67 lines
2.7 KiB
Python
67 lines
2.7 KiB
Python
from transformers import DonutProcessor, VisionEncoderDecoderModel
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import gradio as gr
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from PIL import Image
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import torch
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import re
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from gradio.themes.utils import sizes
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theme = gr.themes.Default(radius_size=sizes.radius_none).set(
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block_label_text_color = '#4D63FF',
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block_title_text_color = '#4D63FF',
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button_primary_text_color = '#4D63FF',
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button_primary_background_fill='#FFFFFF',
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button_primary_border_color='#4D63FF',
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button_primary_background_fill_hover='#EDEFFF',
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)
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processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa")
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model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa")
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def vqa(image, question):
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inp = Image.fromarray(image.astype('uint8'), 'RGB')
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pixel_values = processor(inp, return_tensors="pt").pixel_values
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task_prompt = "{user_input}"
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prompt = task_prompt.replace("{user_input}", question)
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decoder_input_ids = processor.tokenizer(prompt, add_special_tokens=False, return_tensors="pt")["input_ids"]
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model.to(device)
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outputs = model.generate(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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output_scores=True)
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seq = processor.batch_decode(outputs.sequences)[0]
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seq = seq.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
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seq = re.sub(r"<.*?>", "", seq, count=1).strip()
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return processor.token2json(seq)
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with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as demo:
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with gr.Row():
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with gr.Column():
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image = gr.Image(label="图片")
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question = gr.Textbox(label="问题")
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with gr.Row():
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button = gr.Button("提交", variant="primary")
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box2 = gr.Textbox(label="文本")
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button.click(fn=vqa, inputs=[image, question], outputs=box2)
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examples = gr.Examples(examples=[['income.png', 'What are the 2020 net sales?'], ['invoice.png','What is the invoice number?']], inputs=[image, question], label="例子")
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if __name__ == "__main__":
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demo.queue(concurrency_count=3).launch(server_name = "0.0.0.0", server_port = 7026)
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