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app.py

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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)