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import streamlit as st
from io import BytesIO
# import gradio as gr
# Def_04 Docx file to translated_Docx file
#from transformers import MarianMTModel, MarianTokenizer
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import nltk
from nltk.tokenize import sent_tokenize
from nltk.tokenize import LineTokenizer
nltk.download('punkt')
import math
import torch
from docx import Document
from time import sleep
from stqdm import stqdm
import docx
def getText(filename):
doc = docx.Document(filename)
fullText = []
for para in doc.paragraphs:
fullText.append(para.text)
return '\n'.join(fullText)
# mname = 'Helsinki-NLP/opus-mt-en-hi'
# tokenizer = MarianTokenizer.from_pretrained(mname)
# model = MarianMTModel.from_pretrained(mname)
# model.to(device)
#@st.cache
def btTranslator(docxfile):
if torch.cuda.is_available():
dev = "cuda"
else:
dev = "cpu"
device = torch.device(dev)
a=getText(docxfile)
a1=a.split('\n')
bigtext=''' '''
for a in a1:
bigtext=bigtext+'\n'+a
files=Document()
a="Helsinki-NLP/opus-mt-en-ru"
b="Helsinki-NLP/opus-mt-ru-fr"
c="Helsinki-NLP/opus-mt-fr-en"
# d="Helsinki-NLP/opus-mt-es-en"
langs=[a,b,c]
text=bigtext
for _,lang in zip(stqdm(langs),langs):
st.spinner('Wait for it...')
sleep(0.5)
# mname = '/content/drive/MyDrive/Transformers Models/opus-mt-en-hi-Trans Model'
tokenizer = AutoTokenizer.from_pretrained(lang)
model = AutoModelForSeq2SeqLM.from_pretrained(lang)
model.to(device)
lt = LineTokenizer()
batch_size = 64
paragraphs = lt.tokenize(bigtext)
translated_paragraphs = []
for _, paragraph in zip(stqdm(paragraphs),paragraphs):
st.spinner('Wait for it...')
# ######################################
sleep(0.5)
# ######################################
sentences = sent_tokenize(paragraph)
batches = math.ceil(len(sentences) / batch_size)
translated = []
for i in range(batches):
sent_batch = sentences[i*batch_size:(i+1)*batch_size]
model_inputs = tokenizer(sent_batch, return_tensors="pt", padding=True, truncation=True, max_length=500).to(device)
with torch.no_grad():
translated_batch = model.generate(**model_inputs)
translated += translated_batch
translated = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
translated_paragraphs += [" ".join(translated)]
#files.add_paragraph(translated)
translated_text = "\n".join(translated_paragraphs)
bigtext=translated_text
files.add_paragraph(bigtext)
#files2save=files.save("Translated.docx")
#files.save("Translated.docx")
#binary_output = BytesIO()
#f=files.save(binary_output)
#f2=f.getvalue()
return files
#return translated_text
st.title('Translator App')
st.markdown("Translate from Docx file")
st.subheader("File Upload")
datas=st.file_uploader("Original File")
name=st.text_input('Enter New File Name: ')
#data=getText("C:\Users\Ambresh C\Desktop\Python Files\Translators\Trail Doc of 500 words.docx")
#if datas :
#if st.button(label='Data Process'):
binary_output = BytesIO()
if st.button(label='Translate'):
st.spinner('Waiting...')
btTranslator(datas).save(binary_output)
binary_output.getbuffer()
st.success("Translated")
st.download_button(label='Download Translated File',file_name=(f"{name}_Translated.docx"), data=binary_output.getvalue())
#files.save(f"{name}_Translated.docx")
#else:
# st.text('Upload File and Start the process')
#f4=binary_output(f3)
#st.sidebar.download_button(label='Download Translated File',file_name='Translated.docx', data=binary_output.getvalue())
# st.text_area(label="",value=btTranslator(datas),height=100)
# Footer |