app.py

import gradio as gr
import spaces
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import os

# 加载模型和tokenizer
model_name = "meta-llama/Meta-Llama-3-8B"
adapter_name = "FinGPT/fingpt-mt_llama3-8b_lora"

# 获取HF token（Spaces会自动提供）
hf_token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGING_FACE_HUB_TOKEN")

print("正在加载模型...")
try:
    tokenizer = AutoTokenizer.from_pretrained(
        model_name,
        trust_remote_code=True,
        token=hf_token
    )
    tokenizer.pad_token = tokenizer.eos_token

    base_model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype=torch.float16,
        device_map="auto",
        trust_remote_code=True,
        token=hf_token
    )

    model = PeftModel.from_pretrained(base_model, adapter_name)
    model = model.eval()

    print("模型加载完成！")
except Exception as e:
    print(f"模型加载错误: {e}")
    print("请确保在Spaces设置中添加了HF_TOKEN")
    raise


@spaces.GPU
def chat(message, history):
    """
    聊天函数，处理用户消息并返回模型响应
    """
    # 构建对话历史
    conversation = []
    for user_msg, bot_msg in history:
        conversation.append(f"User: {user_msg}")
        conversation.append(f"Assistant: {bot_msg}")

    conversation.append(f"User: {message}")
    conversation.append("Assistant:")

    prompt = "\n".join(conversation)

    # 编码输入
    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=2048)
    inputs = {k: v.to(model.device) for k, v in inputs.items()}

    # 生成响应
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=512,
            temperature=0.7,
            top_p=0.9,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id
        )

    # 解码输出
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)

    # 提取助手的回复
    if "Assistant:" in response:
        response = response.split("Assistant:")[-1].strip()

    return response


# 创建Gradio Chatbot界面
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        # 🤖 FinGPT Chatbot
        
        这是一个基于 **FinGPT/fingpt-mt_llama3-8b_lora** 模型的金融对话助手。
        
        您可以询问关于金融市场、投资、经济分析等问题。
        """
    )

    chatbot = gr.Chatbot(
        label="聊天记录",
        height=500,
        bubble_full_width=False
    )

    with gr.Row():
        msg = gr.Textbox(
            label="输入您的消息",
            placeholder="在这里输入您的问题...",
            scale=4
        )
        submit = gr.Button("发送", scale=1, variant="primary")

    clear = gr.Button("清空对话历史")

    gr.Examples(
        examples=[
            "什么是量化宽松政策？",
            "如何评估一只股票的价值？",
            "请解释一下技术分析中的MACD指标",
            "当前市场环境下应该如何配置资产？"
        ],
        inputs=msg
    )

    # 事件处理
    def user_message(user_msg, history):
        return "", history + [[user_msg, None]]

    def bot_message(history):
        user_msg = history[-1][0]
        bot_response = chat(user_msg, history[:-1])
        history[-1][1] = bot_response
        return history

    msg.submit(user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot_message, chatbot, chatbot
    )
    submit.click(user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot_message, chatbot, chatbot
    )
    clear.click(lambda: None, None, chatbot, queue=False)

if __name__ == "__main__":
    demo.launch()