Create app.py

app.py

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+import torch
+import streamlit as st
+from kobart import get_kobart_tokenizer
+from transformers.models.bart import BartForConditionalGeneration
+
+@st.cache
+def load_model():
+    model = BartForConditionalGeneration.from_pretrained("gogamza/kobart-base-v1")
+    # tokenizer = get_kobart_tokenizer()
+    return model
+
+model = load_model()
+tokenizer = get_kobart_tokenizer()
+st.title("KoBART 요약 Test")
+text = st.text_area("뉴스 입력:")
+
+st.markdown("## 뉴스 원문")
+st.write(text)
+
+if text:
+    text = text.replace('\n', '')
+    st.markdown("## KoBART 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)