import torch, re from PIL import Image from transformers import DonutProcessor, VisionEncoderDecoderModel import streamlit as st from dotenv import load_dotenv import os load_dotenv() # image_path = '/app/Datasplit/test/1099_Div/filled_form_43.jpg' # image = Image.open(image_path) # imgae = image.resize((1864, 1440)) device = "cuda" if torch.cuda.is_available() else "cpu" # Load the processor from the local directory processor = DonutProcessor.from_pretrained("Henge-navuuu/donut-base-finetuned-forms-v1") # Load the model from the local directory model = VisionEncoderDecoderModel.from_pretrained("Henge-navuuu/donut-base-finetuned-forms-v1") model.to(device) @st.cache_resource def inference(image): pixel_values = processor(image, return_tensors="pt").pixel_values task_prompt = "" decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt")["input_ids"] # device = "cuda" if torch.cuda.is_available() else "cpu" model.to(device) 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, output_scores=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)) return processor.token2json(sequence) # data = inference(image) # print(data)