# -*- coding: utf-8 -*-
"""
Created on Fri May 19 01:25:27 2023

@author: ME
"""

import streamlit as st
import json
import pickle
import numpy as np
import os
import itertools
from src.preprocessor import transform_single_data_point
import joblib
import xgboost as xgb

v = st.__version__

print(v)
"""
STREAMLIT INTERFACE
"""

#load model
model_path = "Artifacts/xgboost_model.model"
# Load the model
loaded_model = xgb.XGBClassifier()
loaded_model.load_model(model_path)

#load preprocessor object
preprocessor_path = "Artifacts/preprocessor.pkl"
preprocessor_obj = joblib.load(preprocessor_path)

def main():
    # Face Analysis Application #
    st.title("Credit card fraud detector : Predicting fraudlent transactions by customers")
    activities = ["Home","Predict Transaction"]
    choice = st.sidebar.selectbox("Select Activity", activities)
    st.sidebar.markdown(
        """ Developed by as a project 
            Email me  @ :
            """)
    if choice == "Home":
        html_temp_home1 = """<div style="background-color:#6D7B8D;padding:10px">
                                            <h4 style="color:white;text-align:center;">
                                            Definition:Detecting fraud early is vital to prevent financial losses and protect businesses and individuals by addressing fraudulent activities promptly..</h4>
                                            </div>
                                            </br>"""
        st.markdown(html_temp_home1, unsafe_allow_html=True)
        st.write("""The main function of this application is to predict the likelihood of a transaction being fraudlent with few questions""")
    elif choice == "Predict Transaction":
         
           
         #amount 
         amount  = st.number_input('Enter the amount of transaction made in local currency :')
         
         #olf balance 
         oldbalanceOrg  = st.number_input('Enter the initial balance of customer before transaction :')
         
         
         #new  balance of customer 
         newbalanceOrig  = st.number_input('Enter the new  balance of customer after transaction :')
     
         
         #old  balance of recippient
         oldbalanceDest  = st.number_input('Enter the initial balance of recipient  before transaction :')
        
         
         #new  balance of customer 
         newbalanceDest  = st.number_input('Enter the new  balance of recipient after transaction :')
        
         #new  balance of customer 
         transferAmt = st.number_input('Enter difference between old and new balance :')
        
         
         
         
         #select type of transaction
         t_type = st.selectbox("Select transaction type?",tuple(["CASH_IN",
                                                                   "CASH_OUT",
                                                                   "PAYMENT",
                                                                   "DEBIT",
                                                                   "TRANSFER"
             ]))
        

         
         
         single_data_point = {"amount":amount,
                              "oldbalanceOrg":oldbalanceOrg,
                              "newbalanceOrig":newbalanceOrig,
                              "oldbalanceDest":oldbalanceDest,
                              "newbalanceDest":newbalanceDest,
                              "transferAmt":transferAmt,
                              "type":t_type}
        
         tranformed_single_data = transform_single_data_point(single_data = single_data_point,preprocessing_obj = preprocessor_obj)
         
         
         
         
        
         
         #Prediction button
         if st.button("Predict"):
            output = loaded_model.predict(tranformed_single_data)
             
            if output == 0:
                st.write("This is a normal transaction")
                 
            else:
                st.write("A likelihood  of this transaction being fraudlent is detected -- More investigations should be done")
          
             
                          
             
             
         
        



        


if __name__ == "__main__":
    main()