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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 | |
| 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) | |
| if torch.cuda.is_available(): | |
| dev = "cuda" | |
| else: | |
| dev = "cpu" | |
| device = torch.device(dev) | |
| # 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): | |
| 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): | |
| sleep(0.5) | |
| # mname = '/content/drive/MyDrive/Transformers Models/opus-mt-en-hi-Trans Model' | |
| tokenizer = MarianTokenizer.from_pretrained(lang) | |
| model = MarianMTModel.from_pretrained(lang) | |
| model.to(device) | |
| lt = LineTokenizer() | |
| batch_size = 8 | |
| paragraphs = lt.tokenize(bigtext) | |
| translated_paragraphs = [] | |
| for _, paragraph in zip(stqdm(paragraphs),paragraphs): | |
| # ###################################### | |
| 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) | |
| #files=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") | |
| #data=getText("C:\Users\Ambresh C\Desktop\Python Files\Translators\Trail Doc of 500 words.docx") | |
| binary_output = BytesIO() | |
| f3=btTranslator(datas).save(binary_output) | |
| #if datas : | |
| #if st.button(label='Data Process'): | |
| st.download_button(label='Download Translated File',file_name='Translated.docx', data=binary_output.getvalue()) | |
| #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 | |