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# -*- 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")
default_value = "한국어 문장 변환기 QuillBot입니다."
src_text = st.text_area(
"바꾸고 싶은 문장을 입력하세요:",
default_value,
height=50,
max_chars=200,
)
print(src_text)
# num_beams = st.sidebar.slider(
# "Num Beams", min_value=5, max_value=10, value=5
# ) # https://huggingface.co/blog/constrained-beam-search
# temperature = st.sidebar.slider(
# "Temperature", value=0.9, min_value=0.0, max_value=1.0, step=0.05
# )
# top_k = st.sidebar.slider("Top-k", min_value=0, max_value=5, value=0)
# top_p = st.sidebar.slider("Top-p", min_value=0.0, max_value=1.0, step=0.05, value=1.0)
def infer_sentence(model, src_text, tokenizer=tokenizer):
encoded_prompt = tokenizer.encode(
src_text,
add_special_tokens=False,
return_tensors="pt",
padding=True,
max_length=64,
)
if encoded_prompt.size()[-1] == 0:
input_ids = None
else:
input_ids = encoded_prompt
output_sequences = model.generate(
input_ids=input_ids,
max_length=64,
num_beams=5,
repetition_penalty=1.3,
no_repeat_ngram_size=3,
num_return_sequences=1,
)
print(output_sequences)
generated_sequence = output_sequences[0]
print(generated_sequence)
# Decode text
text = tokenizer.decode(
generated_sequence, clean_up_tokenization_spaces=True, skip_special_tokens=True
)
print(text)
# Remove all text after the pad token
stop_token = tokenizer.eos_token
text = text[: text.find(stop_token) if stop_token else None]
text = text.strip()
return text
if st.button("문장 변환") or src_text == default_value:
if src_text == "":
st.warning("Please **enter text** for translation")
else:
st.success("Translating...")
english_translation = infer_sentence(
model=ko2en_model, src_text=src_text, tokenizer=tokenizer
)
korean_translation = en2ko_model.generate(
**tokenizer(
english_translation,
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,
)
korean_translation = tokenizer.decode(
korean_translation[0],
clean_up_tokenization_spaces=True,
skip_special_tokens=True,
)
st.success(f"{src_text} -> {english_translation} -> {korean_translation}")
else:
pass
st.write(korean_translation)
print(korean_translation)
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