better：重整配置，分离表达，聊天模式区分
重整配置文件路径，添加更多配置选项 分离了人设表达方式和学习到的表达方式 将聊天模式区分为normal focus和auto
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SengokuCola
311
src/individuality/not_using/per_bf_gen.py
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311
src/individuality/not_using/per_bf_gen.py
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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 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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# 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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# 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 individuality.not_using.scene import get_scene_by_factor, PERSONALITY_SCENES # noqa E402
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from individuality.not_using.questionnaire import FACTOR_DESCRIPTIONS # noqa E402
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from individuality.not_using.offline_llm import LLMRequestOff # noqa E402
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# 加载环境变量
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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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print(f"未找到环境变量文件: {env_path}")
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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"]}是面对这个场景的人，而不是场景的其他人。场景中不会有其描述，
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现在，请你给出改编后的场景描述
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"""
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llm = LLMRequestOff(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 PersonalityEvaluatorDirect:
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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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scenes = get_scene_by_factor(trait)
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if not scenes:
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continue
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# 从每个维度选择3个场景
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import random
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scene_keys = list(scenes.keys())
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selected_scenes = random.sample(scene_keys, min(3, len(scene_keys)))
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for scene_key in selected_scenes:
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scene = scenes[scene_key]
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# 为每个场景添加评估维度
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# 主维度是当前特质，次维度随机选择一个其他特质
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other_traits = [t for t in PERSONALITY_SCENES if t != trait]
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secondary_trait = random.choice(other_traits)
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self.scenarios.append(
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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 = LLMRequestOff()
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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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使用 DeepSeek AI 评估用户对特定场景的反应
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"""
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# 构建维度描述
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dimension_descriptions = []
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for dim in dimensions:
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desc = FACTOR_DESCRIPTIONS.get(dim, "")
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if desc:
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dimension_descriptions.append(f"- {dim}：{desc}")
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dimensions_text = "\n".join(dimension_descriptions)
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prompt = f"""请根据以下场景和用户描述，评估用户在大五人格模型中的相关维度得分（1-6分）。
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场景描述：
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{scenario}
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用户回应：
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{response}
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需要评估的维度说明：
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{dimensions_text}
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请按照以下格式输出评估结果（仅输出JSON格式）：
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{{
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"{dimensions[0]}": 分数,
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"{dimensions[1]}": 分数
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}}
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评分标准：
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1 = 非常不符合该维度特征
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2 = 比较不符合该维度特征
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3 = 有点不符合该维度特征
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4 = 有点符合该维度特征
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5 = 比较符合该维度特征
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6 = 非常符合该维度特征
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请根据用户的回应，结合场景和维度说明进行评分。确保分数在1-6之间，并给出合理的评估。"""
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try:
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ai_response, _ = self.llm.generate_response(prompt)
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# 尝试从AI响应中提取JSON部分
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start_idx = ai_response.find("{")
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end_idx = ai_response.rfind("}") + 1
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if start_idx != -1 and end_idx != 0:
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json_str = ai_response[start_idx:end_idx]
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scores = json.loads(json_str)
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# 确保所有分数在1-6之间
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return {k: max(1, min(6, float(v))) for k, v in scores.items()}
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else:
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print("AI响应格式不正确，使用默认评分")
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return {dim: 3.5 for dim in dimensions}
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except Exception as e:
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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 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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total_scenarios = len(self.scenarios)
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progress_bar = tqdm(
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total=total_scenarios,
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desc="场景进度",
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ncols=100,
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bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}]",
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)
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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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print("反应描述不能为空！")
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continue
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print("\n正在评估您的描述...")
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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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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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# 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 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" + "=" * 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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# 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 = PersonalityEvaluatorDirect()
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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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save_dir = os.path.join(root_path, "data", "personality")
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os.makedirs(save_dir, exist_ok=True)
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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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if __name__ == "__main__":
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main()
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