app.py

import gradio as gr
import spaces
import torch
from peft import PeftModel
import os


from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation.utils import GenerationConfig
# 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("=" * 50)
print("开始加载FinGPT情感分析模型...")
print("=" * 50)

model = None
tokenizer = None
device = None

try:

    tokenizer = AutoTokenizer.from_pretrained("Go4miii/DISC-FinLLM", use_fast=False, trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained("Go4miii/DISC-FinLLM", device_map="auto", torch_dtype=torch.float16, trust_remote_code=True)
    model.generation_config = GenerationConfig.from_pretrained("Go4miii/DISC-FinLLM")


except Exception as e:
    print("\n" + "=" * 50)
    print("❌ 模型加载失败！")
    print(f"错误信息: {e}")
    print("=" * 50)
    raise


@spaces.GPU
def analyze_sentiment(news_text):
    """
    分析金融新闻的情感倾向
    """
    if model is None or tokenizer is None:
        return "❌ 模型未正确加载，请检查Spaces日志。"

    try:
        # 构建prompt（按照FinGPT的格式）
        prompt = f'''Instruction: What is the sentiment of this news? Please choose an answer from {{negative/neutral/positive}}
Input: {news_text}
Answer: '''

        # 编码输入
        tokens = tokenizer(
            prompt,
            return_tensors='pt',
            padding=True,
            max_length=512,
            truncation=True
        ).to(device)

        # 生成响应
        with torch.no_grad():
            res = model.generate(
                **tokens,
                max_length=512,
                pad_token_id=tokenizer.eos_token_id
            )

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

        # 提取答案
        if "Answer: " in res_sentence:
            sentiment = res_sentence.split("Answer: ")[1].strip()
            # 清理多余的换行和空格
            sentiment = sentiment.split('\n')[0].strip()
        else:
            sentiment = res_sentence

        return sentiment

    except Exception as e:
        return f"❌ 分析出错: {str(e)}"


# 创建Gradio界面
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        # 📊 FinGPT 金融新闻情感分析
        
        基于 **FinGPT/fingpt-mt_llama3-8b_lora** 模型的金融新闻情感分析工具。
        
        输入金融新闻文本，AI将分析其情感倾向：**positive（积极）** / **neutral（中性）** / **negative（消极）**
        """
    )

    with gr.Row():
        with gr.Column():
            news_input = gr.Textbox(
                label="📰 输入金融新闻",
                placeholder="粘贴或输入金融新闻内容...",
                lines=6
            )
            analyze_btn = gr.Button("🔍 分析情感", variant="primary", size="lg")

        with gr.Column():
            sentiment_output = gr.Textbox(
                label="😊 情感分析结果",
                lines=2
            )

    gr.Examples(
        examples=[
            "FINANCING OF ASPOCOMP 'S GROWTH Aspocomp is aggressively pursuing its growth strategy by increasingly focusing on technologically more demanding HDI printed circuit boards PCBs.",
            "According to Gran, the company has no plans to move all production to Russia, although that is where the company is growing.",
            "Apple Inc. reported record quarterly revenue of $123.9 billion, up 11% year over year, and quarterly earnings per diluted share of $2.10.",
            "The Federal Reserve announced a 0.75 percentage point interest rate increase, the largest since 1994, to combat rising inflation.",
            "Tesla shares tumbled 12% after the company missed delivery expectations for the third consecutive quarter.",
            "Microsoft and OpenAI announced a multi-year partnership to develop advanced AI technologies, with Microsoft investing $10 billion.",
        ],
        inputs=news_input,
        label="📋 示例新闻（点击使用）"
    )

    # 事件处理
    analyze_btn.click(
        fn=analyze_sentiment,
        inputs=news_input,
        outputs=sentiment_output
    )

    news_input.submit(
        fn=analyze_sentiment,
        inputs=news_input,
        outputs=sentiment_output
    )
    clear.click(lambda: None, None, chatbot, queue=False)

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