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streamlit_app.py

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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.sidebar.subheader("File Upload")
+
+datas=st.sidebar.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)
+#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