app.py

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

# 加载模型和分词器（适配 Qwen1.5-1.8B-Chat 并保留优化配置）
model_name = "Qwen/Qwen1.5-1.8B-Chat"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,  # 半精度减少内存占用
    device_map="auto",
    load_in_4bit=True,  # 4-bit 量化降低内存压力
    bnb_4bit_compute_dtype=torch.float16
)

# 优化后的聊天函数（适配 Qwen 的对话模板）
def chat_with_model(message, history):
    # 只保留最近 3 轮历史，减少计算量
    history = history[-3:]
    messages = []
    # 拼接历史对话
    for user_msg, bot_msg in history:
        messages.append({"role": "user", "content": user_msg})
        messages.append({"role": "assistant", "content": bot_msg})
    # 加入当前用户消息
    messages.append({"role": "user", "content": message})
    
    # 生成模型输入
    inputs = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        tokenize=True,
        return_dict=True,
        return_tensors="pt",
    ).to(model.device)
    
    # 推理生成回复
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=150,
            temperature=0.7,
            repetition_penalty=1.1,
            do_sample=True
        )
    # 解码并提取回复
    bot_response = tokenizer.decode(
        outputs[0][inputs["input_ids"].shape[-1]:],
        skip_special_tokens=True
    ).strip()
    return bot_response

# 启动 Gradio 界面（保留资源优化配置）
if __name__ == "__main__":
    gr.ChatInterface(
        fn=chat_with_model,
        title="轻量聊天助手",
        description="基于 Qwen1.5-1.8B-Chat 适配 2 核 16G 配置"
    ).launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        inline=False
    )