add thinking

src/classes/ai.py

@@ -26,21 +26,21 @@ class AI(ABC):
     def __init__(self, avatar: Avatar):
         self.avatar = avatar
 
-    async def decide(self, world: World) -> tuple[ACTION_NAME, ACTION_PARAMS, Event]:
+    async def decide(self, world: World) -> tuple[ACTION_NAME, ACTION_PARAMS, str, Event]:
         """
         决定做什么，同时生成对应的事件
         """
         # 先决定动作和参数
-        action_name, action_params = await self._decide(world)
+        action_name, action_params, avatar_thinking = await self._decide(world)
         
         # 获取动作对象并生成事件
         action = self.avatar.create_action(action_name)
         event = action.get_event(**action_params)
         
-        return action_name, action_params, event
+        return action_name, action_params, avatar_thinking, event
 
     @abstractmethod
-    async def _decide(self, world: World) -> ACTION_PAIR:
+    async def _decide(self, world: World) -> tuple[ACTION_NAME, ACTION_PARAMS, str]:
         """
         决策逻辑：决定执行什么动作和参数
         由子类实现具体的决策逻辑
@@ -51,7 +51,7 @@ class RuleAI(AI):
     """
     规则AI
     """
-    async def _decide(self, world: World) -> ACTION_PAIR:
+    async def _decide(self, world: World) -> tuple[ACTION_NAME, ACTION_PARAMS, str]:
         """
         决策逻辑：决定执行什么动作和参数
         先做一个简单的：
@@ -62,15 +62,15 @@ class RuleAI(AI):
         5. 如果需要突破境界了，则突破境界
         """
         if random.random() < 0.1:
-            return "Play", {}
+            return "Play", {}, ""
         best_region = self.get_best_region(list(world.map.regions.values()))
         if self.avatar.is_in_region(best_region):
             if self.avatar.cultivation_progress.can_break_through():
-                return "Breakthrough", {}
+                return "Breakthrough", {}, ""
             else:
-                return "Cultivate", {}
+                return "Cultivate", {}, ""
         else:
-            return "MoveToRegion", {"region": best_region.name}
+            return "MoveToRegion", {"region": best_region.name}, ""
     
     def get_best_region(self, regions: list[Region]) -> Region:
         """
@@ -93,7 +93,7 @@ class LLMAI(AI):
         不能每个单步step都调用一次LLM来决定下一步做什么。这样子一方面动作一直乱变，另一方面也太费token了。
         decide的作用是，拉取既有的动作链（如果没有了就call_llm），再根据动作链决定动作，以及动作之间的衔接。
     """
-    async def _decide(self, world: World) -> ACTION_PAIR:
+    async def _decide(self, world: World) -> tuple[ACTION_NAME, ACTION_PARAMS, str]:
         """
         异步决策逻辑：通过LLM决定执行什么动作和参数
         """
@@ -108,5 +108,5 @@ class LLMAI(AI):
             "avatar_persona": avatar_persona
         }
         res = await get_ai_prompt_and_call_llm_async(dict_info)
-        action_name, action_params = res["action_name"], res["action_params"]
-        return action_name, action_params
+        action_name, action_params, avatar_thinking = res["action_name"], res["action_params"], res["avatar_thinking"]
+        return action_name, action_params, avatar_thinking

src/classes/avatar.py

@@ -54,6 +54,7 @@ class Avatar:
     ai: AI = None
     cur_action_pair: Optional[ACTION_PAIR] = None
     history_action_pairs: list[ACTION_PAIR] = field(default_factory=list)
+    thinking: str = ""
 
     def __post_init__(self):
         """
@@ -101,8 +102,9 @@ class Avatar:
         
         if self.cur_action_pair is None:
             # 决定动作时生成事件
-            action_name, action_args, event = await self.ai.decide(self.world)
+            action_name, action_args, avatar_thinking, event = await self.ai.decide(self.world)
             action = self.create_action(action_name)
+            self.thinking = avatar_thinking
             self.cur_action_pair = (action, action_args)
         
         # 纯粹执行动作，不产生事件

src/front/front.py

@@ -8,6 +8,7 @@ from src.classes.world import World
 from src.classes.tile import TileType
 from src.classes.avatar import Avatar, Gender
 from src.classes.event import Event
+from src.utils.text_wrap import wrap_text
 
 
 class Front:
@@ -155,10 +156,7 @@ class Front:
 
         pygame.quit()
 
-    def _step_once(self):
-        """执行一步模拟（同步版本，已弃用）"""
-        print("警告：_step_once已弃用，请使用异步版本")
-        pass
+
 
     def _render(self):
         """渲染主画面"""
@@ -239,11 +237,6 @@ class Front:
         # 计算位置：放在操作指南右边，留适当间距
         x_pos = self.margin + self._guide_width + padding * 3
         self.screen.blit(ym_surf, (x_pos, y_pos))
-    
-    def _get_month_number(self) -> int:
-        """获取月份数字（已弃用，保留向后兼容）"""
-        return self.simulator.world.month_stamp.get_month().value
-
 
 
     def _draw_map(self):
@@ -445,6 +438,15 @@ class Front:
         lines.append("")  # 空行分隔
         lines.append("历史动作:")
         lines.extend(avatar.get_history_action_pairs_str().split("\n"))
+        
+        # 添加thinking信息
+        if avatar.thinking:
+            lines.append("")  # 空行分隔
+            lines.append("思考:")
+            # 使用wrap_text函数将thinking信息按20字符换行
+            thinking_lines = wrap_text(avatar.thinking, 20)
+            lines.extend(thinking_lines)
+        
         self._draw_tooltip(lines, *self.pygame.mouse.get_pos(), self.tooltip_font)
 
     def _draw_tooltip_for_region(self, region, mouse_x: int, mouse_y: int):
@@ -545,11 +547,7 @@ class Front:
                     self.avatar_images[avatar_id] = random.choice(self.female_avatars)
                 # 如果没有可用的头像，则使用None，后续会画圆点作为fallback
 
-    def _create_fallback_surface(self, tile_type):
-        """创建默认的fallback surface"""
-        fallback_surface = self.pygame.Surface((self.tile_size, self.tile_size))
-        fallback_surface.fill((128, 128, 128))  # 灰色
-        self.tile_images[tile_type] = fallback_surface
+
 
     def _create_font(self, size: int):
         """创建字体"""

src/utils/llm.py

@@ -71,8 +71,6 @@ def get_prompt_and_call_llm(template_path: Path, infos: dict) -> str:
     prompt = get_prompt(template, infos)
     res = call_llm(prompt)
     json_res = parse_llm_response(res)
-    # print(f"prompt = {prompt}")
-    # print(f"res = {res}")
     return json_res
 
 async def get_prompt_and_call_llm_async(template_path: Path, infos: dict) -> str:
@@ -82,7 +80,7 @@ async def get_prompt_and_call_llm_async(template_path: Path, infos: dict) -> str
     template = read_txt(template_path)
     prompt = get_prompt(template, infos)
     res = await call_llm_async(prompt)
-    print(f"res = {res}")
+    # print(f"res = {res}")
     json_res = parse_llm_response(res)
     return json_res
 

src/utils/text_wrap.py

@@ -0,0 +1,53 @@
+def wrap_text(text: str, max_width: int = 20) -> list[str]:
+    """
+    将长文本按指定宽度换行
+    
+    Args:
+        text: 要换行的文本
+        max_width: 每行的最大字符数，默认20
+        
+    Returns:
+        换行后的文本行列表
+    """
+    if not text:
+        return []
+    
+    lines = []
+    current_line = ""
+    
+    # 按换行符分割，处理已有的换行
+    paragraphs = text.split('\n')
+    
+    for paragraph in paragraphs:
+        if not paragraph.strip():
+            lines.append("")
+            continue
+            
+        words = paragraph.split()
+        
+        for word in words:
+            # 如果当前行加上新词会超过限制
+            if len(current_line) + len(word) + 1 > max_width:
+                if current_line:
+                    lines.append(current_line.strip())
+                    current_line = word
+                else:
+                    # 如果单个词就超过限制，强制换行
+                    if len(word) > max_width:
+                        # 长词强制切分
+                        for i in range(0, len(word), max_width):
+                            lines.append(word[i:i + max_width])
+                    else:
+                        lines.append(word)
+            else:
+                if current_line:
+                    current_line += " " + word
+                else:
+                    current_line = word
+        
+        # 处理段落的最后一行
+        if current_line:
+            lines.append(current_line.strip())
+            current_line = ""
+    
+    return lines