199 lines
7.4 KiB
Python
199 lines
7.4 KiB
Python
'''
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The definition of artificial personality in this paper follows the dispositional para-digm and adapts a definition of 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 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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'''
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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.prod"
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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.plugins.personality.scene import get_scene_by_factor,get_all_scenes,PERSONALITY_SCENES
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from src.plugins.personality.questionnaire import PERSONALITY_QUESTIONS,FACTOR_DESCRIPTIONS
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from src.plugins.personality.offline_llm import LLMModel
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# 加载环境变量
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if env_path.exists():
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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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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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# 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"],
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"评估维度": [trait, secondary_trait],
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"场景编号": scene_key
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})
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self.llm = LLMModel()
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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 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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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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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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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 = evaluator.evaluate_response(scenario_data["场景"], 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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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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# 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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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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# 保存结果
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result = {
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"final_scores": final_scores,
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"dimension_counts": dimension_counts,
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"scenarios": evaluator.scenarios
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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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# 保存到文件
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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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