change to backtranslation only

app.py

@@ -0,0 +1,46 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+
+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")
+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")
+
+if st.button('문장 변환'):
+    if src_text == "":
+        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,
+        ) 
+        
+        backtranslated = en2ko_model.generate(
+        **tokenizer([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
+
+
+print(backtranslated)
+
+st.write(backtranslated)

requirements.txt

@@ -0,0 +1,4 @@
+transformers
+streamlit
+torch
+numpy