Delete app.py

app.py

@@ -1,42 +0,0 @@
-import torch
-import streamlit as st
-from transformers.models.bart import BartForConditionalGeneration
-from transformers import PreTrainedTokenizerFast
-
-#@st.cache
-@st.cache_data(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)
-#'''