# Import Libraries
import matplotlib.pyplot as plt
import streamlit as st
import src.prompt_config as prompt_params
# Models
import xgboost
from sklearn.model_selection import train_test_split
from alepython import ale_plot
# XAI (Explainability)
import shap
from sklearn.inspection import permutation_importance
# Global Variables to Store Model & Data
global_model = None
X_train, X_test, y_train, y_test = None, None, None, None


def train_model():
    """ Train the XGBoost model only once and store it globally. """
    global global_model, X_train, X_test, y_train, y_test
    
    if global_model is None:
        # Load Data from SHAP library
        X, y = shap.datasets.adult()
        
        # Split data
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
        
        # Train XGBoost model
        global_model = xgboost.XGBClassifier()
        global_model.fit(X_train, y_train)
        
        print("XGBoost Model training completed!")

def explain_example():
    """ Explain a given sample without retraining the model. """
    global global_model, X_train, X_test, y_train, y_test
    
    if global_model is None:
        train_model()
    
    fig, ax = plt.subplots(figsize=(10, 5))
    st.write("1D Main Effect ALE Plot")
    perm_imp = permutation_importance(global_model, X_test, y_test,
                                      n_repeats=30,
                                      random_state=0)
    sorted_idx = perm_imp.importances_mean.argsort()
    ax.barh(X_test.columns[sorted_idx], perm_imp.importances[sorted_idx].mean(axis=1).T)
    ax.set_title("Permutation Importances")
    fig.tight_layout()
    st.pyplot(fig)

    fig, ax = plt.subplots(figsize=(10, 5))
    ax.boxplot(perm_imp.importances[sorted_idx].T,
               vert=False, labels=X_test.columns[sorted_idx])
    ax.set_title("Permutation Importances")
    fig.tight_layout()
    st.pyplot(fig)

def main():
    global global_model
    
    # Ensure the model is trained only once
    if global_model is None:
        train_model()
    
    st.title("Permutation Feature Importance")
    st.write(prompt_params.PERMUTATION_INTRODUCTION)
    # Explain the selected sample
    if st.button("Explain Sample"):
        explain_example()


if __name__ == '__main__':
    main()