secret 神秘小测验加强版
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SengokuCola
351
src/plugins/personality/can_i_recog_u.py
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351
src/plugins/personality/can_i_recog_u.py
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"""
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基于聊天记录的人格特征分析系统
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"""
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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 random
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from collections import defaultdict
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import matplotlib.pyplot as plt
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import numpy as np
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from datetime import datetime
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import matplotlib.font_manager as fm
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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, PERSONALITY_SCENES # noqa: E402
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from src.plugins.personality.questionnaire import FACTOR_DESCRIPTIONS # noqa: E402
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from src.plugins.personality.offline_llm import LLMModel # noqa: E402
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from src.plugins.personality.who_r_u import MessageAnalyzer # noqa: E402
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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 ChatBasedPersonalityEvaluator:
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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.message_analyzer = MessageAnalyzer()
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self.llm = LLMModel()
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self.trait_scores_history = defaultdict(list) # 记录每个特质的得分历史
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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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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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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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def analyze_chat_context(self, messages: List[Dict]) -> str:
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"""
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分析一组消息的上下文，生成场景描述
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"""
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context = ""
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for msg in messages:
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nickname = msg.get('user_info', {}).get('user_nickname', '未知用户')
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content = msg.get('processed_plain_text', msg.get('detailed_plain_text', ''))
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if content:
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context += f"{nickname}: {content}\n"
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return context
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def evaluate_chat_response(
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self, user_nickname: str, chat_context: str, dimensions: List[str] = None) -> Dict[str, float]:
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"""
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评估聊天内容在各个人格维度上的得分
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"""
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# 使用所有维度进行评估
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dimensions = list(self.personality_traits.keys())
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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"""请根据以下聊天记录，评估"{user_nickname}"在大五人格模型中的维度得分（1-6分）。
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聊天记录：
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{chat_context}
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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，注意，你的评价对象是"{user_nickname}"（仅输出JSON格式）：
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{{
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"开放性": 分数,
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"严谨性": 分数,
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"外向性": 分数,
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"宜人性": 分数,
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"神经质": 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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如果你觉得某个维度没有相关信息或者无法判断，请输出0分
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请根据聊天记录的内容和语气，结合维度说明进行评分。如果维度可以评分，确保分数在1-6之间。如果没有体现，请输出0分"""
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try:
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ai_response, _ = self.llm.generate_response(prompt)
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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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return {k: max(0, 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: 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: 0 for dim in dimensions}
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def evaluate_user_personality(self, qq_id: str, num_samples: int = 10, context_length: int = 5) -> Dict:
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"""
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基于用户的聊天记录评估人格特征
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Args:
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qq_id (str): 用户QQ号
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num_samples (int): 要分析的聊天片段数量
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context_length (int): 每个聊天片段的上下文长度
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Returns:
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Dict: 评估结果
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"""
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# 获取用户的随机消息及其上下文
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chat_contexts, user_nickname = self.message_analyzer.get_user_random_contexts(
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qq_id, num_messages=num_samples, context_length=context_length)
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if not chat_contexts:
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return {"error": f"没有找到QQ号 {qq_id} 的消息记录"}
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# 初始化评分
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final_scores = defaultdict(float)
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dimension_counts = defaultdict(int)
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chat_samples = []
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# 清空历史记录
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self.trait_scores_history.clear()
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# 分析每个聊天上下文
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for chat_context in chat_contexts:
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# 评估这段聊天内容的所有维度
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scores = self.evaluate_chat_response(user_nickname, chat_context)
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# 记录样本
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chat_samples.append({
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"聊天内容": chat_context,
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"评估维度": list(self.personality_traits.keys()),
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"评分": scores
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})
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# 更新总分和历史记录
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for dimension, score in scores.items():
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if score > 0: # 只统计大于0的有效分数
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final_scores[dimension] += score
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dimension_counts[dimension] += 1
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self.trait_scores_history[dimension].append(score)
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# 计算平均分
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average_scores = {}
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for dimension in self.personality_traits:
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if dimension_counts[dimension] > 0:
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average_scores[dimension] = round(final_scores[dimension] / dimension_counts[dimension], 2)
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else:
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average_scores[dimension] = 0 # 如果没有有效分数，返回0
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# 生成趋势图
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self._generate_trend_plot(qq_id, user_nickname)
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result = {
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"用户QQ": qq_id,
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"用户昵称": user_nickname,
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"样本数量": len(chat_samples),
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"人格特征评分": average_scores,
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"维度评估次数": dict(dimension_counts),
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"详细样本": chat_samples,
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"特质得分历史": {k: v for k, v in self.trait_scores_history.items()}
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}
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# 保存结果
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os.makedirs("results", exist_ok=True)
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result_file = f"results/personality_result_{qq_id}.json"
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with open(result_file, "w", encoding="utf-8") as f:
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json.dump(result, f, ensure_ascii=False, indent=2)
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return result
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def _generate_trend_plot(self, qq_id: str, user_nickname: str):
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"""
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生成人格特质累计平均分变化趋势图
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"""
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# 查找系统中可用的中文字体
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chinese_fonts = []
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for f in fm.fontManager.ttflist:
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try:
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if '简' in f.name or 'SC' in f.name or '黑' in f.name or '宋' in f.name or '微软' in f.name:
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chinese_fonts.append(f.name)
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except Exception:
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continue
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if chinese_fonts:
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plt.rcParams['font.sans-serif'] = chinese_fonts + ['SimHei', 'Microsoft YaHei', 'Arial Unicode MS']
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else:
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# 如果没有找到中文字体，使用默认字体，并将中文昵称转换为拼音或英文
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try:
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from pypinyin import lazy_pinyin
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user_nickname = ''.join(lazy_pinyin(user_nickname))
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except ImportError:
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user_nickname = "User" # 如果无法转换为拼音，使用默认英文
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plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
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plt.figure(figsize=(12, 6))
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plt.style.use('bmh') # 使用内置的bmh样式，它有类似seaborn的美观效果
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colors = {
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"开放性": "#FF9999",
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"严谨性": "#66B2FF",
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"外向性": "#99FF99",
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"宜人性": "#FFCC99",
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"神经质": "#FF99CC"
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}
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# 计算每个维度在每个时间点的累计平均分
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cumulative_averages = {}
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for trait, scores in self.trait_scores_history.items():
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if not scores:
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continue
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averages = []
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total = 0
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valid_count = 0
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for score in scores:
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if score > 0: # 只计算大于0的有效分数
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total += score
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valid_count += 1
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if valid_count > 0:
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averages.append(total / valid_count)
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else:
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# 如果当前分数无效，使用前一个有效的平均分
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if averages:
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averages.append(averages[-1])
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else:
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continue # 跳过无效分数
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if averages: # 只有在有有效分数的情况下才添加到累计平均中
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cumulative_averages[trait] = averages
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# 绘制每个维度的累计平均分变化趋势
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for trait, averages in cumulative_averages.items():
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x = range(1, len(averages) + 1)
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plt.plot(x, averages, 'o-', label=trait, color=colors.get(trait), linewidth=2, markersize=8)
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# 添加趋势线
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z = np.polyfit(x, averages, 1)
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p = np.poly1d(z)
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plt.plot(x, p(x), '--', color=colors.get(trait), alpha=0.5)
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plt.title(f"{user_nickname} 的人格特质累计平均分变化趋势", fontsize=14, pad=20)
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plt.xlabel("评估次数", fontsize=12)
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plt.ylabel("累计平均分", fontsize=12)
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plt.grid(True, linestyle='--', alpha=0.7)
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plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
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plt.ylim(0, 7)
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plt.tight_layout()
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# 保存图表
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os.makedirs("results/plots", exist_ok=True)
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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plot_file = f"results/plots/personality_trend_{qq_id}_{timestamp}.png"
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plt.savefig(plot_file, dpi=300, bbox_inches='tight')
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plt.close()
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def analyze_user_personality(qq_id: str, num_samples: int = 10, context_length: int = 5) -> str:
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"""
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分析用户人格特征的便捷函数
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Args:
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qq_id (str): 用户QQ号
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num_samples (int): 要分析的聊天片段数量
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context_length (int): 每个聊天片段的上下文长度
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Returns:
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str: 格式化的分析结果
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"""
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evaluator = ChatBasedPersonalityEvaluator()
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result = evaluator.evaluate_user_personality(qq_id, num_samples, context_length)
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if "error" in result:
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return result["error"]
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# 格式化输出
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output = f"QQ号 {qq_id} ({result['用户昵称']}) 的人格特征分析结果：\n"
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output += "=" * 50 + "\n\n"
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output += "人格特征评分：\n"
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for trait, score in result["人格特征评分"].items():
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if score == 0:
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output += f"{trait}: 数据不足，无法判断 (评估次数: {result['维度评估次数'].get(trait, 0)})\n"
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else:
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output += f"{trait}: {score}/6 (评估次数: {result['维度评估次数'].get(trait, 0)})\n"
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# 添加变化趋势描述
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if trait in result["特质得分历史"] and len(result["特质得分历史"][trait]) > 1:
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scores = [s for s in result["特质得分历史"][trait] if s != 0] # 过滤掉无效分数
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if len(scores) > 1: # 确保有足够的有效分数计算趋势
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trend = np.polyfit(range(len(scores)), scores, 1)[0]
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if abs(trend) < 0.1:
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trend_desc = "保持稳定"
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elif trend > 0:
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trend_desc = "呈上升趋势"
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else:
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trend_desc = "呈下降趋势"
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output += f" 变化趋势: {trend_desc} (斜率: {trend:.2f})\n"
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output += f"\n分析样本数量：{result['样本数量']}\n"
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output += f"结果已保存至：results/personality_result_{qq_id}.json\n"
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output += "变化趋势图已保存至：results/plots/目录\n"
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return output
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if __name__ == "__main__":
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# 测试代码
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# test_qq = "" # 替换为要测试的QQ号
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# print(analyze_user_personality(test_qq, num_samples=30, context_length=20))
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# test_qq = ""
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# print(analyze_user_personality(test_qq, num_samples=30, context_length=20))
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test_qq = "1026294844"
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print(analyze_user_personality(test_qq, num_samples=30, context_length=30))
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