app.py

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')

import os

file_path = "saaara/real_estate_price_prediction/mon_modele.bin"
if os.path.exists(file_path):
    model = tf.keras.models.load_model(file_path)
else:
    print(f"Error: File '{file_path}' not found.")

    
# Load the original dataset to get unique categories for 'secteur' and 'city'
original_df = pd.read_excel("saaara/real_estate_price_prediction/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)