from huggingface_hub import hf_hub_download
import re
from PIL import Image
import requests
from nougat.dataset.rasterize import rasterize_paper

from transformers import NougatProcessor, VisionEncoderDecoderModel
import torch

processor = NougatProcessor.from_pretrained("nielsr/nougat")
model = VisionEncoderDecoderModel.from_pretrained("nielsr/nougat")

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device) 


def get_pdf(pdf_link):
  unique_filename = f"{os.getcwd()}/downloaded_paper_{uuid.uuid4().hex}.pdf"

  response = requests.get(pdf_link)

  if response.status_code == 200:
      with open(unique_filename, 'wb') as pdf_file:
          pdf_file.write(response.content)
      print("PDF downloaded successfully.")
  else:
      print("Failed to download the PDF.")
  return unique_filename



def predict(image):
  # prepare PDF image for the model
  image = Image.open(image)
  pixel_values = processor(image, return_tensors="pt").pixel_values

  # generate transcription (here we only generate 30 tokens)
  outputs = model.generate(
      pixel_values.to(device),
      min_length=1,
      max_new_tokens=30,
      bad_words_ids=[[processor.tokenizer.unk_token_id]],
  )

  sequence = processor.batch_decode(outputs, skip_special_tokens=True)[0]
  sequence = processor.post_process_generation(sequence, fix_markdown=False)
  return sequence



def inference(pdf_file, pdf_link):
  if pdf_file is None:
    if pdf_link == '':
      print("No file is uploaded and No link is provided")
      return "No data provided. Upload a pdf file or provide a pdf link and try again!"
    else:
      file_name = get_pdf(pdf_link)
  else:
    file_name = pdf_file.name
    pdf_name = pdf_file.name.split('/')[-1].split('.')[0]

  images = rasterize_paper(file_name, return_pil=True)
  sequence = ""
  # infer for every page and concat
  for image in images:
    sequence += predict(image)


  content = sequence.replace(r'\(', '$').replace(r'\)', '$').replace(r'\[', '$$').replace(r'\]', '$$')
  return content

import gradio as gr
import uuid
import os
import requests
import re

css = """
  #mkd {
    height: 500px; 
    overflow: auto; 
    border: 1px solid #ccc; 
  }
"""

with gr.Blocks(css=css) as demo:
  gr.HTML("<h1><center>Nougat: Neural Optical Understanding for Academic Documents 🍫<center><h1>")
  gr.HTML("<h3><center>Lukas Blecher et al. <a href='https://arxiv.org/pdf/2308.13418.pdf' target='_blank'>Paper</a>, <a href='https://facebookresearch.github.io/nougat/'>Project</a><center></h3>")
  gr.HTML("<h3><center>This demo is based on transformers implementation of Nougat 🤗<center><h3>")


  with gr.Row():
    mkd = gr.Markdown('<h4><center>Upload a PDF</center></h4>',scale=1)
    mkd = gr.Markdown('<h4><center><i>OR</i></center></h4>',scale=1)
    mkd = gr.Markdown('<h4><center>Provide a PDF link</center></h4>',scale=1)
  
  
  with gr.Row(equal_height=True):
    pdf_file = gr.File(label='PDF 📑', file_count='single', scale=1)
    pdf_link = gr.Textbox(placeholder='Enter an arxiv link here', label='Link to Paper🔗', scale=1)

  with gr.Row():
    btn = gr.Button('Run Nougat 🍫')
    clr = gr.Button('Clear 🧼')

  output_headline = gr.Markdown("PDF converted to markup language through Nougat-OCR👇")
  parsed_output = gr.Markdown(elem_id='mkd', value='OCR Output 📝')
  
  btn.click(inference, [pdf_file, pdf_link], parsed_output )
  clr.click(lambda : (gr.update(value=None), 
                      gr.update(value=None),
                      gr.update(value=None)), 
             [], 
             [pdf_file, pdf_link, parsed_output]
            )


    
demo.queue()
demo.launch(debug=True)