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| import re | |
| import gradio as gr | |
| import torch | |
| from transformers import DonutProcessor, VisionEncoderDecoderModel | |
| processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa") | |
| model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa") | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model.to(device) | |
| def process_document(image, *questions): | |
| output = [] | |
| for question in questions: | |
| # prepare encoder inputs | |
| pixel_values = processor(image, return_tensors="pt").pixel_values | |
| # prepare decoder inputs | |
| task_prompt = "<s_docvqa><s_question>{user_input}</s_question><s_answer>" | |
| prompt = task_prompt.replace("{user_input}", question) | |
| decoder_input_ids = processor.tokenizer(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 | |
| output.append(processor.token2json(sequence)) | |
| return output | |
| description = "Gradio Demo for Donut, an instance of `VisionEncoderDecoderModel` fine-tuned on DocVQA (document visual question answering). To use it, simply upload your image and type a question and click 'submit', or click one of the examples to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2111.15664' target='_blank'>Donut: OCR-free Document Understanding Transformer</a> | <a href='https://github.com/clovaai/donut' target='_blank'>Github Repo</a></p>" | |
| vqa_questions = {} | |
| vqa_questions["ACCOUNT/BILL NUMBER"] = "What is the account or bill number?" | |
| vqa_questions["TOTAL"] = "What is the total amount or total price?" | |
| vqa_questions["ITEMS"] = "What are the items?" | |
| vqa_questions["GST AMOUNT"] = "What is the GST or tax amount?" | |
| vqa_questions["GST NO."] = "What is the GST number?" | |
| vqa_questions[ | |
| "SELLER/BILLING DETAILS" | |
| ] = "What are the seller details or billing details" | |
| vqa_questions["BILLING ADDRESS"] = "What is the billing address?" | |
| demo = gr.Interface( | |
| fn=process_document, | |
| inputs=["image"] + [gr.components.Textbox(value=question) for question in vqa_questions.values()], | |
| outputs="json", | |
| title="Demo: Donut 🍩 for DocVQA", | |
| description=description, | |
| article=article, | |
| enable_queue=True, | |
| # examples=[["example_3.jpg", "What is the total?"], ["example_1.png", "When is the coffee break?"], ["example_2.jpeg", "What's the population of Stoddard?"]], | |
| cache_examples=False) | |
| demo.launch() |