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# 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
import docx
def getText(filename):
doc = docx.Document(filename)
fullText = []
for para in doc.paragraphs:
fullText.append(para.text)
return '\n'.join(fullText)
# Def_01 applying process bar to function
import sys
def print_progress_bar(index, total, label):
n_bar = 50 # Progress bar width
progress = index / total
sys.stdout.write('\r')
sys.stdout.write(f"[{'=' * int(n_bar * progress):{n_bar}s}] {int(100 * progress)}% {label}")
sys.stdout.flush()
if torch.cuda.is_available():
dev = "cuda"
else:
dev = "cpu"
device = torch.device(dev)
mname = '/content/drive/MyDrive/Transformers Models/opus-mt-en-hi-Trans Model'
tokenizer = MarianTokenizer.from_pretrained(mname)
model = MarianMTModel.from_pretrained(mname)
model.to(device)
def btTranslator(docxfile):
a=getText(docxfile)
a1=a.split('\n')
bigtext=''' '''
for a in a1:
bigtext=bigtext+'\n'+a
files=Document()
lt = LineTokenizer()
batch_size = 8
paragraphs = lt.tokenize(bigtext)
translated_paragraphs = []
for index, paragraph in enumerate(paragraphs):
# ######################################
total=len(paragraphs)
print_progress_bar(index, total, "Percentage Bar")
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)
f=files.save(f"Translated_{docxfile[23:]}")
return translated_paragraphs,f
import gradio as gr
interface = gr.Interface(fn=btTranslator,
inputs=gr.inputs.Textbox(lines=1),
# inputs = gr.inputs.File(file_count="multiple",label="Input Files"),
# inputs=
outputs=['text','file'],
show_progress=True
)
interface.launch(debug=True)
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