# 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

# 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")
    # ale_plot(
    #     global_model,
    #     X_test,
    #     "Age",
    #     bins=5,
    #     monte_carlo=True,
    #     monte_carlo_rep=30,
    #     monte_carlo_ratio=0.5,
    # )
    #
    # st.pyplot(fig)

    fig1, ax1 = plt.subplots(figsize=(10, 5))
    st.write("2D Second-Order ALE Plot")
    ale_plot(global_model, X_test, X_train.columns[:2], bins=10)
    st.pyplot(fig1)

def main():
    global global_model
    
    # Ensure the model is trained only once
    if global_model is None:
        train_model()
    
    st.title("ALE (Accumulated Local Effects)")
    st.write(prompt_params.ALE_INTRODUCTION)
    st.write("now has bug, waiting for fix")
    # Explain the selected sample
    if st.button("Explain Sample"):
        explain_example()


if __name__ == '__main__':
    main()