为了完善代码并增加新的功能，我们将在现有的基础上进行以下改进：

历史记录查询和保存：增加历史记录的存储和查询功能，用户可以查看之前的对话记录。
评价功能：允许用户对羲和的回答进行准确或不准确的评价，并根据评价调整模型。
优化GUI：使GUI更加人性化，增加按钮和文本框，方便用户操作。
以下是改进后的代码：

python

import os
import json
import jsonlines
import torch
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from transformers import BertModel, BertTokenizer
import tkinter as tk
from tkinter import filedialog, messagebox, ttk
import logging
from difflib import SequenceMatcher
from datetime import datetime

# 获取项目根目录
PROJECT_ROOT = os.path.dirname(os.path.abspath(__file__))

# 配置日志
LOGS_DIR = os.path.join(PROJECT_ROOT, 'logs')
os.makedirs(LOGS_DIR, exist_ok=True)

def setup_logging():
    log_file = os.path.join(LOGS_DIR, datetime.now().strftime('%Y-%m-%d_%H-%M-%S_羲和.txt'))
    logging.basicConfig(
        level=logging.INFO,
        format='%(asctime)s - %(levelname)s - %(message)s',
        handlers=[
            logging.FileHandler(log_file),
            logging.StreamHandler()
        ]
    )

setup_logging()

# 数据集类
class XihuaDataset(Dataset):
    def __init__(self, file_path, tokenizer, max_length=128):
        self.tokenizer = tokenizer
        self.max_length = max_length
        self.data = self.load_data(file_path)

    def load_data(self, file_path):
        data = []
        if file_path.endswith('.jsonl'):
            with jsonlines.open(file_path) as reader:
                for i, item in enumerate(reader):
                    try:
                        data.append(item)
                    except jsonlines.jsonlines.InvalidLineError as e:
                        logging.warning(f"跳过无效行 {
     i + 1}: {
     e}")
        elif file_path.endswith('.json'):
            with open(file_path, 'r') as f:
                try:
                    data = json.load(f)
                except json.JSONDecodeError as e:
                    logging.warning(f"跳过无效文件 {
     file_path}: {
     e}")
        return data

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        item = self.data[idx]
        question = item['question']
        human_answer = item['human_answers'][0]
        chatgpt_answer = item['chatgpt_answers'][0]

        try:
            inputs = self.tokenizer(question, return_tensors='pt', padding='max_length', truncation=True, max_length=self.max_length)
            human_inputs = self.tokenizer(human_answer, return_tensors='pt', padding='max_length', truncation=True, max_length=self.max_length)
            chatgpt_inputs = self.tokenizer(chatgpt_answer, return_tensors='pt', padding='max_length', truncation=True, max_length=self.max_length)
        except Exception as e:
            logging.warning(f"跳过无效项 {
     idx}: {
     e}")
            return self.__getitem__((idx + 1) % len(self.data))

        return {
   
            'input_ids': inputs['input_ids'].squeeze(),
            'attention_mask': inputs['attention_mask'].squeeze(),
            'human_input_ids': human_inputs['input_ids'].squeeze(),
            'human_attention_mask': human_inputs['attention_mask'].squeeze(),
            'chatgpt_input_ids': chatgpt_inputs['input_ids'].squeeze(),
            'chatgpt_attention_mask': chatgpt_inputs['attention_mask'].</