Update app.py

app.py

@@ -107,10 +107,8 @@ def process_image(image):
     return image
 
 
-title = "Restaurant/ Hotel Bill information extraction using LayoutLMv3 model"
-description = "Restaurant/ Hotel Bill information extraction - We use Microsoft's LayoutLMv3 trained on WildReceipt Dataset to predict the Store_name_value, Store_name_key, Store_addr_value, Store_addr_key, Tel_value, Tel_key, Date_value, Date_key, Time_value, Time_key, Prod_item_value, Prod_item_key, Prod_quantity_value, Prod_quantity_key, Prod_price_value, Prod_price_key, Subtotal_value, Subtotal_key, Tax_value, Tax_key, Tips_value, Tips_key, Total_value, Total_key. To use it, simply upload an image or use the example image below. Results will show up in a few seconds."
-
-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><br>[3] Hongbin Sun, Zhanghui Kuang, Xiaoyu Yue, Chenhao Lin, and Wayne Zhang. 2021. Spatial Dual-Modality Graph Reasoning for Key Information Extraction. arXiv. DOI:https://doi.org/10.48550/ARXIV.2103.14470  <a href='https://doi.org/10.48550/ARXIV.2103.14470'>Paper Link</a>" 
+title = "Receipt information extraction using LayoutLMv3 model"
+description = "Reciept information extraction - Here I use Microsoft's LayoutLMv3 trained on WildReceipt Dataset to predict the keys and values. To use it, simply upload an image or use the example image below. Results will show up in a few seconds."
 
 examples =[['example1.png'],['example2.png'],['example3.png']]
 
@@ -121,9 +119,8 @@ iface = gr.Interface(fn=process_image,
                      outputs=gr.Image(type="pil", label="annotated image"),
                      title=title,
                      description=description,
-                     article=article,
                      examples=examples,
                      css=css,
-                     analytics_enabled = True, enable_queue=True)
+                     analytics_enabled = True)
 
 iface.launch(inline=False, share=False, debug=False)