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496c151
delete img examples
Browse files- README.md +5 -5
- app.py +56 -0
- requirements.txt +3 -0
README.md
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---
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title:
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emoji:
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colorFrom:
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sdk: gradio
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sdk_version: 3.
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app_file: app.py
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pinned: false
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---
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---
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title: Donut Cord
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emoji: 💻
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colorFrom: red
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colorTo: indigo
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sdk: gradio
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sdk_version: 3.1.4
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app_file: app.py
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pinned: false
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---
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app.py
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import re
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import gradio as gr
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import torch
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from transformers import DonutProcessor, VisionEncoderDecoderModel
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processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")
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model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")
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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 process_document(image):
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# prepare encoder inputs
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pixel_values = processor(image, return_tensors="pt").pixel_values
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# prepare decoder inputs
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task_prompt = "<s_cord-v2>"
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decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
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# generate answer
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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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# postprocess
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sequence = processor.batch_decode(outputs.sequences)[0]
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sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
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sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() # remove first task start token
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return processor.token2json(sequence)
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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."
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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>"
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demo = gr.Interface(
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fn=process_document,
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inputs="image",
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outputs="json",
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title="Demo: Donut 🍩 for Document Parsing",
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description=description,
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article=article,
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enable_queue=True,
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#examples=[["example.png"], ["example_2.png"], ["example_3.png"]],
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cache_examples=False)
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demo.launch()
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requirements.txt
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torch
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git+https://github.com/huggingface/transformers.git
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sentencepiece
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