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Update app.py
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app.py
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@@ -52,29 +52,29 @@ processor = LayoutLMv3Processor.from_pretrained("microsoft/layoutlmv3-base",appl
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model = LayoutLMv3ForTokenClassification.from_pretrained("nielsr/layoutlmv3-finetuned-funsd")
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dataset = load_dataset("nielsr/funsd", split="test")
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image = Image.open(dataset[0]["image_path"]).convert("RGB")
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image = Image.open("./invoice.png")
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image.save("document1.png")
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image = Image.open(dataset[1]["image_path"]).convert("RGB")
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image = Image.open("./invoice2.png")
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image.save("document2.png")
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image = Image.open(dataset[2]["image_path"]).convert("RGB")
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image = Image.open("./invoice3.png")
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image.save("document3.png")
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#dataset = load_dataset("nielsr/funsd-layoutlmv3")
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#example2["image"]
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@@ -151,7 +151,7 @@ description = "Extraction of Form or Invoice Extraction - We use Microsoft's Lay
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article="<b>References</b><br>[1] Y. Xu et al., “LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking.” 2022. <a href='https://arxiv.org/abs/2204.08387'>Paper Link</a><br>[2] <a href='https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv3'>LayoutLMv3 training and inference</a>"
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examples =[['
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css = """.output_image, .input_image {height: 600px !important}"""
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model = LayoutLMv3ForTokenClassification.from_pretrained("nielsr/layoutlmv3-finetuned-funsd")
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dataset = load_dataset("nielsr/funsd", split="test")
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#image = Image.open(dataset[0]["image_path"]).convert("RGB")
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#image = Image.open("./invoice.png")
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#image.save("document1.png")
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#image = Image.open(dataset[1]["image_path"]).convert("RGB")
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#image = Image.open("./invoice2.png")
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#image.save("document2.png")
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#image = Image.open(dataset[2]["image_path"]).convert("RGB")
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#image = Image.open("./invoice3.png")
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#image.save("document3.png")
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#dataset = load_dataset("nielsr/funsd-layoutlmv3")
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example = dataset["test"][0]
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example["image"].save("example1.png")
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example1 = dataset["test"][1]
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example1["image"].save("example2.png")
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example2 = dataset["test"][2]
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example2["image"].save("example3.png")
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#example2["image"]
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article="<b>References</b><br>[1] Y. Xu et al., “LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking.” 2022. <a href='https://arxiv.org/abs/2204.08387'>Paper Link</a><br>[2] <a href='https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv3'>LayoutLMv3 training and inference</a>"
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examples =[['example1.png'],['example1.png'],['example1.png']]
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css = """.output_image, .input_image {height: 600px !important}"""
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