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app.py

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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
+

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:28aa471b6ae23de5abe249cfe72e5bfe8240d72e942feeacb56980d1aed7020c
+size 335533

requirements.txt

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+streamlit==1.37.1
+streamlit-option-menu==0.3.13
+pandas==2.2.0
+lime==0.2.0.1
+Pillow==9.2.0
+seaborn==0.12.2
+matplotlib==3.7.1
+xgboost==2.0.3
+numpy==1.26.3
+openpyxl=3.1.5
+