import gradio as gr
import shap
import pickle
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path

# load our models 
boost_path = Path(__file__).parents[0] / "boost.sav"
boost = pickle.load(open(boost_path,"rb"))

# functions

# preprocessing data function
def preprocess(data):
    columns = ['distance_from_home', 'distance_from_last_transaction',
                'ratio_to_median_purchase_price', 'repeat_retailer', 'used_chip',
                'used_pin_number', 'online_order']
    
    df = pd.DataFrame([data], columns = columns)
    
    # convert data type
    df[['repeat_retailer','used_chip','used_pin_number','online_order']] = df[['repeat_retailer','used_chip','used_pin_number','online_order']].astype('int')
    
    return df


# Prediction function with probabilities
def predict(*data):
        df = preprocess(data)
        prob_pred = boost.predict_proba(df)
        return {"Normal": float(prob_pred[0][0]), "Fraud": float(prob_pred[0][1])}

# plot function
def interpret(*data):
        plt.style.use("fivethirtyeight")
        
        df = preprocess(data)
        
        explainer = shap.TreeExplainer(boost)
        shap_values = explainer.shap_values(df)
        scores_desc = list(zip(shap_values[0], df.columns))
        scores_desc = sorted(scores_desc)
        fig_m = plt.figure(tight_layout=True)
        plt.barh([s[1] for s in scores_desc], [s[0] for s in scores_desc])
        plt.title("Feature Shap Values")
        plt.ylabel("Shap Value")
        plt.xlabel("Feature Importance")
        plt.tight_layout()
        
        return fig_m
    
    
    
with gr.Blocks() as demo:
    gr.HTML("""
    <h1 align="center">Credit Card Fraud Prediction System</h1>
    """)
    with gr.Row():
        with gr.Column():
            repeated_retailer = gr.Radio(["No","Yes"], type = "index", label = "Repeat Retailer", info ="Was the transaction at at a repeated store?")
            
            online_order = gr.Radio(["No","Yes"], type = "index", label = "Online Order", info ="Was the transaction an online order?")
            
            used_chip = gr.Radio(["No","Yes"], type = "index", label = "Used Chip", info ="did the purchase use the security chip of the card?")

            used_pin = gr.Radio(["No","Yes"], type = "index", label = "Used Pin Number", info ="Did the transaction use the pin code of the card?")
            
            distance_home = gr.Number(value = 25, label = "Distance From Home (miles)", info = "How far was the transaction from the card owner's house? (in Miles)")
            
            distance_last = gr.Number(value = 5, label = "Distance From Last Transaction (miles)", info = "How far away was the it from the last transaction that happened? (in Miles)")
            
            gr.HTML("""
                <h4 align="center">Ratio Median Purchase Price Equation</h4>
                    """)
            ratio_median = gr.Number(value = 1.8, label = "Ratio Median Purchase Price", info = "Divide the purchase price by card owners median purchase price?")
        
            
        with gr.Column():
            label = gr.Label()
            plot = gr.Plot()
            with gr.Row():
                predict_btn = gr.Button(value="Predict")
                interpret_btn = gr.Button(value="Explain")
            predict_btn.click(
                predict,
                inputs= [
                    distance_home,
                    distance_last,
                    ratio_median,
                    repeated_retailer,
                    used_chip,
                    used_pin,
                    online_order   
                ],
                outputs=[label],
            )
            interpret_btn.click(
                interpret,
                inputs=[
                    distance_home,
                    distance_last,
                    ratio_median,
                    repeated_retailer,
                    used_chip,
                    used_pin,
                    online_order   
                ],
                outputs=[plot],
            )

demo.launch()