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import re
from transformers import DonutProcessor, VisionEncoderDecoderModel
from datasets import load_dataset
import torch
from PIL import Image
import numpy as np
import streamlit as st
processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-rvlcdip")
model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-rvlcdip")
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
#image = Image.open(r"C:\Invoices\Sample Invoices\sample invoice 1.tif")
#image = image.convert("RGB")
#print(np.array(image).shape)
st.title("Classify Document Image")
file_name = st.file_uploader("Upload a candidate image")
if file_name is not None:
col1, col2, col3 = st.columns(3)
image = Image.open(file_name)
image = image.convert("RGB")
# load document image
#dataset = load_dataset("hf-internal-testing/example-documents", split="test")
#image = dataset[2]["image"]
task_prompt = "<s_rvlcdip>"
decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
pixel_values = processor(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
print(processor.token2json(sequence))
col1.image(image, use_column_width=True)
col2.header("Results")
col2.subheader(processor.token2json(sequence))
processor_ext = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")
model_ext = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")
model_ext.to(device)
# prepare decoder inputs
task_prompt = "<s_cord-v2>"
decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
pixel_values = processor_ext(image, return_tensors="pt").pixel_values
outputs = model_ext.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_ext.batch_decode(outputs.sequences)[0]
sequence = sequence.replace(processor_ext.tokenizer.eos_token, "").replace(processor_ext.tokenizer.pad_token, "")
sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() # remove first task start token
col3.header("Features")
col3.subheader(processor_ext.token2json(sequence))
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