feat(frontend): 增加超参数设置并优化聊天页面布局
- 在聊天页面添加了超参数输入框，包括最大生成长度、温度、Top-p 采样和重复惩罚 - 优化了聊天框的布局，使用 gr.Row() 和 gr.Column() 实现了更合理的界面结构 - 更新了 bot 函数，支持根据用户输入的超参数进行文本生成 - 修复了一些代码格式问题，提高了代码的可读性
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carry
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@ -10,91 +10,81 @@ from global_var import get_model, get_tokenizer # 假设这两个函数能正确
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def chat_page():
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with gr.Blocks() as demo:
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gr.Markdown("## 聊天")
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chatbot = gr.Chatbot(type="messages", label="聊天机器人") # 使用 messages 类型，label 可选
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msg = gr.Textbox(label="输入消息")
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clear = gr.Button("清除对话")
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# 聊天框
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gr.Markdown("## 对话")
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with gr.Row():
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with gr.Column(scale=4):
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chatbot = gr.Chatbot(type="messages", label="聊天机器人")
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msg = gr.Textbox(label="输入消息")
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with gr.Column(scale=1):
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# 新增超参数输入框
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max_new_tokens_input = gr.Textbox(label="最大生成长度", value="1024")
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temperature_input = gr.Textbox(label="温度 (Temperature)", value="0.8")
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top_p_input = gr.Textbox(label="Top-p 采样", value="0.95")
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repetition_penalty_input = gr.Textbox(label="重复惩罚", value="1.1")
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clear = gr.Button("清除对话")
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def user(user_message, history: list):
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# 清空输入框，并将用户消息添加到 history
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# 确保 history 是 list of lists 或者 list of tuples (根据 Gradio 版本和 Chatbot type)
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# 对于 type="messages"，期望的格式是 [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]
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return "", history + [{"role": "user", "content": user_message}]
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def bot(history: list):
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def bot(history: list, max_new_tokens, temperature, top_p, repetition_penalty):
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model = get_model()
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tokenizer = get_tokenizer()
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if not history: # 避免 history 为空时出错
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if not history:
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yield history
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return
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# 检查模型和分词器是否存在
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if model is None or tokenizer is None:
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history.append({"role": "assistant", "content": "错误：模型或分词器未加载。"})
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yield history
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return
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history.append({"role": "assistant", "content": "错误：模型或分词器未加载。"})
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yield history
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return
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try:
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# --- 关键改动点 ---
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# 1. 使用完整的 history (或者根据需要裁剪) 来创建输入
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# apply_chat_template 通常能处理这种 [{"role": ..., "content": ...}] 格式
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# 确保你的模型和 tokenizer 支持 chat template
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inputs = tokenizer.apply_chat_template(
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history,
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tokenize=True,
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add_generation_prompt=True, # 对很多指令调优模型是必要的
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add_generation_prompt=True,
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return_tensors="pt",
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).to(model.device) # 将输入张量移动到模型所在的设备
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).to(model.device)
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# 2. 使用 TextIteratorStreamer
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# skip_prompt=True: 不在流中包含原始输入 prompt
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# skip_special_tokens=True: 不在流中包含特殊 token (如 <eos>)
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streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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# 3. 将 model.generate 放入单独的线程中运行
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# 这样 Gradio 的主线程不会被阻塞，可以接收流式输出
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# 将超参数转换为数值类型
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generation_kwargs = dict(
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input_ids=inputs,
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streamer=streamer,
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max_new_tokens=1024,
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# temperature=1.5, # 1.5 通常太高，容易产生随机无意义内容，建议 0.7-1.0
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# min_p=0.1, # min_p 不常用，top_p 更常见
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do_sample=True, # 启用采样，让 temperature/top_p 生效
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temperature=0.8, # 稍微降低温度
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top_p=0.95, # 使用 top_p 采样
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repetition_penalty=1.1, # 轻微惩罚重复
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use_cache=False # 通常可以开启以加速生成，除非有特殊原因
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max_new_tokens=int(max_new_tokens),
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temperature=float(temperature),
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top_p=float(top_p),
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repetition_penalty=float(repetition_penalty),
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do_sample=True,
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use_cache=False
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)
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thread = Thread(target=model.generate, kwargs=generation_kwargs)
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thread.start()
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# 4. 在 history 中添加一个空的 assistant 回复占位符
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history.append({"role": "assistant", "content": ""})
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# 5. 迭代 streamer，实时更新 history 中最后一条消息的内容
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for new_text in streamer:
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if new_text: # 确保不是空字符串
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if new_text:
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history[-1]["content"] += new_text
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yield history # yield 更新后的 history 给 Gradio Chatbot
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yield history
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except Exception as e:
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# 异常处理，将错误信息显示在聊天框
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import traceback
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error_message = f"生成回复时出错：\n{traceback.format_exc()}"
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# 如果最后一条消息是助手的空消息，覆盖它；否则追加
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if history and history[-1]["role"] == "assistant" and history[-1]["content"] == "":
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import traceback
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error_message = f"生成回复时出错：\n{traceback.format_exc()}"
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if history and history[-1]["role"] == "assistant" and history[-1]["content"] == "":
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history[-1]["content"] = error_message
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else:
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else:
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history.append({"role": "assistant", "content": error_message})
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yield history # 显示错误信息
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yield history
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# .then() 中 bot 函数的输出直接连接到 chatbot 组件
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# 更新 .then() 调用，将超参数传递给 bot 函数
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msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
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bot, chatbot, chatbot
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bot, [chatbot, max_new_tokens_input, temperature_input, top_p_input, repetition_penalty_input], chatbot
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)
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# 清除按钮点击后，返回一个空列表来清空 chatbot
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clear.click(lambda: [], None, chatbot, queue=False)
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return demo
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