#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} $")