state = text_mood_response if numerical_mood_response: old_mood_values = self.mood_values.copy() self.mood_values = numerical_mood_response # 发送面部表情 new_expression = self.facial_expression.select_expression_by_mood(self.mood_values) if new_expression != self.last_expression: # 立即发送表情 asyncio.create_task(self.facial_expression.send_expression(new_expression)) self.last_expression = new_expression self.expression_needs_update = True # 标记表情已更新 self.last_change_time = message_time async def regress_mood(self): message_time = time.time() message_list_before_now = get_raw_msg_by_timestamp_with_chat_inclusive( chat_id=self.chat_id, timestamp_start=self.last_change_time, timestamp_end=message_time, limit=15, limit_mode="last", ) chat_talking_prompt = build_readable_messages( message_list_before_now, replace_bot_name=True, merge_messages=False, timestamp_mode="normal_no_YMD", read_mark=0.0, truncate=True, show_actions=True, ) bot_name = global_config.bot.nickname if global_config.bot.alias_names: bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}" else: bot_nickname = "" prompt_personality = global_config.personality.personality_core indentify_block = f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}：" async def _regress_text_mood(): prompt = await global_prompt_manager.format_prompt( "regress_mood_prompt_vtb", chat_talking_prompt=chat_talking_prompt, indentify_block=indentify_block, mood_state=self.mood_state, ) logger.debug(f"text regress prompt: {prompt}") response, (reasoning_content, model_name) = await self.mood_model.generate_response_async(prompt=prompt) logger.info(f"text regress response: {response}") logger.debug(f"text regress reasoning_content: {reasoning_content}") return response async def _regress_numerical_mood(): prompt = await global_prompt_manager.format_prompt( "regress_mood_numerical_prompt", chat_talking_prompt=chat_talking_prompt, indentify_block=indentify_block, mood_state=self.mood_state, joy=self.mood_values["joy"], anger=self.mood_values["anger"], sorrow=self.mood_values["sorrow"], fear=self.mood_values["fear"], ) logger.debug(f"numerical regress prompt: {prompt}") response, (reasoning_content, model_name) = await self.mood_model_numerical.generate_response_async( prompt=prompt ) logger.info(f"numerical regress response: {response}") logger.debug(f"numerical regress reasoning_content: {reasoning_content}") return self._parse_numerical_mood(response) results = await asyncio.gather(_regress_text_mood(), _regress_numerical_mood()) text_mood_response, numerical_mood_response = results if text_mood_response: self.mood_state = text_mood_response if numerical_mood_response: old_mood_values = self.mood_values.copy() self.mood_values = numerical_mood_response # 发送面部表情 new_expression = self.facial_expression.select_expression_by_mood(self.mood_values) if new_expression != self.last_expression: # 立即发送表情 asyncio.create_task(self.facial_expression.send_expression(new_expression)) self.last_expression = new_expression self.expression_needs_update = True # 标记表情已更新 self.regression_count += 1 async def send_expression_update_if_needed(self): """如果表情有变化，发送更新到amadus""" if self.expression_needs_update: # 发送当前表情状态到amadus，使用简洁的action/data格式 expression_data = { "action": self.last_expression, "data": 1.0 } await send_api.custom_to_stream( message_type="amadus_expression_update", content=expression_data, stream_id=self.chat_id ) logger.info(f"[{self.chat_id}] 发送表情更新到amadus: {expression_data}") self.expression_needs_update = False # 重置标记 class MoodRegressionTask(AsyncTask): def __init__(self, mood_manager: "MoodManager"): super().__init__(task_name="MoodRegressionTask", run_interval=30) self.mood_manager = mood_manager async def run(self): logger.debug("Running mood regression task...") now = time.time() for mood in self.mood_manager.mood_list: if mood.last_change_time == 0: continue if now - mood.last_change_time > 180: if mood.regression_count >= 3: continue logger.info(f"chat {mood.chat_id} 开始情绪回归, 这是第 {mood.regression_count + 1} 次") await mood.regress_mood() class ExpressionUpdateTask(AsyncTask): def __init__(self, mood_manager: "MoodManager"): super().__init__(task_name="ExpressionUpdateTask", run_interval=1) self.mood_manager = mood_manager async def run(self): logger.debug("Running expression update task...") for mood in self.mood_manager.mood_list: await mood.send_expression_update_if_needed() class MoodManager: def __init__(self): self.mood_list: list[ChatMood] = [] """当前情绪状态""" self.task_started: bool = False async def start(self): """启动情绪回归后台任务""" if self.task_started: return logger.info("启动情绪管理任务...") # 启动情绪回归任务 regression_task = MoodRegressionTask(self) await async_task_manager.add_task(regression_task) # 启动表情更新任务 expression_task = ExpressionUpdateTask(self) await async_task_manager.add_task(expression_task) self.task_started = True logger.info("情绪管理任务已启动（包含情绪回归和表情更新）") def get_mood_by_chat_id(self, chat_id: str) -> ChatMood: for mood in self.mood_list: if mood.chat_id == chat_id: return mood new_mood = ChatMood(chat_id) self.mood_list.append(new_mood) return new_mood def reset_mood_by_chat_id(self, chat_id: str): for mood in self.mood_list: if mood.chat_id == chat_id: mood.mood_state = "感觉很平静" mood.mood_values = {"joy": 5, "anger": 1, "sorrow": 1, "fear": 1} mood.regression_count = 0 # 重置面部表情为中性 asyncio.create_task(mood.facial_expression.reset_expression()) mood.last_expression = "neutral" mood.expression_needs_update = True # 标记表情需要更新 return # 如果没有找到现有的mood，创建新的 new_mood = ChatMood(chat_id) self.mood_list.append(new_mood) asyncio.create_task(new_mood.facial_expression.reset_expression()) new_mood.expression_needs_update = True # 标记表情需要更新 def get_facial_expression_by_chat_id(self, chat_id: str) -> FacialExpression: """获取聊天对应的面部表情管理器""" for mood in self.mood_list: if mood.chat_id == chat_id: return mood.facial_expression # 如果没有找到，创建新的 new_mood = ChatMood(chat_id) self.mood_list.append(new_mood) return new_mood.facial_expression init_prompt() mood_manager = MoodManager() """全局情绪管理器"""