Create app.py

app.py

@@ -0,0 +1,69 @@
+import gradio as gr
+import numpy as np
+import pandas as pd
+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('/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()
+unique_cities = original_df['city'].unique()
+
+# Define the column names
+columns = ['surface', 'pieces', 'chambres', 'sdb', 'age', 'etage', 'etat_Bon état', 'etat_Nouveau', 'etat_À rénover', 'secteur', 'city']
+
+# Function to preprocess user input
+def preprocess_input(user_input, columns, unique_secteurs, unique_cities):
+    # Define the total number of features expected by the model
+    total_features = 1015
+
+    # Initialize all features to 0
+    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']
+    for feature in numerical_features:
+        input_array[0, columns.index(feature)] = user_input[feature]
+
+    # Update categorical features
+    for feature in ['secteur', 'city']:
+        if user_input[feature] in unique_secteurs or user_input[feature] in unique_cities:
+            input_array[0, columns.index(user_input[feature])] = 1
+
+    return input_array
+
+# Function to predict price based on user input
+def predict_price(user_input):
+    # Preprocess the user input
+    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]
+
+# 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 '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),
+        gr.Slider(label=f"Enter value for 'age'", minimum=0, maximum=115, step=1),
+        gr.Slider(label=f"Enter value for 'etage'", minimum=0, maximum=20, step=1),
+        gr.Slider(label=f"Enter value for 'etat_Bon état'", minimum=0, maximum=1, step=1),
+        gr.Slider(label=f"Enter value for 'etat_Nouveau'", minimum=0, maximum=1, step=1),
+        gr.Slider(label=f"Enter value for 'etat_À rénover'", minimum=0, maximum=1, step=1),
+        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)
+)
+
+# Launch the Gradio interface
+interface.launch(share=False, debug=False)