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import gradio as gr
import numpy as np
import pandas as pd
import tensorflow as tf
from IPython.display import HTML
from tensorflow.keras.metrics import MeanSquaredError
# 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
# 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_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(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 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(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),
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(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
interface.launch(share=False, debug=False) |