import re import gradio as gr import torch from transformers import DonutProcessor, VisionEncoderDecoderModel 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 = "cuda" if torch.cuda.is_available() else "cpu" model.to(device) def process_document(image): # prepare encoder inputs. pixel_values = processor(image, return_tensors="pt").pixel_values # prepare decoder inputs task_prompt = "" decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids # generate answer outputs = model.generate( pixel_values.to(device), decoder_input_ids=decoder_input_ids.to(device), max_length=model.decoder.config.max_position_embeddings, early_stopping=True, pad_token_id=processor.tokenizer.pad_token_id, eos_token_id=processor.tokenizer.eos_token_id, use_cache=True, num_beams=1, bad_words_ids=[[processor.tokenizer.unk_token_id]], return_dict_in_generate=True, ) # postprocess 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() return processor.token2json(sequence) description = "To use it, simply upload your image and click 'submit', or click one of the examples to load them. Read more at the links below." article = "

Donut: OCR-free Document Understanding Transformer | Github Repo

" demo = gr.Interface( fn=process_document, inputs="image", outputs="json", title="Donut 🍩 for Document Parsing and Converting the Image to Database", description=description, article=article, enable_queue=True, examples=[["example.png"], ["example_2.png"], ["example_3.png"]], cache_examples=False) demo.launch()