Update app.py

app.py

@@ -136,7 +136,6 @@ def app_outputs(uploaded_pdf):
 with gr.Blocks(title="Inference APP for Document Understanding at paragraph level (v1 - LiLT base)", css=".gradio-container") as demo:
     gr.HTML("""
     <div style="font-family:'Times New Roman', 'Serif'; font-size:26pt; font-weight:bold; text-align:center;"><h1>Inference APP for Document Understanding at paragraph level (v1 - LiLT base)</h1></div>
-    <div style="margin-top: 40px"><p><b>[ Check as well the <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/spaces/pierreguillou/Inference-APP-Document-Understanding-at-linelevel-v1" target="_blank">Inference APP for Document Understanding at LINE level</a>! ]</b></p></div>
     <div><p>(02/16/2023) This Inference APP uses the <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/pierreguillou/lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-paragraphlevel-ml512" target="_blank">model LiLT base combined with XLM-RoBERTa base and finetuned on the dataset DocLayNet base at paragraph level</a> (chunk size of 512 tokens).</p></div>
     <div><p><a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://arxiv.org/abs/2202.13669" target="_blank">LiLT (Language-Independent Layout Transformer)</a> is a Document Understanding model that uses both layout and text in order to detect labels of bounding boxes. Combined with the model <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/xlm-roberta-base" target="_blank">XML-RoBERTa base</a>, this finetuned model has the capacity to <b>understand any language</b>. Finetuned on the dataset <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/datasets/pierreguillou/DocLayNet-base" target="_blank">DocLayNet base</a>, it can <b>classifly any bounding box (and its OCR text) to 11 labels</b> (Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title).</p></div>
     <div><p>It relies on an external OCR engine to get words and bounding boxes from the document image. Thus, let's run in this APP an OCR engine (<a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://github.com/madmaze/pytesseract#python-tesseract" target="_blank">PyTesseract</a>) to get the bounding boxes, then run LiLT (already fine-tuned on the dataset DocLayNet base at paragraph level) on the individual tokens and then, visualize the result at paragraph level!</p></div>