Delete app.py

app.py

@@ -1,102 +0,0 @@
-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
-