feat：动态麦麦人格测评工具

src/individuality/offline_llm.py

@@ -20,7 +20,7 @@ class LLM_request_off:
         if not self.api_key or not self.base_url:
             raise ValueError("环境变量未正确加载：SILICONFLOW_KEY 或 SILICONFLOW_BASE_URL 未设置")
 
-        logger.info(f"API URL: {self.base_url}")  # 使用 logger 记录 base_url
+        # logger.info(f"API URL: {self.base_url}")  # 使用 logger 记录 base_url
 
     def generate_response(self, prompt: str) -> Union[str, Tuple[str, str]]:
         """根据输入的提示生成模型的响应"""

src/individuality/per_bf_gen.py

@@ -1,36 +1,30 @@
-"""
-The definition of artificial personality in this paper follows the dispositional para-digm and adapts a definition of
-personality developed for humans [17]:
-Personality for a human is the "whole and organisation of relatively stable tendencies and patterns of experience and
-behaviour within one person (distinguishing it from other persons)". This definition is modified for artificial
-personality:
-Artificial personality describes the relatively stable tendencies and patterns of behav-iour of an AI-based machine that
-can be designed by developers and designers via different modalities, such as language, creating the impression
-of individuality of a humanized social agent when users interact with the machine."""
-
 from typing import Dict, List
 import json
 import os
 from pathlib import Path
 from dotenv import load_dotenv
 import sys
+import toml
+import random
+from tqdm import tqdm
 
-"""
+# 添加项目根目录到 Python 路径
-第一种方案：基于情景评估的人格测定
+root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))
-"""
-current_dir = Path(__file__).resolve().parent
-project_root = current_dir.parent.parent.parent
-env_path = project_root / ".env"
-
-root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
 sys.path.append(root_path)
 
-from src.individuality.scene import get_scene_by_factor, PERSONALITY_SCENES  # noqa: E402
+# 加载配置文件
-from src.individuality.questionnaire import FACTOR_DESCRIPTIONS  # noqa: E402
+config_path = os.path.join(root_path, "config", "bot_config.toml")
-from src.individuality.offline_llm import LLMModel  # noqa: E402
+with open(config_path, "r", encoding="utf-8") as f:
+    config = toml.load(f)
+
+# 现在可以导入src模块
+from src.individuality.scene import get_scene_by_factor, PERSONALITY_SCENES
+from src.individuality.questionnaire import FACTOR_DESCRIPTIONS
+from src.individuality.offline_llm import LLM_request_off
 
 # 加载环境变量
-if env_path.exists():
+env_path = os.path.join(root_path, ".env")
+if os.path.exists(env_path):
     print(f"从 {env_path} 加载环境变量")
     load_dotenv(env_path)
 else:
@@ -38,10 +32,60 @@ else:
     print("将使用默认配置")
 
 
+def adapt_scene(scene: str) -> str:
+    
+    personality_core = config['personality']['personality_core']
+    personality_sides = config['personality']['personality_sides']
+    personality_side = random.choice(personality_sides)
+    identity_details = config['identity']['identity_detail']
+    identity_detail = random.choice(identity_details)
+    
+    """
+    根据config中的属性，改编场景使其更适合当前角色
+    
+    Args:
+        scene: 原始场景描述
+        
+    Returns:
+        str: 改编后的场景描述
+    """
+    try:
+        prompt = f"""
+这是一个参与人格测评的角色形象:
+- 昵称: {config['bot']['nickname']}
+- 性别: {config['identity']['gender']}
+- 年龄: {config['identity']['age']}岁
+- 外貌: {config['identity']['appearance']}
+- 性格核心: {personality_core}
+- 性格侧面: {personality_side}
+- 身份细节: {identity_detail}
+
+请根据上述形象，改编以下原始场景，在测评中，用户将根据该场景给出上述角色形象的反应:
+{scene}
+
+保持场景的本质不变，但最好贴近生活且具体，并且让它更适合这个角色。
+改编后的场景应该自然、连贯，并考虑角色的年龄、身份和性格特点。只返回改编后的场景描述，不要包含其他说明。注意{config['bot']['nickname']}是面对这个情景的人，而不是场景的其他人。"""
+
+        llm = LLM_request_off(model_name=config['model']['llm_normal']['name'])
+        adapted_scene, _ = llm.generate_response(prompt)
+        
+        # 检查返回的场景是否为空或错误信息
+        if not adapted_scene or "错误" in adapted_scene or "失败" in adapted_scene:
+            print("场景改编失败，将使用原始场景")
+            return scene
+            
+        return adapted_scene
+    except Exception as e:
+        print(f"场景改编过程出错：{str(e)}，将使用原始场景")
+        return scene
+
+
 class PersonalityEvaluator_direct:
     def __init__(self):
         self.personality_traits = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
         self.scenarios = []
+        self.final_scores = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
+        self.dimension_counts = {trait: 0 for trait in self.final_scores.keys()}
 
         # 为每个人格特质获取对应的场景
         for trait in PERSONALITY_SCENES:
@@ -67,7 +111,7 @@ class PersonalityEvaluator_direct:
                     {"场景": scene["scenario"], "评估维度": [trait, secondary_trait], "场景编号": scene_key}
                 )
 
-        self.llm = LLMModel()
+        self.llm = LLM_request_off()
 
     def evaluate_response(self, scenario: str, response: str, dimensions: List[str]) -> Dict[str, float]:
         """
@@ -126,24 +170,35 @@ class PersonalityEvaluator_direct:
             print(f"评估过程出错：{str(e)}")
             return {dim: 3.5 for dim in dimensions}
     
-
+    def run_evaluation(self):
-def main():
+        """
-    print("欢迎使用人格形象创建程序！")
+        运行整个评估过程
-    print("接下来，您将面对一系列场景（共15个）。请根据您想要创建的角色形象，描述在该场景下可能的反应。")
+        """
-    print("每个场景都会评估不同的人格维度，最终得出完整的人格特征评估。")
+        print(f"欢迎使用{config['bot']['nickname']}形象创建程序！")
-    print("评分标准：1=非常不符合，2=比较不符合，3=有点不符合，4=有点符合，5=比较符合，6=非常符合")
+        print("接下来，将给您呈现一系列有关您bot的场景（共15个）。")
+        print("请想象您的bot在以下场景下会做什么，并描述您的bot的反应。")
+        print("每个场景都会进行不同方面的评估。")
+        print("\n角色基本信息：")
+        print(f"- 昵称：{config['bot']['nickname']}")
+        print(f"- 性格核心：{config['personality']['personality_core']}")
+        print(f"- 性格侧面：{config['personality']['personality_sides']}")
+        print(f"- 身份细节：{config['identity']['identity_detail']}")
         print("\n准备好了吗？按回车键开始...")
         input()
         
-    evaluator = PersonalityEvaluator_direct()
+        total_scenarios = len(self.scenarios)
-    final_scores = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
+        progress_bar = tqdm(total=total_scenarios, desc="场景进度", ncols=100, bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}]')
-    dimension_counts = {trait: 0 for trait in final_scores.keys()}
 
-    for i, scenario_data in enumerate(evaluator.scenarios, 1):
+        for i, scenario_data in enumerate(self.scenarios, 1):
-        print(f"\n场景 {i}/{len(evaluator.scenarios)} - {scenario_data['场景编号']}:")
+            # print(f"\n{'-' * 20} 场景 {i}/{total_scenarios} - {scenario_data['场景编号']} {'-' * 20}")
-        print("-" * 50)
+            
-        print(scenario_data["场景"])
+            # 改编场景，使其更适合当前角色
-        print("\n请描述您的角色在这种情况下会如何反应：")
+            print(f"{config['bot']['nickname']}祈祷中...")
+            adapted_scene = adapt_scene(scenario_data["场景"])
+            scenario_data["改编场景"] = adapted_scene
+            
+            print(adapted_scene)
+            print(f"\n请描述{config['bot']['nickname']}在这种情况下会如何反应：")
             response = input().strip()
 
             if not response:
@@ -151,45 +206,102 @@ def main():
                 continue
 
             print("\n正在评估您的描述...")
-        scores = evaluator.evaluate_response(scenario_data["场景"], response, scenario_data["评估维度"])
+            scores = self.evaluate_response(adapted_scene, response, scenario_data["评估维度"])
 
             # 更新最终分数
             for dimension, score in scores.items():
-            final_scores[dimension] += score
+                self.final_scores[dimension] += score
-            dimension_counts[dimension] += 1
+                self.dimension_counts[dimension] += 1
 
             print("\n当前评估结果：")
             print("-" * 30)
             for dimension, score in scores.items():
                 print(f"{dimension}: {score}/6")
 
-        if i < len(evaluator.scenarios):
+            # 更新进度条
-            print("\n按回车键继续下一个场景...")
+            progress_bar.update(1)
-            input()
+            
+            # if i < total_scenarios:
+                # print("\n按回车键继续下一个场景...")
+                # input()
+        
+        progress_bar.close()
         
         # 计算平均分
-    for dimension in final_scores:
+        for dimension in self.final_scores:
-        if dimension_counts[dimension] > 0:
+            if self.dimension_counts[dimension] > 0:
-            final_scores[dimension] = round(final_scores[dimension] / dimension_counts[dimension], 2)
+                self.final_scores[dimension] = round(self.final_scores[dimension] / self.dimension_counts[dimension], 2)
 
-    print("\n最终人格特征评估结果：")
+        print("\n" + "=" * 50)
-    print("-" * 30)
+        print(f"  {config['bot']['nickname']}的人格特征评估结果  ".center(50))
-    for trait, score in final_scores.items():
+        print("=" * 50)
-        print(f"{trait}: {score}/6")
+        for trait, score in self.final_scores.items():
-        print(f"测试场景数：{dimension_counts[trait]}")
+            print(f"{trait}: {score}/6".ljust(20) + f"测试场景数：{self.dimension_counts[trait]}".rjust(30))
+        print("=" * 50)
 
-    # 保存结果
+        # 返回评估结果
-    result = {"final_scores": final_scores, "dimension_counts": dimension_counts, "scenarios": evaluator.scenarios}
+        return self.get_result()
+    
+    def get_result(self):
+        """
+        获取评估结果
+        """
+        return {
+            "final_scores": self.final_scores, 
+            "dimension_counts": self.dimension_counts, 
+            "scenarios": self.scenarios,
+            "bot_info": {
+                "nickname": config['bot']['nickname'],
+                "gender": config['identity']['gender'],
+                "age": config['identity']['age'],
+                "height": config['identity']['height'],
+                "weight": config['identity']['weight'],
+                "appearance": config['identity']['appearance'],
+                "personality_core": config['personality']['personality_core'],
+                "personality_sides": config['personality']['personality_sides'],
+                "identity_detail": config['identity']['identity_detail']
+            }
+        }
+
+
+def main():
+    evaluator = PersonalityEvaluator_direct()
+    result = evaluator.run_evaluation()
+
+    # 准备简化的结果数据
+    simplified_result = {
+        "openness": round(result["final_scores"]["开放性"] / 6, 1),  # 转换为0-1范围
+        "conscientiousness": round(result["final_scores"]["严谨性"] / 6, 1),
+        "extraversion": round(result["final_scores"]["外向性"] / 6, 1),
+        "agreeableness": round(result["final_scores"]["宜人性"] / 6, 1),
+        "neuroticism": round(result["final_scores"]["神经质"] / 6, 1),
+        "bot_nickname": config['bot']['nickname']
+    }
 
     # 确保目录存在
-    os.makedirs("results", exist_ok=True)
+    save_dir = os.path.join(root_path, "data", "personality")
+    os.makedirs(save_dir, exist_ok=True)
     
-    # 保存到文件
+    # 创建文件名，替换可能的非法字符
+    bot_name = config['bot']['nickname']
+    # 替换Windows文件名中不允许的字符
+    for char in ['\\', '/', ':', '*', '?', '"', '<', '>', '|']:
+        bot_name = bot_name.replace(char, '_')
+    
+    file_name = f"{bot_name}_personality.per"
+    save_path = os.path.join(save_dir, file_name)
+    
+    # 保存简化的结果
+    with open(save_path, "w", encoding="utf-8") as f:
+        json.dump(simplified_result, f, ensure_ascii=False, indent=4)
+
+    print(f"\n结果已保存到 {save_path}")
+    
+    # 同时保存完整结果到results目录
+    os.makedirs("results", exist_ok=True)
     with open("results/personality_result.json", "w", encoding="utf-8") as f:
         json.dump(result, f, ensure_ascii=False, indent=2)
 
-    print("\n结果已保存到 results/personality_result.json")
-
 
 if __name__ == "__main__":
     main()

（测试版）麦麦生成人格.bat

@@ -0,0 +1,56 @@
+@echo off
+chcp 65001 > nul
+setlocal enabledelayedexpansion
+cd /d %~dp0
+
+title 麦麦人格生成
+
+cls
+echo ======================================
+echo    警告提示
+echo ======================================
+echo  1.这是一个demo系统,仅供体验，特性可能会在将来移除
+echo ======================================
+
+echo.
+echo ======================================
+echo    请选择Python环境:
+echo    1 - venv (推荐)
+echo    2 - conda
+echo ======================================
+choice /c 12 /n /m "请输入数字选择(1或2): "
+
+if errorlevel 2 (
+    echo ======================================
+    set "CONDA_ENV="
+    set /p CONDA_ENV="请输入要激活的 conda 环境名称: "
+    
+    :: 检查输入是否为空
+    if "!CONDA_ENV!"=="" (
+        echo 错误：环境名称不能为空
+        pause
+        exit /b 1
+    )
+    
+    call conda activate !CONDA_ENV!
+    if errorlevel 1 (
+        echo 激活 conda 环境失败
+        pause
+        exit /b 1
+    )
+    
+    echo Conda 环境 "!CONDA_ENV!" 激活成功
+    python src/individuality/per_bf_gen.py
+) else (
+    if exist "venv\Scripts\python.exe" (
+        venv\Scripts\python src/individuality/per_bf_gen.py
+    ) else (
+        echo ======================================
+        echo 错误: venv环境不存在，请先创建虚拟环境
+        pause
+        exit /b 1
+    )
+)
+
+endlocal
+pause