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| import streamlit as st | |
| import numpy as np | |
| import pickle | |
| import streamlit.components.v1 as components | |
| from sklearn.preprocessing import LabelEncoder | |
| # Load the pickled model | |
| def load_model(): | |
| return pickle.load(open('online_payment_fraud_detection_randomforest.pkl', 'rb')) | |
| # Load the LabelEncoder | |
| def load_label_encoder(): | |
| with open('label_encoder.pkl', 'rb') as f: | |
| return pickle.load(f) | |
| # Function for model prediction | |
| def model_prediction(model, features): | |
| predicted = str(model.predict(features)[0]) | |
| return predicted | |
| def transform(le, text): | |
| text = le.transform(text) | |
| return text[0] | |
| def app_design(le): | |
| # Add input fields for High, Open, and Low values | |
| image = 'Ramdevs2' | |
| st.image(image, use_column_width=True) | |
| st.subheader("Enter the following values:") | |
| step = st.number_input("Step: represents a unit of time where 1 step equals 1 hour") | |
| typeup = st.selectbox('Type of online transaction', ('PAYMENT', 'TRANSFER', 'CASH_OUT', 'DEBIT', 'CASH_IN')) | |
| typeup = transform(le, [typeup]) | |
| amount = st.number_input("The amount of the transaction") | |
| nameOrig = st.text_input("Transaction ID") | |
| nameOrig = transform(le, [nameOrig]) | |
| oldbalanceOrg = st.number_input("Balance before the transaction") | |
| newbalanceOrig = st.number_input("Balance after the transaction") | |
| nameDest = st.text_input("Recipient ID") | |
| nameDest = transform(le, [nameDest]) | |
| oldbalanceDest = st.number_input("Initial balance of recipient before the transaction") | |
| newbalanceDest = st.number_input("The new balance of recipient after the transaction") | |
| isFlaggedFraud = st.selectbox('IsFlaggedFraud', ('Yes', 'No')) | |
| isFlaggedFraud = transform(le, [isFlaggedFraud]) | |
| # Create a feature list from the user inputs | |
| features = [[step, typeup, amount, nameOrig, oldbalanceOrg, newbalanceOrig, nameDest, oldbalanceDest, newbalanceDest, isFlaggedFraud]] | |
| # Load the model | |
| model = load_model() | |
| # Make a prediction when the user clicks the "Predict" button | |
| if st.button('Predict Online Payment Fraud'): | |
| predicted_value = model_prediction(model, features) | |
| if predicted_value == '1': | |
| st.success("Online payment fraud not happened") | |
| else: | |
| st.success("Online payment fraud happened") | |
| def about_RamDevs(): | |
| components.html(""" | |
| <div> | |
| #bla bla bla | |
| </div> | |
| """, height=600) | |
| def main(): | |
| st.set_page_config(page_title="Online Payment Fraud Detection", page_icon=":chart_with_upwards_trend:") | |
| st.title("Welcome to our Online Payment Fraud Detection App!") | |
| le = load_label_encoder() | |
| app_design(le) | |
| st.header("About RamDevs Community") | |
| about_RamDevs() | |
| if __name__ == '__main__': | |
| main() |