Update app.py

app.py

@@ -58,7 +58,7 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 # model
 from transformers import LayoutLMv2ForTokenClassification
 
-model_id = "pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-linelevel-ml384"
+model_id = "pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-paragraphlevel-ml512"
 
 model = LayoutLMv2ForTokenClassification.from_pretrained(model_id);
 model.to(device);
@@ -133,7 +133,7 @@ def app_outputs(uploaded_pdf):
     return msg, img_files[0], img_files[1], images[0], images[1], csv_files[0], csv_files[1], df[0], df[1]
 
 # gradio APP
-with gr.Blocks(title="Inference APP for Document Understanding at line level (v2 - LayoutXLM base)", css=".gradio-container") as demo:
+with gr.Blocks(title="Inference APP for Document Understanding at paragraph level (v2 - LayoutXLM 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 line level (v2 - LayoutXLM base)</h1></div>
     <div style="margin-top: 40px"><p>(03/05/2023) This Inference APP uses the <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-linelevel-ml384" target="_blank">model Layout XLM base combined with XLM-RoBERTa base and finetuned on the dataset DocLayNet base at line level</a> (chunk size of 384 tokens).</p></div>