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import streamlit as st
import pandas as pd
import pickle
import lime
import lime.lime_tabular
import streamlit.components.v1 as components
# Load your trained model
with open('model.pkl', 'rb') as file:
model = pickle.load(file)
obesity_mapping = {
0: 'Normal',
1: 'Surpoid/Obése'
}
# Define the input features for the user to input
def user_input_features():
age = st.number_input('Age:',min_value=8, max_value=100, value=24, step=1, format="%d")
classe = st.radio('Classe_', ('Primaire','Secondaire'))
Zone = st.radio('zone', ('Rurale', 'Urbaine'))
Diversité = st.radio('Consumption of food between meals (CAEC)', ('Mauvaise', 'Bonne'))
Region = st.selectbox(
'Region de ',
('Nord_ouest' ,'Sud_ouest', '1Ouest')
)
Sexe = st.radio('Genre', ('F', 'M'))
Zone = 1 if Zone == 'Rurale' else 0
classe = 1 if classe == 'Primaire' else 0
Diversité = 1 if Diversité == 'Mauvaise' else 0
Region = ['Nord_ouest' ,'Sud_ouest', '1Ouest'].index(Region)
sex_f = 1 if Sexe == 'F' else 0
sex_m = 1 if Sexe == 'M' else 0
data = {
'Region': Region,
'Zone': Zone,
'Classe': classe,
'Age': age,
'Diversité': Diversité,
'Genre_F': sex_f,
'Genre_M': sex_m
}
features = pd.DataFrame(data, index=[0])
return features
st.title('Obesity App')
# Display the input fields
input_df = user_input_features()
# Initialiser LIME
explainer = lime.lime_tabular.LimeTabularExplainer(
training_data=input_df.values, # Entraînement sur la base des données d'entrée
feature_names=input_df.columns,
class_names=[obesity_mapping[0], obesity_mapping[1]],
mode='classification'
)
# Predict button
if st.button('Predict'):
# Make a prediction
prediction = model.predict(input_df)
prediction_proba = model.predict_proba(input_df)[0]
data = {
'Obesity Type': [obesity_mapping[i] for i in range(len(prediction_proba))],
'Probability': prediction_proba
}
# Create a dataframe to display the results
result_df = pd.DataFrame(data)
# Transpose the dataframe to have obesity types as columns and add a row header
result_df = result_df.T
result_df.columns = result_df.iloc[0]
result_df = result_df.drop(result_df.index[0])
result_df.index = ['Probability']
# Display the results in a table with proper formatting
st.table(result_df.style.format("{:.4f}"))
# Générer l'explication LIME pour l'individu
# exp = explainer.explain_instance(input_df.values[0], model.predict_proba, num_features=5)
# # Afficher les explications dans Streamlit
# st.subheader('Explication LIME')
# exp.show_in_notebook(show_table=True, show_all=False)
# st.write(exp.as_list())
# Générer l'explication LIME pour l'individu
exp = explainer.explain_instance(input_df.values[0], model.predict_proba, num_features=4)
# Récupérer l'explication LIME sous forme HTML
explanation_html = exp.as_html()
# Afficher l'explication LIME dans Streamlit
st.subheader('Explication LIME')
# Utiliser Streamlit pour afficher du HTML
components.html(explanation_html, height=800) # Ajuster la hauteur selon le contenu
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