Update app.py

app.py

@@ -15,6 +15,7 @@ with demo:
     
     gr.Markdown("# **Binary Classification using Gated Residual and Variable Selection Networks** \n")
     gr.Markdown("This space demonstrates the use of Gated Residual Networks (GRN) and Variable Selection Networks (VSN), proposed by Bryan Lim et al. in <a href=\"https://arxiv.org/abs/1912.09363/\">Temporal Fusion Transformers (TFT) for Interpretable Multi-horizon Time Series Forecasting</a> for structured data classification")
+    gr.Markdown("The model used by this space was trained on the [United States Census Income Dataset](https://archive.ics.uci.edu/ml/datasets/Census-Income+%28KDD%29) dataset and helps to predict if the income of a person will be >$500K or not.")
     gr.Markdown("Play around and see yourself 🤗 ")
     
     with gr.Tabs():
@@ -54,7 +55,7 @@ with demo:
     
     predict_button.click(user_input_predict, inputs=inputs_list, outputs=final_output)
     compute_button.click(batch_predict, inputs=input_df, outputs=output_df)
-    gr.Markdown('\n Author: <a href=\"https://www.linkedin.com/in/shivalika-singh/\">Shivalika Singh</a> <br> Based on this <a href=\"https://keras.io/examples/structured_data/classification_with_grn_and_vsn/\">Keras example</a> by <a href=\"https://www.linkedin.com/in/khalid-salama-24403144/\">Khalid Salama</a> <br> Demo Powered by this <a href=\"https://huggingface.co/keras-io/structured-data-classification-grn-vsn/\">GRN-VSN model</a>')
+    gr.Markdown('\n Demo created by: <a href=\"https://www.linkedin.com/in/shivalika-singh/\">Shivalika Singh</a> <br> Based on this <a href=\"https://keras.io/examples/structured_data/classification_with_grn_and_vsn/\">Keras example</a> by <a href=\"https://www.linkedin.com/in/khalid-salama-24403144/\">Khalid Salama</a> <br> Demo Powered by this <a href=\"https://huggingface.co/keras-io/structured-data-classification-grn-vsn/\">GRN-VSN model</a>')
     
     
 demo.launch()