NMT_KD / app.py
Sakharam Gawade
Add application file
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import streamlit as st
import pandas as pd
import numpy as np
import re
from datetime import datetime
import subprocess
from fairseq.models.transformer import TransformerModel
time_interval=0
st.title('Knowledge Distillation in Neural Machine Translation')
title = st.text_input('English Text', 'I welcome you to the demonstration.')
if st.button('En-Hi Teacher'):
time_1 = datetime.now()
#subprocess.run('source ~/miniconda3/etc/profile.d/conda.sh && conda init bash')
file1 = open("translation/input-files/flores/eng.devtest","w")
file1.write(title)
file1.close()
subprocess.run('cd translation && bash -i translate-en-hi.sh && cd ..', shell=True)
time_2 = datetime.now()
time_interval = time_2 - time_1
file1 = open("translation/output-translation/flores/test-flores.hi","r")
st.write('Hindi Translation: ',file1.read())
file1.close()
st.write('Inference Time: ',time_interval)
if st.button('En-Hi Student'):
#title = re.sub('([.,!?()])', r' \1 ', title)
#title = re.sub('\s{2,}', ' ', title)
time_1 = datetime.now()
zh2en = TransformerModel.from_pretrained('Student_en_hi/out_distill/tokenized.en-hi/', checkpoint_file='../../checkpoint_use.pt',bpe='subword_nmt', bpe_codes='/home/sakharam/RnD/translation/en-hi/bpe-codes/codes.en',tokenizer='moses')
time_2 = datetime.now()
time_interval = time_2 - time_1
st.write('Hindi Translation: ',zh2en.translate([title.lower()])[0])
st.write('Inference Time: ',time_interval)