Updated file

app.py

@@ -1,6 +1,4 @@
 import os
-os.system('pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu')
-
 import gradio as gr
 import numpy as np
 from transformers import AutoModelForTokenClassification
@@ -92,23 +90,15 @@ def process_image(image):
     return image
 
 
-title = "Invoice Information extraction using LayoutLMv3 model"
-description = "Invoice Information Extraction - We use Microsoft's LayoutLMv3 trained on Invoice Dataset to predict the Biller Name, Biller Address, Biller post_code, Due_date, GST, Invoice_date, Invoice_number, Subtotal and Total. 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>" 
-
-examples =[['example1.png'],['example2.png'],['example3.png']]
+title = "Invoice Information extraction by Mohit Kumar"
+description = "Invoice Information Extraction for Biller Name, Biller Address, Biller post_code, Due_date, GST, Invoice_date, Invoice_number, Subtotal and Total. To use it, simply upload an image or use the example image below. Results will show up in a few seconds."
 
-css = """.output_image, .input_image {height: 600px !important}"""
 
 iface = gr.Interface(fn=process_image, 
                      inputs=gr.inputs.Image(type="pil"), 
                      outputs=gr.outputs.Image(type="pil", label="annotated image"),
                      title=title,
                      description=description,
-                     article=article,
-                     examples=examples,
-                     css=css,
                      analytics_enabled = True, enable_queue=True)
 
 iface.launch(inline=False, share=False, debug=False)