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| # Imports | |
| import re | |
| import gradio as gr | |
| import torch | |
| from transformers import DonutProcessor, VisionEncoderDecoderModel | |
| from dotenv import load_dotenv | |
| import os | |
| from torchvision import transforms | |
| import torch | |
| from PIL import Image | |
| # device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| model_name = 'naver-clova-ix/donut-base-finetuned-docvqa' | |
| # Importante esta app esta pensada para que el modelo corra en CPU | |
| processor = DonutProcessor.from_pretrained(model_name) | |
| model = VisionEncoderDecoderModel.from_pretrained(model_name) | |
| # Defino la funcion principal que ejecuta el modelo y obtiene los resultados | |
| def process_image(image, question): | |
| # Paso por el procesador la imagen y especifico que los outputs sean tensores de pytorch | |
| pixel_values = processor(image, return_tensors='pt').pixel_values | |
| # Seteo el prompt | |
| prompt = f"<s_docvqa><s_question>{question}</s_question><s_answer>" | |
| # Generamos la sequencia de tokens de salida esto es un vector largo con los ids | |
| # Esta parte encodea la pregunta y se la pasa al decoder junto con la representaci贸n de la imagen post encoder | |
| decoder_input_ids = processor.tokenizer(prompt, add_special_tokens=False, return_tensors='pt').input_ids | |
| # Defino los outputs | |
| outputs = model.generate( | |
| pixel_values, | |
| decoder_input_ids=decoder_input_ids, | |
| 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, # Probar cambiando este parametro | |
| bad_words_ids=[[processor.tokenizer.unk_token_id]], | |
| return_dict_in_generate=True | |
| ) | |
| # Realizo el Post-processing de la salida del modelo | |
| sequence = processor.batch_decode(outputs.sequences)[0] | |
| sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "") | |
| processor.tokenizer.eos_token | |
| sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() | |
| return processor.token2json(sequence) | |
| description = "Esta es una aplicacci贸n realizada con el modelo Donut fine tuned en DocVQA" | |
| demo = gr.Interface( | |
| fn=process_image, | |
| inputs=['image', 'text'], | |
| outputs='json', | |
| title='Demo: Document Question Answering', | |
| description=description, | |
| enable_queue=True, | |
| examples=[ | |
| ['examples/dni_25.jpg', 'cual es el documento / document number?'], | |
| ['examples/extracto.jpg', 'cual es el telefono de centros servicios de banco galicia?'], | |
| ['examples/factura_5.jpg', 'cual es el total de la factura?'], | |
| ] | |
| ) | |
| demo.launch(inline=True) |