app.py

import gradio as gr
import joblib

# Load the model and scaler
model_artifacts = joblib.load("knn_model_with_scaler.pkl")
knn = model_artifacts["model"]
scaler = model_artifacts["scaler"]

# Define the prediction function
def classify_instance(account_age, freq_credential_change, return_to_order, vpn_temp_mail, credit_score):
    # Create a new instance from inputs
    new_instance = [account_age, freq_credential_change, return_to_order, vpn_temp_mail, credit_score]
    new_instance_scaled = scaler.transform([new_instance])  # Scale the input
    prediction = knn.predict(new_instance_scaled)  # Predict using the KNN model
    return "Fraud" if prediction[0] == 1 else "Not Fraud"

# Define the Gradio interface
inputs = [
    gr.Number(label="Account Age (months)"),
    gr.Number(label="Frequency of Credential Change (per year)"),
    gr.Number(label="Return to Order Ratio"),
    gr.Radio(["No", "Yes"], label="VPN/Temp Mail?", type="index"),
    gr.Number(label="Credit Score")
]
outputs = gr.Textbox(label="Prediction")

gr.Interface(
    fn=classify_instance,
    inputs=inputs,
    outputs=outputs,
    title="Fraud Detection KNN Model",
    description="Enter the attributes of a transaction to predict if it's fraud or not."
).launch()