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| # -*- coding: utf-8 -*- | |
| """ | |
| Created on Fri May 19 01:25:27 2023 | |
| @author: ME | |
| """ | |
| import streamlit as st | |
| import numpy as np | |
| from src.preprocessor import transform_single_data_point | |
| import joblib | |
| import xgboost as xgb | |
| """ | |
| 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() |