Update app.py

app.py

@@ -4,7 +4,7 @@
 # Prediction using Gradio on Hugging Face
 # Written by: Prakash R. Kota
 # Written on: 12 Feb 2025
-# Last update: 12 Feb 2025
+# Last update: 17 Mar 2025
 
 # Data Set from
 # Original: 
@@ -52,7 +52,7 @@ def predict(input_text):
 import gradio as gr
 
 # Create the Gradio interface
-interface = gr.Interface(
+iface = gr.Interface(
     fn=predict, 
     inputs=gr.Textbox(label="Enter 30 feature values, comma-separated"), 
     outputs="text",
@@ -60,6 +60,28 @@ interface = gr.Interface(
     description="Enter 30 numerical feature values separated by commas to predict whether the biopsy is Malignant or Benign."
 )
 
+# Add the Markdown footer with a clickable hyperlink
+footer = gr.Markdown("""
+    To test the Model, please copy the data from the original article, excluding the first two data points. <br>
+    For convenience, the data is reproduced here: <br>
+    17.99,10.38,122.8,1001,0.1184,0.2776,0.3001,0.1471,0.2419,0.07871,1.095,0.9053,8.589,153.4,0.006399,0.04904,0.05373,0.01587,0.03003,0.006193,25.38,17.33,184.6,2019,0.1622,0.6656,0.7119,0.2654,0.4601,0.1189 <br>
+    The original article - 
+    <a href="https://prakashkota.com/2025/02/11/the-power-of-machine-learning-in-medical-diagnosis-breast-cancer-mini-case-using-neural-networks/" target="_blank">
+    The Power of Machine Learning in Medical Diagnosis – Breast Cancer Mini Case using Neural Networks</a>
+""")
+
+# Launch the interface with the footer
+with gr.Blocks() as demo:
+    iface.render()
+    footer.render()
+
+# Ensure the app launches when executed
+if __name__ == "__main__":
+    demo.launch(share=True)
+
+# The first version had the interface function for the Gradio markdown
 # Launch the Gradio app
-interface.launch()
+#interface.launch()
+
+