Delete streamlit_app.py

streamlit_app.py

@@ -1,123 +0,0 @@
-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