Update app.py

app.py

@@ -1,4 +1,3 @@
-
 import gradio as gr
 import numpy as np
 import pandas as pd
@@ -7,10 +6,8 @@ import tensorflow as tf
 # Load the trained model
 model = tf.keras.models.load_model('real_estate_price_prediction_model.h5')
 
-
-    
 # Load the original dataset to get unique categories for 'secteur' and 'city'
-original_df = pd.read_excel('Moroccan Real Estate Price Clean Dataset .xlsx')  # Replace with your dataset path
+original_df = pd.read_excel('/content/Moroccan Real Estate Price Clean Dataset .xlsx')  # Replace with your dataset path
 
 # Get unique categories for 'secteur' and 'city'
 unique_secteurs = original_df['secteur'].unique()
@@ -28,7 +25,7 @@ def preprocess_input(user_input, columns, unique_secteurs, unique_cities):
     input_array = np.zeros((1, total_features), dtype=np.float64)
 
     # Update numerical features
-    numerical_features = ['surface', 'pieces', 'chambres', 'sdb', 'age', 'etage', 'etat_Bon état', 'etat_Nouveau', 'etat_À rénover']
+    numerical_features = ['surface', 'pieces', 'chambres', 'sdb', 'age', 'etage', 'etat_Bon état', 'etat_À rénover']
     for feature in numerical_features:
         input_array[0, columns.index(feature)] = user_input[feature]
 
@@ -40,14 +37,27 @@ def preprocess_input(user_input, columns, unique_secteurs, unique_cities):
     return input_array
 
 # Function to predict price based on user input
-def predict_price(user_input):
+def predict_price(surface, pieces, chambres, sdb, age, etage, etat_Bon_état, etat_Nouveau, etat_À_rénover, secteur, city):
     # Preprocess the user input
+    user_input = {
+        'surface': surface,
+        'pieces': pieces,
+        'chambres': chambres,
+        'sdb': sdb,
+        'age': age,
+        'etage': etage,
+        'etat_Bon état': etat_Bon_état,
+        'etat_Nouveau': etat_Nouveau,
+        'etat_À rénover': etat_À_rénover,
+        'secteur': secteur,
+        'city': city
+    }
     input_array = preprocess_input(user_input, columns, unique_secteurs, unique_cities)
 
     # Make prediction using the model
     predicted_price = model.predict(input_array)
 
-    return predicted_price[0][0]
+    return f"Predicted price: {predicted_price[0][0]}"
 
 # Gradio interface setup
 interface = gr.Interface(
@@ -65,8 +75,8 @@ 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:", interactive=False)
+    outputs=gr.Textbox(label="Predicted Price(Dh):", interactive=False)
 )
 
 # Launch the Gradio interface
-interface.launch(share=False, debug=False)
+interface.launch(share=False, debug=False)