import pickle
import pandas as pd
import shap
from shap.plots._force_matplotlib import draw_additive_plot
import gradio as gr
import numpy as np
import matplotlib.pyplot as plt

# load the model from disk
loaded_model = pickle.load(open("filtered_xgb_model.pkl", 'rb'))

# Setup SHAP
explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.

example_options = {
    "🟢 Well Engaged": [4.9, 5, 5, 4.9, 5, 5],
    "🟡 Marginal": [5, 4.6, 5, 5, 5, 4.7],
    "🔴 At Risk": [4.5, 4.7, 4.8, 4.5, 4.7, 4.5]
}

# Function to apply the example values
def fill_example(example_label):
    return example_options[example_label]

# Create the main function for server
def main_func(GM3, WorkEnv3, WellBeing2, GM2, JobSecurity, WellBeing1):
    new_row = pd.DataFrame.from_dict({
        'GM3': GM3,
        'WorkEnv3': WorkEnv3,
        'WellBeing2': WellBeing2,
        'GM2': GM2,
        'JobSecurity': JobSecurity,
        'WellBeing1': WellBeing1
    }, orient='index').transpose()
    
    prob = loaded_model.predict_proba(new_row)
    
    shap_values = explainer(new_row)
    # plot = shap.force_plot(shap_values[0], matplotlib=True, figsize=(30,30), show=False)
    # plot = shap.plots.waterfall(shap_values[0], max_display=6, show=False)
    plot = shap.plots.bar(shap_values[0], max_display=6, order=shap.Explanation.abs, show_data='auto', show=False)

    plt.tight_layout()
    local_plot = plt.gcf()
    plt.rcParams['figure.figsize'] = 6,4
    plt.close()
    
    return {"Leave": float(prob[0][0]), "Stay": 1-float(prob[0][0])}, local_plot

with gr.Blocks(title="🌟 Intent to Stay Prediction") as demo:
    gr.Image("https://1000logos.net/wp-content/uploads/2017/02/Font-Hilton-Logo.jpg", elem_id="banner")
    gr.Markdown("# 🌟 Employee Retention Predictor")
    gr.Markdown("Predict if an employee will **Stay** or **Leave** based on key workplace factors.")
    gr.Markdown("---")

    with gr.Row():        
        with gr.Column():
            GM3 = gr.Slider(label="👨‍💼 My General Manager is an effective leader", minimum=1, maximum=5, value=4, step=0.1)
            WorkEnv3 = gr.Slider(label="🏢 My Work Environment is comfortable and welcoming", minimum=1, maximum=5, value=4, step=0.1)
            WellBeing2 = gr.Slider(label="💖 I feel balanced and healthy", minimum=1, maximum=5, value=4, step=0.1)
            GM2 = gr.Slider(label="📊 My General Manager uses feedback from Team Members", minimum=1, maximum=5, value=4, step=0.1)
            JobSecurity = gr.Slider(label="🔐 Job Security", minimum=1, maximum=5, value=4, step=0.1)
            WellBeing1 = gr.Slider(label="🧠 My mental health is good", minimum=1, maximum=5, value=4, step=0.1)
            submit_btn = gr.Button("🚀 Analyze Now", variant="primary")

        with gr.Column():
            label = gr.Label(label="🔮 Prediction Result")
            local_plot = gr.Plot(label="SHAP Analysis")

    # Dropdown for labeled examples
    gr.Markdown("### 🏷️ Select an Example:")
    example_dropdown = gr.Dropdown(
        label="Choose a scenario",
        choices=list(example_options.keys())
    )

    # Apply example values when selected
    example_dropdown.change(
        fill_example, 
        inputs=[example_dropdown], 
        outputs=[GM3, WorkEnv3, WellBeing2, GM2, JobSecurity, WellBeing1]
    )

    # Submit button functionality
    submit_btn.click(main_func, [GM3, WorkEnv3, WellBeing2, GM2, JobSecurity, WellBeing1], [label, local_plot])

demo.launch()