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requirements.txt

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+opencc-python-reimplemented
+streamlit
+transformers
+sentencepiece
+torch
+torchvision
+torchaudio

summary.py

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+import streamlit as st
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import opencc
+
+#local_path = "./LLM"
+# 使用中文摘要模型
+local_path = 'utrobinmv/t5_summary_en_ru_zh_base_2048'
+model = T5ForConditionalGeneration.from_pretrained(local_path)
+tokenizer = T5Tokenizer.from_pretrained(local_path)
+
+
+
+# Streamlit UI
+st.title("中文文章摘要工具")
+# Create an OpenCC converter for converting simplified Chinese to traditional Chinese
+converter = opencc.OpenCC('s2t')
+# Input text area for the article
+article = st.text_area("請輸入文章", "")
+
+# Function to generate summary
+@st.cache_data
+def generate_summary(article):
+    inputs = tokenizer.encode("摘要：" + article, return_tensors="pt", max_length=1024, truncation=True)
+    summary_ids = model.generate(inputs, max_length=180, min_length=60, length_penalty=2.0, num_beams=4, early_stopping=True)
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
+# Button to generate summary
+if st.button("生成摘要"):
+    if article.strip() == "":
+        st.error("請輸入文章。")
+    else:
+        summary = generate_summary(article)
+        traditional_summary = converter.convert(summary)
+        st.subheader("摘要:")
+        st.write(traditional_summary)