app.py

import streamlit as st
import pandas as pd
import joblib

st.title('Fraud Detection')

st.write('Please review the attributes below, then hit the Submit button to get your results.')

st.header('Input Attributes')

# Define the sliders with the specified range
time = st.number_input('Time', min_value=0.0, max_value=20000.0, value=472.0, step=1.0)
v3 = st.number_input('V3', min_value=-4.0, max_value=4.0, value=1.088, step=0.001)
v7 = st.number_input('V7', min_value=-4.0, max_value=4.0, value=0.325, step=0.001)
v10 = st.number_input('V10', min_value=-4.0, max_value=4.0, value=-0.838,  step=0.001)
v12 = st.number_input('V12', min_value=-4.0, max_value=4.0, value=-0.414, step=0.001)
v14 = st.number_input('V14', min_value=-4.0, max_value=4.0, value=-0.503, step=0.001)
v16 = st.number_input('V16', min_value=-4.0, max_value=4.0, value=-1.692, step=0.001)
v17 = st.number_input('V17', min_value=-4.0, max_value=4.0, value=0.666, step=0.001)
amount = st.number_input('Amount', min_value=1.0, max_value=30000.0, value=529.0, step=0.1)

submit_button = st.button('Submit')

if submit_button:
    try:
        # Load the model from the file
        ensemble_model = joblib.load('updated_ensemble.pkl') 

        # Create new data with the specified columns
        new_data = pd.DataFrame({
            'Time': [time],
            'V3': [v3],
            'V7': [v7],
            'V10': [v10],
            'V12': [v12],
            'V14': [v14],
            'V16': [v16],
            'V17': [v17],
            'Amount': [amount]
        })

        # Predict using the ensemble model
        prediction = ensemble_model.predict(new_data)

        st.header('Prediction')

        if prediction[0] == 0:
            st.title('Not Fraud')
        else:
            st.title('Fraud')
    except Exception as e:
        st.error(f"An error occurred: {e}")
        st.error("Ensure the model file is in the correct path")