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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from configs.download_files import FileDownloader
from configs.db_configs import add_one_item
from streamlit.components.v1 import html
from configs.html_features import set_image
def translate_text_to_text(text, source_lang, target_lang):
prefix = f'translate {source_lang} to {target_lang}: '
text = prefix + text
tokenizer = AutoTokenizer.from_pretrained('stevhliu/my_awesome_opus_books_model')
input_ids = tokenizer(text, return_tensors='pt').input_ids
model = AutoModelForSeq2SeqLM.from_pretrained('stevhliu/my_awesome_opus_books_model')
output_ids = model.generate(input_ids, max_new_tokens=len(input_ids[0]) * 3, do_sample=False)
translated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
return translated_text
def main():
st.title('Text Translator')
im1, im2, im3 = st.columns([1, 5.3, 1])
with im1:
pass
with im2:
url = "https://i.postimg.cc/jdF1hPng/combined.png"
html(set_image(url), height=500, width=500)
with im3:
pass
languages = ['English', 'French']
source_lang = st.sidebar.selectbox('Source Language', languages)
target_lang = st.sidebar.selectbox('Target Language', languages, index=1)
text = st.text_area('Text Translator', placeholder='Enter your input text here ...', height=200, label_visibility='hidden')
if st.button('translate it'):
if text != '':
if (source_lang == 'English' and target_lang == 'English') or (source_lang == 'French' and target_lang == 'French'):
st.error('Expected different values for source and target languages, but got the same values!')
else:
with st.expander('Original Text'):
st.write(text)
add_one_item(text, 'Text Translator')
with st.expander('Translated Text'):
translated_text = translate_text_to_text(text, source_lang, target_lang)
st.write(translated_text)
with st.expander('Download Translated Text'):
FileDownloader(translated_text, 'txt').download()
else:
st.error('Please enter a non-empty text.')
if __name__ == '__main__':
main() |