Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import numpy as np
 import pandas as pd
 import tensorflow as tf
+from IPython.display import HTML
 
 # Load the trained model
 model = tf.keras.models.load_model('real_estate_price_prediction_model.h5')
@@ -59,11 +60,14 @@ def predict_price(surface, pieces, chambres, sdb, age, etage, etat_Bon_état, et
 
     return f"Predicted price: {predicted_price[0][0]}"
 
+# Create HTML code to display an image
+image_html = "<img src='/content/Capture d’écran 2024-01-28 155359.jpg' style='max-width:100%;'>"
+
 # Gradio interface setup
 interface = gr.Interface(
     fn=predict_price,  # The function to be called with user input
     inputs=[
-        gr.Slider(label=f"Enter value for 'surface'", minimum=0, maximum=500, step=1),
+        gr.Slider(label=f"Enter value for 'surface(m²)'", minimum=0, maximum=500, step=1),
         gr.Slider(label=f"Enter value for 'pieces'", minimum=0, maximum=15, step=1),
         gr.Slider(label=f"Enter value for 'chambres'", minimum=0, maximum=10, step=1),
         gr.Slider(label=f"Enter value for 'sdb'", minimum=0, maximum=5, step=1),
@@ -75,7 +79,14 @@ interface = gr.Interface(
         gr.Textbox(label=f"Enter value for 'secteur'", type="text"),
         gr.Textbox(label=f"Enter value for 'city'", type="text")
     ],
-    outputs=gr.Textbox(label="Predicted Price(Dh):", interactive=False)
+    outputs=gr.Textbox(label="Predicted Price(Dh):", interactive=False),
+    title="Real Estate Price Prediction",
+    description="Enter property details to predict its price.",
+    examples=[
+        [250, 5, 3, 2, 10, 3, 1, 0, 0, "'Secteur_A'", "'City_X'"],
+        [150, 4, 2, 1, 5, 2, 1, 0, 0, "'Secteur_B'", "'City_Y'"]
+    ],
+    theme="compact",  # Compact theme for a cleaner look
 )
 
 # Launch the Gradio interface