import gradio as gr
import numpy as np
import pytesseract as pt
import pdf2image
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 summarizer import Summarizer,TransformerSummarizer
from transformers import pipelines
nltk.download('punkt')
from transformers import AutoTokenizer, AutoModelForPreTraining, AutoConfig
# model_name = 'distilbert-base-uncased'
model_name = 'nlpaueb/legal-bert-base-uncased'
#model_name = 'laxya007/gpt2_legal'
# model_name = 'facebook/bart-large-cnn'

# 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)
print('Using model {}\n'.format(model_name))



def lincoln(content = input_text):
  summary_text = ""
  for i, paragraph in enumerate(content.split("\n\n")):
      # get rid of empty paragraphs and one word paras and extra whitespaces
      paragraph = paragraph.replace('\n',' ')
      paragraph = paragraph.replace('\t','')
      paragraph = ' '.join(paragraph.split())
      # count words in the paragraph and exclude if less than 4 words
      tokens = word_tokenize(paragraph)
      # only do real words
      tokens = [word for word in tokens if word.isalpha()]
      # print("\nTokens: {}\n".format(len(tokens)))
      # only do sentences with more than 1 words excl. alpha crap
      if len(tokens) <= 1:
          continue
      # Perhaps also ignore paragraphs with no sentence?
      sentences = sent_tokenize(paragraph)

      # recreate paragraph from the only words tokens list
      paragraph = ' '.join(tokens)

      print("\nParagraph:")
      print(paragraph+"\n")
      # T5 needs to have 'summarize' in order to work:
      # text = "summarize:" + paragraph
      text = paragraph
      # encoding the input text
      
      summary = bert_legal_model(content, ratio = 0.01)
      # summary = tokenizer_t5.decode(summary_ids[0], skip_special_tokens=True)
      summary_text += str(summary) + "\n\n"
      print("Summary:")
      print(summary)

  summary = bert_legal_model(content, ratio=0.1)

  all_text = str(summary) + "\n\n\n" \
      + "-------- The Larger Summary --------\n" + str(summary_text) 
  output_text  = all_text
  return 

    
iface = gr.Interface(
    lincoln, 
    "text", 
    "text"
   )

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