Create app.py

app.py

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+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()