# 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 sklearn.inspection import PartialDependenceDisplay
# 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(features, kind):
    """ 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))
    PartialDependenceDisplay.from_estimator(global_model, X_test, features, kind=kind)
    
    st.pyplot(fig)

def main():
    global global_model
    
    # Ensure the model is trained only once
    if global_model is None:
        train_model()
    # Define feature names
    

    selected_feature = st.sidebar.selectbox("Select a feature for PDP/ICE analysis:", ("Age", "Workclass", "Education-Num", "Marital Status", "Occupation",
                     "Relationship", "Race", "Sex", "Capital Gain", "Capital Loss", "Hours per week", "Country"),)
    print(f"selected feature is {selected_feature}")
    kind = st.sidebar.selectbox("Select plot type:", ("individual", "average", "both"),)
    
    st.title("ICE and PDP")
    st.write(prompt_params.ICE_INTRODUCTION)
    # Explain the selected sample
    if st.button("Explain Sample"):
        explain_example("Age", kind)


if __name__ == '__main__':
    main()