#**************** IMPORT PACKAGES ********************
import gradio as gr
import numpy as np
import pytesseract as pt
import pdf2image
import os
import tempfile
from fpdf import FPDF
import re
import nltk
from nltk.tokenize import sent_tokenize
from nltk.tokenize import word_tokenize
import os
import pdfkit
import yake
from transformers import AutoTokenizer, AutoModelForPreTraining, AutoModel, AutoConfig
from summarizer import Summarizer,TransformerSummarizer
from transformers import pipelines
nltk.download('punkt')


model_name = 'nlpaueb/legal-bert-base-uncased'
# The setup of huggingface.co
custom_config = AutoConfig.from_pretrained(model_name)
custom_config.output_hidden_states=True
custom_tokenizer = AutoTokenizer.from_pretrained(model_name)
custom_model = AutoModel.from_pretrained(model_name, config=custom_config)
bert_legal_model = Summarizer(custom_model=custom_model, custom_tokenizer=custom_tokenizer)
from zipfile import ZipFile

from gtts import gTTS
from pdfminer.high_level import extract_text

def pdf_to_text(file_obj):
   text = extract_text(file_obj.name)
   
   output_text= bert_legal_model(text,  min_length = 8, ratio = 0.05)
   output_text = output_text.replace('   ',' ')
   output_text = output_text .replace(',.',',')
   output_text = output_text .replace('\n','  ')
   output_text = output_text .replace('..','.')
   
   pdf = FPDF()  

  # Add a page
   pdf.add_page()

   pdf.set_font("Times", size = 12)

  # open the text file in read mode
   f = output_text
  # insert the texts in pdf
   pdf.multi_cell(190, 10, txt = f, align = 'C')
  # save the pdf with name .pdf
   pdf.output("legal.pdf")  
   all_text
   
   myobj = gTTS(text=output_text, lang='en', slow=False)
   myobj.save("legal.wav")
   
   return 'legal.wav', output_text, "legal.pdf"


    
 #   path = folder_name
    
  #  return path 
    #pageObject.extractText() 
iface = gr.Interface(fn = pdf_to_text,
   inputs =  "file", outputs=["audio","text", "file"] )

if __name__ == "__main__":
    iface.launch(share=True)