import gradio as gr
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM

# 加载中文摘要模型
summarizer = pipeline("summarization", model="IDEA-CCNL/Randeng-Pegasus-523M-Summary-Chinese")

# 加载中文情感分析模型
sentiment_analyzer = pipeline("sentiment-analysis", model="uer/roberta-base-finetuned-jd-binary-chinese")

def analyze_text(text):
    # 生成摘要
    summary = summarizer(text, max_length=150, min_length=50, do_sample=False)[0]['summary_text']
    
    # 分析情感
    sentiment = sentiment_analyzer(text)[0]
    sentiment_label = "积极" if sentiment['label'] == 'positive' else "消极"
    sentiment_score = sentiment['score']
    
    # 格式化输出
    result = f"摘要：\n{summary}\n\n情感倾向：{sentiment_label}\n情感得分：{sentiment_score:.2f}"
    
    return result

# 创建 Gradio 接口
iface = gr.Interface(
    fn=analyze_text,
    inputs=gr.Textbox(lines=10, label="输入中文文章"),
    outputs=gr.Textbox(label="分析结果"),
    title="中文文章摘要与情感分析",
    description="输入中文文章，获取摘要和情感分析结果。"
)

# 启动接口
iface.launch()