secret 神秘小功能
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SengokuCola
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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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import random
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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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current_dir = Path(__file__).resolve().parent
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# 获取项目根目录（上三层目录）
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project_root = current_dir.parent.parent.parent
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# env.dev文件路径
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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.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:
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def __init__(self):
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self.personality_traits = {
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"开放性": 0,
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"尽责性": 0,
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"外向性": 0,
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"宜人性": 0,
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"神经质": 0
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}
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self.scenarios = [
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{
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"场景": "在团队项目中，你发现一个同事的工作质量明显低于预期，这可能会影响整个项目的进度。",
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"评估维度": ["尽责性", "宜人性"]
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},
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{
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"场景": "你被邀请参加一个完全陌生的社交活动，现场都是不认识的人。",
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"评估维度": ["外向性", "神经质"]
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},
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{
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"场景": "你的朋友向你推荐了一个新的艺术展览，但风格与你平时接触的完全不同。",
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"评估维度": ["开放性", "外向性"]
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},
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{
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"场景": "在工作中，你遇到了一个技术难题，需要学习全新的技术栈。",
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"评估维度": ["开放性", "尽责性"]
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},
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{
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"场景": "你的朋友因为个人原因情绪低落，向你寻求帮助。",
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"评估维度": ["宜人性", "神经质"]
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}
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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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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（0-10分）。
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场景：{scenario}
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用户描述：{response}
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需要评估的维度：{', '.join(dimensions)}
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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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"维度1": 分数,
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"维度2": 分数
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}}
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评估标准：
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- 开放性：对新事物的接受程度和创造性思维
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- 尽责性：计划性、组织性和责任感
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- 外向性：社交倾向和能量水平
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- 宜人性：同理心、合作性和友善程度
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- 神经质：情绪稳定性和压力应对能力
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请确保分数在0-10之间，并给出合理的评估理由。"""
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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0-10之间
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return {k: max(0, min(10, 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: 5.0 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: 5.0 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。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("\n准备好了吗？按回车键开始...")
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input()
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evaluator = PersonalityEvaluator()
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final_scores = {
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"开放性": 0,
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"尽责性": 0,
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"外向性": 0,
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"宜人性": 0,
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"神经质": 0
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}
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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)}:")
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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}/10")
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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}/10")
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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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"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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