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import streamlit as st | |
import model | |
import inference | |
with st.spinner('Your TransFormer is on the way...'): | |
if 'transformer' not in st.session_state: | |
transformer,tokenizer_ass,tokenizer_en,MAX_LENGTH = model.prepare_model() | |
st.session_state['transformer'] = transformer | |
st.session_state['tokenizer_ass'] = tokenizer_ass | |
st.session_state['tokenizer_en'] = tokenizer_en | |
st.session_state['MAX_LENGTH'] = MAX_LENGTH | |
def show_information(): | |
st.header('Translate Assamese with Transformer!🤖') | |
def select_text(): | |
option = st.selectbox( | |
'Suggested Assamese Sentences', | |
('মানুহে সদায় ইজনে সিজনক সহায় কৰিব লাগিব', | |
'আমি সদায় আমাৰ মাক সন্মান কৰিব লাগিব', | |
'আপুনি আপোনাৰ সপোন প্ৰাপ্ত নকৰালৈকে সদায় কঠোৰ আৰু কঠোৰ পৰিশ্ৰম কৰিব লাগিব')) | |
st.write('You have selected suggested text') | |
title = st.text_input('Assamese Text Input', option) | |
# st.write('Your Assamese Text', title) | |
return title | |
def main(): | |
st.title('📚Assamese to English Translator🤖') | |
show_information() | |
text = select_text() | |
if st.button('Translate'): | |
result = inference.translate_main(st.session_state['transformer'],text,st.session_state['tokenizer_ass'], | |
st.session_state['tokenizer_en'],st.session_state['MAX_LENGTH']) | |
st.caption('Your Assamese translated text') | |
st.text(result) | |
if __name__ == "__main__": | |
main() |