#Import libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pickle
import streamlit as st

#Charger le modele enregistre
@st.cache_resource
def load_model():
    with open("model.pkl","rb") as file:
        model = pickle.load(file)
    return model
model = load_model()

#Interface utilisateur
st.title('Prediction des charges medicales')
st.write('remplissez les informations ci-dessous pour estimer les charges')

#Entrees utilisateur
age = st.slider('Age',18,100,30)
sex = st.selectbox('Sexe',['Homme','Femme'])
bmi = st.number_input('Indice de masse corporelle (IMC)',10.0,50.0,25.0)
children = st.number_input('Nombre d enfants',0,10,0)
fumeur = st.selectbox('Fumeur?',['Oui','Non'])

#Transformation des donnees
sex = 1 if sex =='Homme' else 0
fumeur = 1 if fumeur =='Oui' else 0

#Prediction
if st.button('Predire'):
    input_data = np.array([[age,sex,bmi,children,fumeur]])
    prediction = model.predict(input_data)[0]
    st.success(f"charges medicales estimee:{prediction:.2f} $")