feat：动态麦麦人格测评工具
This commit is contained in:
SengokuCola
@@ -20,7 +20,7 @@ class LLM_request_off:
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if not self.api_key or not self.base_url:
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raise ValueError("环境变量未正确加载：SILICONFLOW_KEY 或 SILICONFLOW_BASE_URL 未设置")
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logger.info(f"API URL: {self.base_url}") # 使用 logger 记录 base_url
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# logger.info(f"API URL: {self.base_url}") # 使用 logger 记录 base_url
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def generate_response(self, prompt: str) -> Union[str, Tuple[str, str]]:
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"""根据输入的提示生成模型的响应"""
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@@ -1,36 +1,30 @@
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"""
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The definition of artificial personality in this paper follows the dispositional para-digm and adapts a definition of
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personality developed for humans [17]:
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Personality for a human is the "whole and organisation of relatively stable tendencies and patterns of experience and
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behaviour within one person (distinguishing it from other persons)". This definition is modified for artificial
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personality:
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Artificial personality describes the relatively stable tendencies and patterns of behav-iour of an AI-based machine that
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can be designed by developers and designers via different modalities, such as language, creating the impression
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of individuality of a humanized social agent when users interact with the machine."""
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from typing import Dict, List
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import json
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import os
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from pathlib import Path
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from dotenv import load_dotenv
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import sys
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import toml
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import random
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from tqdm import tqdm
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"""
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第一种方案：基于情景评估的人格测定
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"""
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current_dir = Path(__file__).resolve().parent
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project_root = current_dir.parent.parent.parent
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env_path = project_root / ".env"
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root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
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# 添加项目根目录到 Python 路径
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root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))
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sys.path.append(root_path)
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from src.individuality.scene import get_scene_by_factor, PERSONALITY_SCENES # noqa: E402
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from src.individuality.questionnaire import FACTOR_DESCRIPTIONS # noqa: E402
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from src.individuality.offline_llm import LLMModel # noqa: E402
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# 加载配置文件
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config_path = os.path.join(root_path, "config", "bot_config.toml")
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with open(config_path, "r", encoding="utf-8") as f:
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config = toml.load(f)
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# 现在可以导入src模块
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from src.individuality.scene import get_scene_by_factor, PERSONALITY_SCENES
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from src.individuality.questionnaire import FACTOR_DESCRIPTIONS
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from src.individuality.offline_llm import LLM_request_off
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# 加载环境变量
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if env_path.exists():
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env_path = os.path.join(root_path, ".env")
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if os.path.exists(env_path):
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print(f"从 {env_path} 加载环境变量")
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load_dotenv(env_path)
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else:
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@@ -38,10 +32,60 @@ else:
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print("将使用默认配置")
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def adapt_scene(scene: str) -> str:
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personality_core = config['personality']['personality_core']
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personality_sides = config['personality']['personality_sides']
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personality_side = random.choice(personality_sides)
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identity_details = config['identity']['identity_detail']
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identity_detail = random.choice(identity_details)
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"""
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根据config中的属性，改编场景使其更适合当前角色
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Args:
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scene: 原始场景描述
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Returns:
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str: 改编后的场景描述
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"""
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try:
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prompt = f"""
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这是一个参与人格测评的角色形象:
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- 昵称: {config['bot']['nickname']}
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- 性别: {config['identity']['gender']}
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- 年龄: {config['identity']['age']}岁
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- 外貌: {config['identity']['appearance']}
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- 性格核心: {personality_core}
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- 性格侧面: {personality_side}
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- 身份细节: {identity_detail}
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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:
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{scene}
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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。
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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。"""
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llm = LLM_request_off(model_name=config['model']['llm_normal']['name'])
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adapted_scene, _ = llm.generate_response(prompt)
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# 检查返回的场景是否为空或错误信息
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if not adapted_scene or "错误" in adapted_scene or "失败" in adapted_scene:
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print("场景改编失败，将使用原始场景")
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return scene
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return adapted_scene
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except Exception as e:
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print(f"场景改编过程出错：{str(e)}，将使用原始场景")
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return scene
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class PersonalityEvaluator_direct:
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def __init__(self):
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self.personality_traits = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
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self.scenarios = []
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self.final_scores = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
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self.dimension_counts = {trait: 0 for trait in self.final_scores.keys()}
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# 为每个人格特质获取对应的场景
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for trait in PERSONALITY_SCENES:
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@@ -67,7 +111,7 @@ class PersonalityEvaluator_direct:
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{"场景": scene["scenario"], "评估维度": [trait, secondary_trait], "场景编号": scene_key}
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)
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self.llm = LLMModel()
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self.llm = LLM_request_off()
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def evaluate_response(self, scenario: str, response: str, dimensions: List[str]) -> Dict[str, float]:
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"""
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@@ -126,24 +170,35 @@ class PersonalityEvaluator_direct:
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print(f"评估过程出错：{str(e)}")
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return {dim: 3.5 for dim in dimensions}
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def main():
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print("欢迎使用人格形象创建程序！")
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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。")
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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。")
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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")
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def run_evaluation(self):
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"""
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运行整个评估过程
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"""
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print(f"欢迎使用{config['bot']['nickname']}形象创建程序！")
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print("接下来，将给您呈现一系列有关您bot的场景（共15个）。")
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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。")
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print("每个场景都会进行不同方面的评估。")
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print("\n角色基本信息：")
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print(f"- 昵称：{config['bot']['nickname']}")
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print(f"- 性格核心：{config['personality']['personality_core']}")
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print(f"- 性格侧面：{config['personality']['personality_sides']}")
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print(f"- 身份细节：{config['identity']['identity_detail']}")
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print("\n准备好了吗？按回车键开始...")
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input()
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evaluator = PersonalityEvaluator_direct()
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final_scores = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
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dimension_counts = {trait: 0 for trait in final_scores.keys()}
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total_scenarios = len(self.scenarios)
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progress_bar = tqdm(total=total_scenarios, desc="场景进度", ncols=100, bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}]')
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for i, scenario_data in enumerate(evaluator.scenarios, 1):
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print(f"\n场景 {i}/{len(evaluator.scenarios)} - {scenario_data['场景编号']}:")
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print("-" * 50)
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print(scenario_data["场景"])
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print("\n请描述您的角色在这种情况下会如何反应：")
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for i, scenario_data in enumerate(self.scenarios, 1):
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# print(f"\n{'-' * 20} 场景 {i}/{total_scenarios} - {scenario_data['场景编号']} {'-' * 20}")
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# 改编场景，使其更适合当前角色
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print(f"{config['bot']['nickname']}祈祷中...")
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adapted_scene = adapt_scene(scenario_data["场景"])
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scenario_data["改编场景"] = adapted_scene
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print(adapted_scene)
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print(f"\n请描述{config['bot']['nickname']}在这种情况下会如何反应：")
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response = input().strip()
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if not response:
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@@ -151,45 +206,102 @@ def main():
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continue
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print("\n正在评估您的描述...")
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scores = evaluator.evaluate_response(scenario_data["场景"], response, scenario_data["评估维度"])
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scores = self.evaluate_response(adapted_scene, response, scenario_data["评估维度"])
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# 更新最终分数
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for dimension, score in scores.items():
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final_scores[dimension] += score
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dimension_counts[dimension] += 1
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self.final_scores[dimension] += score
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self.dimension_counts[dimension] += 1
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print("\n当前评估结果：")
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print("-" * 30)
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for dimension, score in scores.items():
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print(f"{dimension}: {score}/6")
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if i < len(evaluator.scenarios):
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print("\n按回车键继续下一个场景...")
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input()
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# 更新进度条
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progress_bar.update(1)
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# if i < total_scenarios:
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# print("\n按回车键继续下一个场景...")
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# input()
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progress_bar.close()
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# 计算平均分
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for dimension in final_scores:
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if dimension_counts[dimension] > 0:
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final_scores[dimension] = round(final_scores[dimension] / dimension_counts[dimension], 2)
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for dimension in self.final_scores:
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if self.dimension_counts[dimension] > 0:
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self.final_scores[dimension] = round(self.final_scores[dimension] / self.dimension_counts[dimension], 2)
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print("\n最终人格特征评估结果：")
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print("-" * 30)
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for trait, score in final_scores.items():
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print(f"{trait}: {score}/6")
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print(f"测试场景数：{dimension_counts[trait]}")
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print("\n" + "=" * 50)
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print(f" {config['bot']['nickname']}的人格特征评估结果 ".center(50))
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print("=" * 50)
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for trait, score in self.final_scores.items():
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print(f"{trait}: {score}/6".ljust(20) + f"测试场景数：{self.dimension_counts[trait]}".rjust(30))
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print("=" * 50)
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# 保存结果
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result = {"final_scores": final_scores, "dimension_counts": dimension_counts, "scenarios": evaluator.scenarios}
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# 返回评估结果
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return self.get_result()
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def get_result(self):
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"""
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获取评估结果
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"""
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return {
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"final_scores": self.final_scores,
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"dimension_counts": self.dimension_counts,
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"scenarios": self.scenarios,
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"bot_info": {
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"nickname": config['bot']['nickname'],
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"gender": config['identity']['gender'],
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"age": config['identity']['age'],
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"height": config['identity']['height'],
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"weight": config['identity']['weight'],
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"appearance": config['identity']['appearance'],
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"personality_core": config['personality']['personality_core'],
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"personality_sides": config['personality']['personality_sides'],
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"identity_detail": config['identity']['identity_detail']
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}
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}
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def main():
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evaluator = PersonalityEvaluator_direct()
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result = evaluator.run_evaluation()
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# 准备简化的结果数据
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simplified_result = {
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"openness": round(result["final_scores"]["开放性"] / 6, 1), # 转换为0-1范围
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"conscientiousness": round(result["final_scores"]["严谨性"] / 6, 1),
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"extraversion": round(result["final_scores"]["外向性"] / 6, 1),
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"agreeableness": round(result["final_scores"]["宜人性"] / 6, 1),
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"neuroticism": round(result["final_scores"]["神经质"] / 6, 1),
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"bot_nickname": config['bot']['nickname']
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}
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# 确保目录存在
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os.makedirs("results", exist_ok=True)
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save_dir = os.path.join(root_path, "data", "personality")
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os.makedirs(save_dir, exist_ok=True)
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# 保存到文件
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# 创建文件名，替换可能的非法字符
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bot_name = config['bot']['nickname']
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# 替换Windows文件名中不允许的字符
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for char in ['\\', '/', ':', '*', '?', '"', '<', '>', '|']:
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bot_name = bot_name.replace(char, '_')
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file_name = f"{bot_name}_personality.per"
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save_path = os.path.join(save_dir, file_name)
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# 保存简化的结果
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with open(save_path, "w", encoding="utf-8") as f:
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json.dump(simplified_result, f, ensure_ascii=False, indent=4)
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print(f"\n结果已保存到 {save_path}")
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# 同时保存完整结果到results目录
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os.makedirs("results", exist_ok=True)
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with open("results/personality_result.json", "w", encoding="utf-8") as f:
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json.dump(result, f, ensure_ascii=False, indent=2)
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print("\n结果已保存到 results/personality_result.json")
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if __name__ == "__main__":
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main()
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56
（测试版）麦麦生成人格.bat
Normal file
56
（测试版）麦麦生成人格.bat
Normal file
@@ -0,0 +1,56 @@
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@echo off
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chcp 65001 > nul
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setlocal enabledelayedexpansion
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cd /d %~dp0
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title 麦麦人格生成
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cls
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echo ======================================
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echo 警告提示
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echo ======================================
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echo 1.这是一个demo系统,仅供体验，特性可能会在将来移除
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echo ======================================
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echo.
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echo ======================================
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echo 请选择Python环境:
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echo 1 - venv (推荐)
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echo 2 - conda
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echo ======================================
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choice /c 12 /n /m "请输入数字选择(1或2): "
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if errorlevel 2 (
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echo ======================================
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set "CONDA_ENV="
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set /p CONDA_ENV="请输入要激活的 conda 环境名称: "
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:: 检查输入是否为空
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if "!CONDA_ENV!"=="" (
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echo 错误：环境名称不能为空
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pause
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exit /b 1
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)
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call conda activate !CONDA_ENV!
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if errorlevel 1 (
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echo 激活 conda 环境失败
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pause
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exit /b 1
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)
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echo Conda 环境 "!CONDA_ENV!" 激活成功
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python src/individuality/per_bf_gen.py
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) else (
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if exist "venv\Scripts\python.exe" (
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venv\Scripts\python src/individuality/per_bf_gen.py
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) else (
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echo ======================================
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echo 错误: venv环境不存在，请先创建虚拟环境
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pause
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exit /b 1
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)
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)
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endlocal
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pause
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Reference in New Issue
Block a user