initial commit

app.py

@@ -0,0 +1,63 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+
+st.set_page_config(
+    page_title="", layout="wide", initial_sidebar_state="expanded"
+)
+
+@st.cache
+def load_model(model_name):
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    return model
+
+tokenizer = AutoTokenizer.from_pretrained("snoop2head/KoBrailleT5-small-v1")
+model = load_model("snoop2head/KoBrailleT5-small-v1")
+
+
+st.title("한국어 점역과 역점역")
+st.write("Braille Pattern Conversion")
+
+
+default_value = '위스키 브랜디 블루진 하이힐'
+src_text = st.text_area(
+    "번역하고 싶은 문장을 입력하세요:",
+    default_value,
+    height=300,
+    max_chars=100,
+)
+print(src_text)
+
+
+
+if src_text == "":
+    st.warning("Please **enter text** for translation")
+else: 
+    # translate into english sentence
+
+    translation_result = model.generate(
+        **tokenizer(
+            src_text,
+            return_tensors="pt",
+            padding="max_length",
+            truncation=True,
+            max_length=64,
+        ),
+        max_length=64,
+        num_beams=5,
+        repetition_penalty=1.3,
+        no_repeat_ngram_size=3,
+        num_return_sequences=1,
+    )
+    translation_result = tokenizer.decode(
+        translation_result[0],
+        clean_up_tokenization_spaces=True,
+        skip_special_tokens=True,
+    )
+
+    print(f"{src_text} -> {translation_result}")
+
+    st.write(translation_result)
+    print(translation_result)