优化代码格式和异常处理
- 修复异常处理链，使用from语法保留原始异常 - 格式化代码以符合项目规范 - 优化导入模块的顺序 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
春河晴
@@ -1,7 +1,6 @@
|
||||
from typing import Dict, List
|
||||
import json
|
||||
import os
|
||||
import random
|
||||
from pathlib import Path
|
||||
from dotenv import load_dotenv
|
||||
import sys
|
||||
@@ -15,7 +14,7 @@ env_path = project_root / ".env.prod"
|
||||
root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
|
||||
sys.path.append(root_path)
|
||||
|
||||
from src.plugins.personality.offline_llm import LLMModel
|
||||
from src.plugins.personality.offline_llm import LLMModel # noqa E402
|
||||
|
||||
# 加载环境变量
|
||||
if env_path.exists():
|
||||
@@ -28,37 +27,22 @@ else:
|
||||
|
||||
class PersonalityEvaluator:
|
||||
def __init__(self):
|
||||
self.personality_traits = {
|
||||
"开放性": 0,
|
||||
"尽责性": 0,
|
||||
"外向性": 0,
|
||||
"宜人性": 0,
|
||||
"神经质": 0
|
||||
}
|
||||
self.personality_traits = {"开放性": 0, "尽责性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
|
||||
self.scenarios = [
|
||||
{
|
||||
"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。",
|
||||
"评估维度": ["尽责性", "宜人性"]
|
||||
},
|
||||
{
|
||||
"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。",
|
||||
"评估维度": ["外向性", "神经质"]
|
||||
"评估维度": ["尽责性", "宜人性"],
|
||||
},
|
||||
{"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。", "评估维度": ["外向性", "神经质"]},
|
||||
{
|
||||
"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。",
|
||||
"评估维度": ["开放性", "外向性"]
|
||||
"评估维度": ["开放性", "外向性"],
|
||||
},
|
||||
{
|
||||
"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。",
|
||||
"评估维度": ["开放性", "尽责性"]
|
||||
},
|
||||
{
|
||||
"场景": "你的朋友因为个人原因情绪低落，向你寻求帮助。",
|
||||
"评估维度": ["宜人性", "神经质"]
|
||||
}
|
||||
{"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。", "评估维度": ["开放性", "尽责性"]},
|
||||
{"场景": "你的朋友因为个人原因情绪低落，向你寻求帮助。", "评估维度": ["宜人性", "神经质"]},
|
||||
]
|
||||
self.llm = LLMModel()
|
||||
|
||||
|
||||
def evaluate_response(self, scenario: str, response: str, dimensions: List[str]) -> Dict[str, float]:
|
||||
"""
|
||||
使用 DeepSeek AI 评估用户对特定场景的反应
|
||||
@@ -67,7 +51,7 @@ class PersonalityEvaluator:
|
||||
场景：{scenario}
|
||||
用户描述：{response}
|
||||
|
||||
需要评估的维度：{', '.join(dimensions)}
|
||||
需要评估的维度：{", ".join(dimensions)}
|
||||
|
||||
请按照以下格式输出评估结果（仅输出JSON格式）：
|
||||
{{
|
||||
@@ -87,8 +71,8 @@ class PersonalityEvaluator:
|
||||
try:
|
||||
ai_response, _ = self.llm.generate_response(prompt)
|
||||
# 尝试从AI响应中提取JSON部分
|
||||
start_idx = ai_response.find('{')
|
||||
end_idx = ai_response.rfind('}') + 1
|
||||
start_idx = ai_response.find("{")
|
||||
end_idx = ai_response.rfind("}") + 1
|
||||
if start_idx != -1 and end_idx != 0:
|
||||
json_str = ai_response[start_idx:end_idx]
|
||||
scores = json.loads(json_str)
|
||||
@@ -101,75 +85,68 @@ class PersonalityEvaluator:
|
||||
print(f"评估过程出错：{str(e)}")
|
||||
return {dim: 5.0 for dim in dimensions}
|
||||
|
||||
|
||||
def main():
|
||||
print("欢迎使用人格形象创建程序！")
|
||||
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。")
|
||||
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。")
|
||||
print("\n准备好了吗？按回车键开始...")
|
||||
input()
|
||||
|
||||
|
||||
evaluator = PersonalityEvaluator()
|
||||
final_scores = {
|
||||
"开放性": 0,
|
||||
"尽责性": 0,
|
||||
"外向性": 0,
|
||||
"宜人性": 0,
|
||||
"神经质": 0
|
||||
}
|
||||
final_scores = {"开放性": 0, "尽责性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
|
||||
dimension_counts = {trait: 0 for trait in final_scores.keys()}
|
||||
|
||||
|
||||
for i, scenario_data in enumerate(evaluator.scenarios, 1):
|
||||
print(f"\n场景 {i}/{len(evaluator.scenarios)}:")
|
||||
print("-" * 50)
|
||||
print(scenario_data["场景"])
|
||||
print("\n请描述您的角色在这种情况下会如何反应：")
|
||||
response = input().strip()
|
||||
|
||||
|
||||
if not response:
|
||||
print("反应描述不能为空！")
|
||||
continue
|
||||
|
||||
|
||||
print("\n正在评估您的描述...")
|
||||
scores = evaluator.evaluate_response(scenario_data["场景"], response, scenario_data["评估维度"])
|
||||
|
||||
|
||||
# 更新最终分数
|
||||
for dimension, score in scores.items():
|
||||
final_scores[dimension] += score
|
||||
dimension_counts[dimension] += 1
|
||||
|
||||
|
||||
print("\n当前评估结果：")
|
||||
print("-" * 30)
|
||||
for dimension, score in scores.items():
|
||||
print(f"{dimension}: {score}/10")
|
||||
|
||||
|
||||
if i < len(evaluator.scenarios):
|
||||
print("\n按回车键继续下一个场景...")
|
||||
input()
|
||||
|
||||
|
||||
# 计算平均分
|
||||
for dimension in final_scores:
|
||||
if dimension_counts[dimension] > 0:
|
||||
final_scores[dimension] = round(final_scores[dimension] / dimension_counts[dimension], 2)
|
||||
|
||||
|
||||
print("\n最终人格特征评估结果：")
|
||||
print("-" * 30)
|
||||
for trait, score in final_scores.items():
|
||||
print(f"{trait}: {score}/10")
|
||||
|
||||
|
||||
# 保存结果
|
||||
result = {
|
||||
"final_scores": final_scores,
|
||||
"scenarios": evaluator.scenarios
|
||||
}
|
||||
|
||||
result = {"final_scores": final_scores, "scenarios": evaluator.scenarios}
|
||||
|
||||
# 确保目录存在
|
||||
os.makedirs("results", exist_ok=True)
|
||||
|
||||
|
||||
# 保存到文件
|
||||
with open("results/personality_result.json", "w", encoding="utf-8") as f:
|
||||
json.dump(result, f, ensure_ascii=False, indent=2)
|
||||
|
||||
|
||||
print("\n结果已保存到 results/personality_result.json")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
Reference in New Issue
Block a user