import re
from transformers import DonutProcessor, VisionEncoderDecoderModel
import torch
import streamlit as st
from PIL import Image
import PyPDF2
from pypdf.errors import PdfReadError
from pypdf import PdfReader
import pypdfium2 as pdfium

document = st.file_uploader(label="Upload the document you want to explore",type=["png",'jpg', "jpeg","pdf"])


model_option = st.selectbox("Select the output of the model:",["Classification","Extract Info"])
if model_option == "Classification":
    processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-rvlcdip")
    model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-rvlcdip")

    device = "cpu"
    model.to(device)
    # load document image
    if document == None:
        st.write("Please upload the document in the box above")
    else:
        try:
            PdfReader(document)
            pdf = pdfium.PdfDocument(document)
            page = pdf.get_page(0)
            pil_image = page.render(scale = 300/72).to_pil()

            task_prompt = "<s_rvlcdip>"
            decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids

            pixel_values = processor(pil_image, return_tensors="pt").pixel_values

            outputs = model.generate(
                pixel_values.to(device),
                decoder_input_ids=decoder_input_ids.to(device),
                max_length=model.decoder.config.max_position_embeddings,
                pad_token_id=processor.tokenizer.pad_token_id,
                eos_token_id=processor.tokenizer.eos_token_id,
                use_cache=True,
                bad_words_ids=[[processor.tokenizer.unk_token_id]],
                return_dict_in_generate=True,
            )

            sequence = processor.batch_decode(outputs.sequences)[0]
            sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
            sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()  # remove first task start token
            st.image(pil_image,"Document uploaded")
            st.write(processor.token2json(sequence))

        except PdfReadError:
            document = Image.open(document)
            task_prompt = "<s_rvlcdip>"
            decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids

            pixel_values = processor(document, return_tensors="pt").pixel_values

            outputs = model.generate(
                pixel_values.to(device),
                decoder_input_ids=decoder_input_ids.to(device),
                max_length=model.decoder.config.max_position_embeddings,
                pad_token_id=processor.tokenizer.pad_token_id,
                eos_token_id=processor.tokenizer.eos_token_id,
                use_cache=True,
                bad_words_ids=[[processor.tokenizer.unk_token_id]],
                return_dict_in_generate=True,
            )

            sequence = processor.batch_decode(outputs.sequences)[0]
            sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
            sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()  # remove first task start token
            st.image(document,"Document uploaded")
            st.write(processor.token2json(sequence))


elif model_option == "Extract Info":
    processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")
    model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")

    device = "cpu"
    model.to(device)
    # load document image
    if document == None:
        st.write("Please upload the document in the box above")
    else:
        try:
            PdfReader(document)
            pdf = pdfium.PdfDocument(document)
            page = pdf.get_page(0)
            pil_image = page.render(scale = 300/72).to_pil()

            task_prompt = "<s_cord-v2>"
            decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids

            pixel_values = processor(pil_image, return_tensors="pt").pixel_values

            outputs = model.generate(
                pixel_values.to(device),
                decoder_input_ids=decoder_input_ids.to(device),
                max_length=model.decoder.config.max_position_embeddings,
                pad_token_id=processor.tokenizer.pad_token_id,
                eos_token_id=processor.tokenizer.eos_token_id,
                use_cache=True,
                bad_words_ids=[[processor.tokenizer.unk_token_id]],
                return_dict_in_generate=True,
            )

            sequence = processor.batch_decode(outputs.sequences)[0]
            sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
            sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()  # remove first task start token
            st.image(pil_image,"Document uploaded")
            st.write(processor.token2json(sequence))

        except PdfReadError:
            document = Image.open(document)
            task_prompt = "<s_cord-v2>"
            decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids

            pixel_values = processor(document, return_tensors="pt").pixel_values

            outputs = model.generate(
                pixel_values.to(device),
                decoder_input_ids=decoder_input_ids.to(device),
                max_length=model.decoder.config.max_position_embeddings,
                pad_token_id=processor.tokenizer.pad_token_id,
                eos_token_id=processor.tokenizer.eos_token_id,
                use_cache=True,
                bad_words_ids=[[processor.tokenizer.unk_token_id]],
                return_dict_in_generate=True,
            )

            sequence = processor.batch_decode(outputs.sequences)[0]
            sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
            sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()  # remove first task start token
            st.image(document,"Document uploaded")
            st.write(processor.token2json(sequence))