43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
import gradio as gr
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import torch
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from transformers import LayoutLMv3Processor, LayoutLMv3ForQuestionAnswering
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##这个模型的示例存在问题，无法运行
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def inference(img):
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pretrained_model_path = "layoutlmv3-base-mpdocvqa"
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processor = LayoutLMv3Processor.from_pretrained(pretrained_model_path, apply_ocr=False)
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model = LayoutLMv3ForQuestionAnswering.from_pretrained(pretrained_model_path)
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image = img.convert("RGB")
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question = "Is this a question?"
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context = ["Example"]
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boxes = [0, 0, 1000, 1000] # This is an example bounding box covering the whole image.
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document_encoding = processor(image, question, context, boxes=boxes, return_tensors="pt")
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outputs = model(**document_encoding)
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# Get the answer
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start_idx = torch.argmax(outputs.start_logits, axis=1)
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end_idx = torch.argmax(outputs.end_logits, axis=1)
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answers = processor.tokenizer.decode(input_tokens[start_idx: end_idx + 1]).strip()
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return answers
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title = "layoutlmv3-base-mpdocvqa"
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description = "Gradio Demo for layoutlmv3-base-mpdocvqa. To use it, simply upload your image, or click one of the examples to load them."
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article = "<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='visitor badge'></center></p>"
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examples = [['example_cat.jpg'], ['Masahiro.png']]
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demo = gr.Interface(
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fn=inference,
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inputs=[gr.inputs.Image(type="pil")],
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outputs=gr.outputs.Textbox(),
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title=title,
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description=description,
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article=article,
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examples=examples)
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demo.launch()
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