BERT的中文问答系统30

为了完善代码并实现评估功能，我们对现有的代码进行一些调整和扩展。以下是具体的改进：
评估功能：添加评估模型的功能，计算模型在测试集上的准确率。
GUI改进：优化GUI界面，使其更加用户友好。
日志记录：增强日志记录，确保每个步骤都有详细的记录。

1. 评估功能
   首先，我们需要添加一个评估函数，该函数将计算模型在测试集上的准确率。

# 评估函数
def evaluate(model, data_loader, device):
    model.eval()
    correct_predictions = 0
    total_predictions = 0

    with torch.no_grad():
        for batch in data_loader:
            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)

            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)

            human_preds = (torch.sigmoid(human_logits) > 0.5).float()
            chatgpt_preds = (torch.sigmoid(chatgpt_logits) > 0.5).float()

            correct_predictions += (human_preds == human_labels).sum().item()
            correct_predictions += (chatgpt_preds == chatgpt_labels).sum().item()
            total_predictions += human_labels.size(0) + chatgpt_labels.size(0)

    accuracy = correct_predictions / total_predictions
    return accuracy

2. GUI改进
   在GUI中添加一个按钮来启动评估功能，并显示评估结果。

class XihuaChatbotGUI:
    # ... 其他方法保持不变 ...

    def create_widgets(self):
        # ... 其他组件保持不变 ...

        self.evaluate_button = tk.Button(bottom_frame, text="评估模型", command=self.evaluate_model, font=("Arial", 12))
        self.evaluate_button.grid(row=3, column=0, padx=10, pady=10)

        self.history_button = tk.Button(bottom_frame, text="查看历史记录", command=self.view_history, font=("Arial", 12))
        self.history_button.grid(row=3, column=1, padx=10, pady=10)

        self.save_history_button = tk.Button(bottom_frame, text="保存历史记录", command=self.save_history, font=("Arial", 12))
        self.save_history_button.grid(row=3, column=2, padx=10, pady=10)

    def evaluate_model(self):
        test_data_loader = get_data_loader(os.path.join(PROJECT_ROOT, 'data/test_data.jsonl'), self.tokenizer, batch_size=8, max_length=128)
        accuracy = evaluate(self.model, test_data_loader, self.device)
        logging.info(f"模型评估准确率: {accuracy:.4f}")
        self.log_text.insert(tk.END, f"模型评估准确率: {accuracy:.4f}\n")
        self.log_text.see(tk.END)
        messagebox.showinfo("评估结果", f"模型评估准确率: {accuracy:.4f}")

3. 日志记录
   确保每个步骤都有详细的日志记录，以便于调试和跟踪。

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()
        ]
    )

完整代码
以下是完整的代码，包括评估功能和改进的GUI：

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'].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)

# 评估函数
def evaluate(model, data_loader, device):
    model.eval()
    correct_predictions = 0
    total_predictions = 0

    with torch.no_grad():
        for batch in data_loader:
            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)

            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)

            human_preds = (torch.sigmoid(human_logits) > 0.5).float()
            chatgpt_preds = (torch.sigmoid(chatgpt_logits) > 0.5).float()

            correct_predictions += (human_preds == human_labels).sum().item()
            correct_predictions += (chatgpt_preds == chatgpt_labels).sum().item()
            total_predictions += human_labels.size(0) + chatgpt_labels.size(0)

    accuracy = correct_predictions / total_predictions
    return accuracy

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

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

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

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

        self.answer_button = tk.Button(top_frame, text="获取回答", command=self.get_answer, font=("Arial", 12))
        self.answer_button.grid(row=0, column=2, padx=10)

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

        self.answer_label = tk.Label(middle_frame, text="回答:", font=("Arial", 12))
        self.answer_label.grid(row=0, column=0, padx=10)

        self.answer_text = tk.Text(middle_frame, height=10, width=70, font=("Arial", 12))
        self.answer_text.grid(row=1, column=0, padx=10)

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

        self.correct_button = tk.Button(bottom_frame, text="准确", command=self.mark_correct, font=("Arial", 12))
        self.correct_button.grid(row=0, column=0, padx=10)

        self.incorrect_button = tk.Button(bottom_frame, text="不准确", command=self.mark_incorrect, font=("Arial", 12))
        self.incorrect_button.grid(row=0, column=1, padx=10)

        self.train_button = tk.Button(bottom_frame, text="训练模型", command=self.train_model, font=("Arial", 12))
        self.train_button.grid(row=0, column=2, padx=10)

        self.retrain_button = tk.Button(bottom_frame, text="重新训练模型", command=lambda: self.train_model(retrain=True), font=("Arial", 12))
        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)
        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 = tk.Button(bottom_frame, text="评估模型", command=self.evaluate_model, font=("Arial", 12))
        self.evaluate_button.grid(row=3, column=0, padx=10, pady=10)

        self.history_button = tk.Button(bottom_frame, text="查看历史记录", command=self.view_history, font=("Arial", 12))
        self.history_button.grid(row=3, column=1, padx=10, pady=10)

        self.save_history_button = tk.Button(bottom_frame, text="保存历史记录", command=self.save_history, font=("Arial", 12))
        self.save_history_button.grid(row=3, column=2, 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.answer_text.delete(1.0, tk.END)
        self.answer_text.insert(tk.END, f"{answer_type}\n{specific_answer}")

        # 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):
        test_data_loader = get_data_loader(os.path.join(PROJECT_ROOT, 'data/test_data.jsonl'), self.tokenizer, batch_size=8, max_length=128)
        accuracy = evaluate(self.model, test_data_loader, self.device)
        logging.info(f"模型评估准确率: {accuracy:.4f}")
        self.log_text.insert(tk.END, f"模型评估准确率: {accuracy:.4f}\n")
        self.log_text.see(tk.END)
        messagebox.showinfo("评估结果", f"模型评估准确率: {accuracy:.4f}")

    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
            messagebox.showinfo("评价成功", "您认为这次回答是不准确的")

    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")

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

这样，我们就完成了评估功能的添加和GUI的改进。现在，用户可以通过点击“评估模型”按钮来评估模型的性能，并在GUI中看到评估结果。