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app.py

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+import torch
+import streamlit as st
+from transformers.models.bart import BartForConditionalGeneration
+from transformers import PreTrainedTokenizerFast
+
+#@st.cache
+@st.cache(allow_output_mutation=True)
+def load_model():
+    #model = BartForConditionalGeneration.from_pretrained('logs/model_chp/epoch-6')
+    model = BartForConditionalGeneration.from_pretrained('LeeJang/news-summarization-v2')
+    # tokenizer = get_kobart_tokenizer()
+    return model
+
+model = load_model()
+tokenizer = PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-base-v1')
+st.title("2문장 뉴스 요약기")
+text = st.text_area("뉴스 입력:")
+
+st.markdown("## 뉴스 원문")
+st.write(text)
+
+#'''
+if text:
+    text = text.replace('\n', ' ')
+    text = text.strip()
+    
+    arr = text.split(' ')
+
+    if len(arr) > 501:
+        #print('!!!')
+        arr = arr[:501]
+        text = ' '.join(arr)
+    
+    st.markdown("## 요약 결과")
+    with st.spinner('processing..'):
+        input_ids = tokenizer.encode(text)
+        input_ids = torch.tensor(input_ids)
+        input_ids = input_ids.unsqueeze(0)
+        output = model.generate(input_ids, eos_token_id=1, max_length=512, num_beams=5)
+        output = tokenizer.decode(output[0], skip_special_tokens=True)
+    st.write(output)
+#'''

requirements.txt

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+torch
+transformers
+streamlit