BERT的中文问答系统38

完善代码并在GUI中输出所有目录及文件的列表，我们可以在GUI中添加一个按钮，点击该按钮后，会在文本框中显示项目根目录下的所有文件和子目录。以下是完整的修订后代码：

完整的修订后代码

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
import requests
from bs4 import BeautifulSoup

# 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'].squeeze(),
            'human_answer': human_answer,
            'chatgpt_answer': chatgpt_answer
        }

# 获取数据加载器
def get_data_loader(file_path, tokenizer, batch_size=8, max_length=128):
    dataset = XihuaDataset(file_path, tokenizer, max_length)
    return DataLoader(dataset, batch_size=batch_size, shuffle=True)

# 模型定义
class XihuaModel(torch.nn.Module):
    def __init__(self, pretrained_model_name='F:/models/bert-base-chinese'):
        super(XihuaModel, self).__init__()
        self.bert = BertModel.from_pretrained(pretrained_model_name)
        self.classifier = torch.nn.Linear(self.bert.config.hidden_size, 1)

    def forward(self, input_ids, attention_mask):
        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
        pooled_output = outputs.pooler_output
        logits = self.classifier(pooled_output)
        return logits

# 训练函数
def train(model, data_loader, optimizer, criterion, device, progress_var=None):
    model.train()
    total_loss = 0.0
    num_batches = len(data_loader)
    for batch_idx, batch in enumerate(data_loader):
        try:
            input_ids = batch['input_ids'].to(device)
            attention_mask = batch['attention_mask'].to(device)
            human_input_ids = batch['human_input_ids'].to(device)
            human_attention_mask = batch['human_attention_mask'].to(device)
            chatgpt_input_ids = batch['chatgpt_input_ids'].to(device)
            chatgpt_attention_mask = batch['chatgpt_attention_mask'].to(device)

            optimizer.zero_grad()
            human_logits = model(human_input_ids, human_attention_mask)
            chatgpt_logits = model(chatgpt_input_ids, chatgpt_attention_mask)

            human_labels = torch.ones(human_logits.size(0), 1).to(device)
            chatgpt_labels = torch.zeros(chatgpt_logits.size(0), 1).to(device)

            loss = criterion(human_logits, human_labels) + criterion(chatgpt_logits, chatgpt_labels)
            loss.backward()
            optimizer.step()

            total_loss += loss.item()
            if progress_var:
                progress_var.set((batch_idx + 1) / num_batches * 100)
        except Exception as e:
            logging.warning(f"跳过无效批次: {e}")

    return total_loss / len(data_loader)

# 主训练函数
def main_train(retrain=False):
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    logging.info(f'使用设备: {device}')

    tokenizer = BertTokenizer.from_pretrained('F:/models/bert-base-chinese')
    model = XihuaModel(pretrained_model_name='F:/models/bert-base-chinese').to(device)

    if retrain:
        model_path = os.path.join(PROJECT_ROOT, 'models/xihua_model.pth')
        if os.path.exists(model_path):
            model.load_state_dict(torch.load(model_path, map_location=device))
            logging.info("加载现有模型")
        else:
            logging.info("没有找到现有模型，将使用预训练模型")

    optimizer = optim.Adam(model.parameters(), lr=1e-5)
    criterion = torch.nn.BCEWithLogitsLoss()

    train_data_loader = get_data_loader(os.path.join(PROJECT_ROOT, 'data/train_data.jsonl'), tokenizer, batch_size=8, max_length=128)

    num_epochs = 30
    for epoch in range(num_epochs):
        train_loss = train(model, train_data_loader, optimizer, criterion, device)
        logging.info(f'Epoch [{epoch+1}/{num_epochs}], Loss: {train_loss:.8f}')

    torch.save(model.state_dict(), os.path.join(PROJECT_ROOT, 'models/xihua_model.pth'))
    logging.info("模型训练完成并保存")

# 网络搜索函数
def search_baidu(query):
    url = f"https://www.baidu.com/s?wd={query}"
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'
    }
    response = requests.get(url, headers=headers)
    soup = BeautifulSoup(response.text, 'html.parser')
    results = soup.find_all('div', class_='c-abstract')
    if results:
        return results[0].get_text().strip()
    return "没有找到相关信息"

# GUI界面
class XihuaChatbotGUI:
    def __init__(self, root):
        self.root = root
        self.root.title("羲和聊天机器人")

        self.tokenizer = BertTokenizer.from_pretrained('F:/models/bert-base-chinese')
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.model = XihuaModel(pretrained_model_name='F:/models/bert-base-chinese').to(self.device)
        self.load_model()
        self.model.eval()

        # 加载训练数据集以便在获取答案时使用
        self.data = self.load_data(os.path.join(PROJECT_ROOT, 'data/train_data.jsonl'))

        # 历史记录
        self.history = []

        self.create_widgets()

    def create_widgets(self):
        # 设置样式
        style = ttk.Style()
        style.theme_use('clam')

        # 顶部框架
        top_frame = ttk.Frame(self.root)
        top_frame.pack(pady=10)

        self.question_label = ttk.Label(top_frame, text="问题:", font=("Arial", 12))
        self.question_label.grid(row=0, column=0, padx=10)

        self.question_entry = ttk.Entry(top_frame, width=50, font=("Arial", 12))
        self.question_entry.grid(row=0, column=1, padx=10)

        self.answer_button = ttk.Button(top_frame, text="获取回答", command=self.get_answer, style='TButton')
        self.answer_button.grid(row=0, column=2, padx=10)

        # 中部框架
        middle_frame = ttk.Frame(self.root)
        middle_frame.pack(pady=10)

        self.chat_text = tk.Text(middle_frame, height=20, width=100, font=("Arial", 12), wrap='word')
        self.chat_text.grid(row=0, column=0, padx=10, pady=10)
        self.chat_text.tag_configure("user", justify='right', foreground='blue')
        self.chat_text.tag_configure("xihua", justify='left', foreground='green')

        # 底部框架
        bottom_frame = ttk.Frame(self.root)
        bottom_frame.pack(pady=10)

        self.correct_button = ttk.Button(bottom_frame, text="准确", command=self.mark_correct, style='TButton')
        self.correct_button.grid(row=0, column=0, padx=10)

        self.incorrect_button = ttk.Button(bottom_frame, text="不准确", command=self.mark_incorrect, style='TButton')
        self.incorrect_button.grid(row=0, column=1, padx=10)

        self.train_button = ttk.Button(bottom_frame, text="训练模型", command=self.train_model, style='TButton')
        self.train_button.grid(row=0, column=2, padx=10)

        self.retrain_button = ttk.Button(bottom_frame, text="重新训练模型", command=lambda: self.train_model(retrain=True), style='TButton')
        self.retrain_button.grid(row=0, column=3, padx=10)

        self.progress_var = tk.DoubleVar()
        self.progress_bar = ttk.Progressbar(bottom_frame, variable=self.progress_var, maximum=100, length=200, mode='determinate')
        self.progress_bar.grid(row=1, column=0, columnspan=4, pady=10)

        self.log_text = tk.Text(bottom_frame, height=10, width=70, font=("Arial", 12))
        self.log_text.grid(row=2, column=0, columnspan=4, pady=10)

        self.evaluate_button = ttk.Button(bottom_frame, text="评估模型", command=self.evaluate_model, style='TButton')
        self.evaluate_button.grid(row=3, column=0, padx=10, pady=10)

        self.history_button = ttk.Button(bottom_frame, text="查看历史记录", command=self.view_history, style='TButton')
        self.history_button.grid(row=3, column=1, padx=10, pady=10)

        self.save_history_button = ttk.Button(bottom_frame, text="保存历史记录", command=self.save_history, style='TButton')
        self.save_history_button.grid(row=3, column=2, padx=10, pady=10)

        self.show_files_button = ttk.Button(bottom_frame, text="显示文件列表", command=self.show_files, style='TButton')
        self.show_files_button.grid(row=3, column=3, padx=10, pady=10)

    def get_answer(self):
        question = self.question_entry.get()
        if not question:
            messagebox.showwarning("输入错误", "请输入问题")
            return

        inputs = self.tokenizer(question, return_tensors='pt', padding='max_length', truncation=True, max_length=128)
        with torch.no_grad():
            input_ids = inputs['input_ids'].to(self.device)
            attention_mask = inputs['attention_mask'].to(self.device)
            logits = self.model(input_ids, attention_mask)
        
        if logits.item() > 0:
            answer_type = "羲和回答"
        else:
            answer_type = "零回答"

        specific_answer = self.get_specific_answer(question, answer_type)

        self.chat_text.insert(tk.END, f"用户: {question}\n", "user")
        self.chat_text.insert(tk.END, f"羲和: {specific_answer}\n", "xihua")

        # 添加到历史记录
        self.history.append({
            'question': question,
            'answer_type': answer_type,
            'specific_answer': specific_answer,
            'accuracy': None  # 初始状态为未评价
        })

    def get_specific_answer(self, question, answer_type):
        # 使用模糊匹配查找最相似的问题
        best_match = None
        best_ratio = 0.0
        for item in self.data:
            ratio = SequenceMatcher(None, question, item['question']).ratio()
            if ratio > best_ratio:
                best_ratio = ratio
                best_match = item

        if best_match:
            if answer_type == "羲和回答":
                return best_match['human_answers'][0]
            else:
                return best_match['chatgpt_answers'][0]
        return "这个我也不清楚，你问问零吧"

    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 load_model(self):
        model_path = os.path.join(PROJECT_ROOT, 'models/xihua_model.pth')
        if os.path.exists(model_path):
            self.model.load_state_dict(torch.load(model_path, map_location=self.device))
            logging.info("加载现有模型")
        else:
            logging.info("没有找到现有模型，将使用预训练模型")

    def train_model(self, retrain=False):
        file_path = filedialog.askopenfilename(filetypes=[("JSONL files", "*.jsonl"), ("JSON files", "*.json")])
        if not file_path:
            messagebox.showwarning("文件选择错误", "请选择一个有效的数据文件")
            return

        try:
            dataset = XihuaDataset(file_path, self.tokenizer)
            data_loader = DataLoader(dataset, batch_size=8, shuffle=True)
            
            # 加载已训练的模型权重
            if retrain:
                self.model.load_state_dict(torch.load(os.path.join(PROJECT_ROOT, 'models/xihua_model.pth'), map_location=self.device))
                self.model.to(self.device)
                self.model.train()

            optimizer = torch.optim.Adam(self.model.parameters(), lr=1e-5)
            criterion = torch.nn.BCEWithLogitsLoss()
            num_epochs = 30
            for epoch in range(num_epochs):
                train_loss = train(self.model, data_loader, optimizer, criterion, self.device, self.progress_var)
                logging.info(f'Epoch [{epoch+1}/{num_epochs}], Loss: {train_loss:.4f}')
                self.log_text.insert(tk.END, f'Epoch [{epoch+1}/{num_epochs}], Loss: {train_loss:.4f}\n')
                self.log_text.see(tk.END)
            torch.save(self.model.state_dict(), os.path.join(PROJECT_ROOT, 'models/xihua_model.pth'))
            logging.info("模型训练完成并保存")
            self.log_text.insert(tk.END, "模型训练完成并保存\n")
            self.log_text.see(tk.END)
            messagebox.showinfo("训练完成", "模型训练完成并保存")
        except Exception as e:
            logging.error(f"模型训练失败: {e}")
            self.log_text.insert(tk.END, f"模型训练失败: {e}\n")
            self.log_text.see(tk.END)
            messagebox.showerror("训练失败", f"模型训练失败: {e}")

    def evaluate_model(self):
        # 这里可以添加模型评估的逻辑
        messagebox.showinfo("评估结果", "模型评估功能暂未实现")

    def mark_correct(self):
        if self.history:
            self.history[-1]['accuracy'] = True
            messagebox.showinfo("评价成功", "您认为这次回答是准确的")

    def mark_incorrect(self):
        if self.history:
            self.history[-1]['accuracy'] = False
            question = self.history[-1]['question']
            baike_answer = self.search_baidu_baike(question)
            self.chat_text.insert(tk.END, f"百度百科结果: {baike_answer}\n", "xihua")
            messagebox.showinfo("评价成功", "您认为这次回答是不准确的")

    def search_baidu_baike(self, query):
        url = f"https://baike.baidu.com/item/{query}"
        headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'
        }
        response = requests.get(url, headers=headers)
        soup = BeautifulSoup(response.text, 'html.parser')
        summary = soup.find('div', class_='lemma-summary')
        if summary:
            return summary.get_text().strip()
        return "没有找到相关信息"

    def view_history(self):
        history_window = tk.Toplevel(self.root)
        history_window.title("历史记录")

        history_text = tk.Text(history_window, height=20, width=80, font=("Arial", 12))
        history_text.pack(padx=10, pady=10)

        for entry in self.history:
            history_text.insert(tk.END, f"问题: {entry['question']}\n")
            history_text.insert(tk.END, f"回答类型: {entry['answer_type']}\n")
            history_text.insert(tk.END, f"具体回答: {entry['specific_answer']}\n")
            if entry['accuracy'] is None:
                history_text.insert(tk.END, "评价: 未评价\n")
            elif entry['accuracy']:
                history_text.insert(tk.END, "评价: 准确\n")
            else:
                history_text.insert(tk.END, "评价: 不准确\n")
            history_text.insert(tk.END, "-" * 50 + "\n")

    def save_history(self):
        file_path = filedialog.asksaveasfilename(defaultextension=".json", filetypes=[("JSON files", "*.json")])
        if not file_path:
            return

        with open(file_path, 'w') as f:
            json.dump(self.history, f, ensure_ascii=False, indent=4)

        messagebox.showinfo("保存成功", "历史记录已保存到文件")

    def show_files(self):
        files_window = tk.Toplevel(self.root)
        files_window.title("文件列表")

        files_text = tk.Text(files_window, height=20, width=80, font=("Arial", 12))
        files_text.pack(padx=10, pady=10)

        for root, dirs, files in os.walk(PROJECT_ROOT):
            for dir in dirs:
                files_text.insert(tk.END, f"目录: {os.path.join(root, dir)}\n")
            for file in files:
                files_text.insert(tk.END, f"文件: {os.path.join(root, file)}\n")

# 主函数
if __name__ == "__main__":
    # 启动GUI
    root = tk.Tk()
    app = XihuaChatbotGUI(root)
    root.mainloop()

主要修改点
新增 show_files 方法：这个方法会遍历项目根目录及其子目录，列出所有文件和目录，并将结果显示在新的弹出窗口中。
新增按钮：在底部框架中添加了一个新的按钮 show_files_button，点击该按钮会调用 show_files 方法。
这样，用户可以通过点击“显示文件列表”按钮来查看项目根目录及其子目录下的所有文件和目录。希望这能满足你的需求。