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import re |
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import transformers |
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from PIL import Image |
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from transformers import DonutProcessor, VisionEncoderDecoderModel |
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import torch |
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import random |
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import numpy as np |
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import gradio as gr |
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access_token = "" |
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transformers.logging.disable_default_handler() |
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processor = DonutProcessor.from_pretrained("daquarti/donut-base-sroie", use_auth_token=access_token) |
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model = VisionEncoderDecoderModel.from_pretrained("daquarti/donut-base-sroie", use_auth_token=access_token) |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model.to(device) |
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def load_image (f): |
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with Image.open(f) as img: |
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a = img.load() |
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return img.convert('RGB') |
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def pred (a): |
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pixel_values = processor(a, return_tensors="pt").pixel_values |
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task_prompt = "<s>" |
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decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids |
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outputs = model.generate( |
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pixel_values.to(device), |
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decoder_input_ids=decoder_input_ids.to(device), |
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max_length=model.decoder.config.max_position_embeddings, |
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early_stopping=True, |
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pad_token_id=processor.tokenizer.pad_token_id, |
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eos_token_id=processor.tokenizer.eos_token_id, |
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use_cache=True, |
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num_beams=1, |
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bad_words_ids=[[processor.tokenizer.unk_token_id]], |
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return_dict_in_generate=True, |
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) |
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prediction = processor.batch_decode(outputs.sequences)[0] |
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prediction = processor.token2json(prediction) |
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return str (prediction) |
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examples = ['1.jpg', '2.jpg'] |
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demo = gr.Interface(fn=pred, inputs="image", outputs= "text", examples= examples) |
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demo.launch(share= False) |
