Create app.py

app.py

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+#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} $")