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import streamlit as st
import numpy as np
from tensorflow.keras.models import load_model
from tensorflow.keras.losses import MeanSquaredError
mse = MeanSquaredError()
model = load_model('REG_houses.h5', custom_objects={'mse': mse})
# Funci贸n para preprocesar los datos de entrada
def preprocess_input(values):
# Aseg煤rate de que los valores est茅n en el formato correcto
return np.array([values], dtype=np.float32)
# Interfaz de usuario
st.title('Predicci贸n de Precios de Viviendas')
# Campos de entrada para cada par谩metro
#price = st.number_input('Price', format="%.2f")
area = st.number_input('Area', format="%.2f")
bedrooms = st.number_input('Bedrooms', format="%.2f")
bathrooms = st.number_input('Bathrooms', format="%.2f")
stories = st.number_input('Stories', format="%.2f")
parking = st.number_input('Parking', format="%.2f")
mainroad_yes = st.number_input('Mainroad (1=Yes, 0=No)', format="%.2f")
guestroom_yes = st.number_input('Guestroom (1=Yes, 0=No)', format="%.2f")
basement_yes = st.number_input('Basement (1=Yes, 0=No)', format="%.2f")
hotwaterheating_yes = st.number_input('Hot Water Heating (1=Yes, 0=No)', format="%.2f")
airconditioning_yes = st.number_input('Air Conditioning (1=Yes, 0=No)', format="%.2f")
prefarea_yes = st.number_input('Preferred Area (1=Yes, 0=No)', format="%.2f")
furnishingstatus_semi_furnished = st.number_input('Furnishing Status (Semi-Furnished: 1, Unfurnished: 0)', format="%.2f")
furnishingstatus_unfurnished = st.number_input('Furnishing Status (Unfurnished: 1, Semi-Furnished: 0)', format="%.2f")
# Crear una lista con los valores ingresados
input_values = [ area, bedrooms, bathrooms, stories, parking,
mainroad_yes, guestroom_yes, basement_yes,
hotwaterheating_yes, airconditioning_yes, prefarea_yes,
furnishingstatus_semi_furnished, furnishingstatus_unfurnished
]
if st.button('Predecir Precio'):
# Preprocesar los datos de entrada
processed_input = preprocess_input(input_values)
# Realizar la predicci贸n
prediction = model.predict(processed_input)
# Mostrar el resultado
st.write(f'Valor: ${prediction[0][0]:.2f}') |