app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# -------------------------------
# 模型加载
# -------------------------------
MODEL_ID = "caobin/llm-caobin"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    device_map="auto",  # CPU 上自动映射到 CPU
    trust_remote_code=True
)

# -------------------------------
# 工具函数：清理历史
# -------------------------------
def clean_history(history):
    """
    将历史消息的 content 转为字符串，避免 list 导致空回答
    """
    cleaned = []
    for msg in history:
        content = msg['content']
        if isinstance(content, list):
            # list -> str
            content = " ".join([str(c) for c in content])
        cleaned.append({"role": msg['role'], "content": content})
    return cleaned

# -------------------------------
# 聊天函数
# -------------------------------
def chat_fn(message, history):
    history = clean_history(history)
    recent_history = history[-6:]  # 保留最近 3 轮对话
    full_prompt = ""
    
    for msg in recent_history:
        if msg["role"] == "user":
            full_prompt += f"<|user|>{msg['content']}<|assistant|>"
        elif msg["role"] == "assistant":
            full_prompt += msg['content']
    
    # 当前用户问题
    full_prompt += f"<|user|>{message}<|assistant|>"

    # tokenizer -> tensor
    inputs = tokenizer(full_prompt, return_tensors="pt").to(model.device)

    # 生成回答
    output_ids = model.generate(
        **inputs,
        max_new_tokens=128,
        temperature=0.3,
        top_p=0.3,
        do_sample=True,
    )

    output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    if "<|assistant|>" in output_text:
        output_text = output_text.split("<|assistant|>")[-1]
    return output_text.strip()

# -------------------------------
# Gradio UI
# -------------------------------
with gr.Blocks(title="caobin LLM Chatbot") as demo:
    gr.Markdown("# 🤖 caobin's AI assistant")
    chatbot = gr.Chatbot(height=450)
    msg = gr.Textbox(label="输入你的问题")

    def respond(message, chat_history):
        response = chat_fn(message, chat_history)
        # 用字典格式添加消息
        chat_history.append({"role": "user", "content": message})
        chat_history.append({"role": "assistant", "content": response})
        return "", chat_history

    msg.submit(respond, [msg, chatbot], [msg, chatbot])

# -------------------------------
# 启动
# -------------------------------
demo.launch()