initial value hotfix

app.py

@@ -4,7 +4,9 @@ import streamlit as st
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 
-st.set_page_config(page_title='KoQuillBot', layout='wide', initial_sidebar_state='expanded')
+st.set_page_config(
+    page_title="KoQuillBot", layout="wide", initial_sidebar_state="expanded"
+)
 
 tokenizer = AutoTokenizer.from_pretrained("QuoQA-NLP/KE-T5-Ko2En-Base")
 ko2en_model = AutoModelForSeq2SeqLM.from_pretrained("QuoQA-NLP/KE-T5-Ko2En-Base")
@@ -13,31 +15,50 @@ en2ko_model = AutoModelForSeq2SeqLM.from_pretrained("QuoQA-NLP/KE-T5-En2Ko-Base"
 
 st.title("🤖 KoQuillBot")
 
-src_text = st.text_area("바꾸고 싶은 문장을 입력하세요:",height=None,max_chars=None,key=None,help="Enter your text here")
 
-backtranslated = ""
+default_value = "안녕하세요. 저는 문장을 다시 작성해주는 KoQuillBot입니다."
+src_text = st.text_area(
+    "바꾸고 싶은 문장을 입력하세요:",
+    default_value,
+    height=50,
+    max_chars=200,
+)
 
-if st.button('문장 변환'):
+
+if st.button("문장 변환"):
     if src_text == "":
-        st.warning('Please **enter text** for translation')
+        st.warning("Please **enter text** for translation")
 
     else:
         translated = ko2en_model.generate(
-        **tokenizer([src_text], return_tensors="pt", padding=True, max_length=64,),
-        max_length=64,
-        num_beams=5,
-        repetition_penalty=1.3,
-        no_repeat_ngram_size=3,
-        num_return_sequences=1,
-        ) 
-        list_translated = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
+            **tokenizer(
+                [src_text],
+                return_tensors="pt",
+                padding=True,
+                max_length=64,
+            ),
+            max_length=64,
+            num_beams=5,
+            repetition_penalty=1.3,
+            no_repeat_ngram_size=3,
+            num_return_sequences=1,
+        )
+
+        list_translated = [
+            tokenizer.decode(t, skip_special_tokens=True) for t in translated
+        ]
         backtranslated = en2ko_model.generate(
-        **tokenizer(list_translated, return_tensors="pt", padding=True, max_length=64,),
-        max_length=64,
-        num_beams=5,
-        repetition_penalty=1.3,
-        no_repeat_ngram_size=3,
-        num_return_sequences=1,
+            **tokenizer(
+                list_translated,
+                return_tensors="pt",
+                padding=True,
+                max_length=64,
+            ),
+            max_length=64,
+            num_beams=5,
+            repetition_penalty=1.3,
+            no_repeat_ngram_size=3,
+            num_return_sequences=1,
         )
 else:
     pass