import re import gradio as gr import torch from transformers import DonutProcessor, VisionEncoderDecoderModel processor = DonutProcessor.from_pretrained("debu-das/donut_receipt_v2.29") model = VisionEncoderDecoderModel.from_pretrained("debu-das/donut_receipt_v2.29") 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() # remove first task start token return processor.token2json(sequence) description = "Gradio Demo for Donut, an instance of `VisionEncoderDecoderModel` fine-tuned on CORD (document parsing). 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="Demo: Donut 🍩 for Document Parsing", description=description, article=article, enable_queue=True, examples=[["example.png"], ["example_1.png"],["example_2.png"], ["example_3.png"],["example_4.png"]], cache_examples=False) demo.launch()