add app file

app.py

@@ -0,0 +1,66 @@
+
+import gradio as gr
+import joblib
+import pandas as pd
+import numpy as np
+
+# Load model bundle
+bundle = joblib.load("rf_model_bundle.pkl")
+model = bundle["model"]
+threshold = bundle["threshold"]
+
+# Prediction function
+def predict_fraud(step, amount, oldbalanceOrig, newbalanceOrig,
+                  oldbalanceDest, newbalanceDest,
+                  nameDest_freq, nameDest_fraud_rate,
+                  type, OrigEmptyBefore, OrigEmptyAfter, DestEmptyBefore, DestEmptyAfter):
+
+    balanceChangeOrig = oldbalanceOrig - newbalanceOrig
+    balanceChangeDest = newbalanceDest - oldbalanceDest
+
+    X = pd.DataFrame([{
+        "step": step,
+        "amount": amount,
+        "oldbalanceOrig": oldbalanceOrig,
+        "newbalanceOrig": newbalanceOrig,
+        "oldbalanceDest": oldbalanceDest,
+        "newbalanceDest": newbalanceDest,
+        "balanceChangeOrig": balanceChangeOrig,
+        "balanceChangeDest": balanceChangeDest,
+        "nameDest_freq": nameDest_freq,
+        "nameDest_fraud_rate": nameDest_fraud_rate,
+        "type": type,
+        "OrigEmptyBefore": OrigEmptyBefore,
+        "OrigEmptyAfter": OrigEmptyAfter,
+        "DestEmptyBefore": DestEmptyBefore,
+        "DestEmptyAfter": DestEmptyAfter
+    }])
+
+    prob = model.predict_proba(X)[0][1]
+    pred = int(prob >= threshold)
+    return f"{'🚨 Fraud' if pred else '✅ Not Fraud'} (Probability: {prob:.2f})"
+
+# Gradio UI
+demo = gr.Interface(
+    fn=predict_fraud,
+    inputs=[
+        gr.Number(label="Transaction Step", info="Time unit since system start"),
+        gr.Number(label="Transaction Amount ($)", info="Total amount of the transaction"),
+        gr.Number(label="Sender's Balance Before", info="Balance before transaction"),
+        gr.Number(label="Sender's Balance After", info="Balance after transaction"),
+        gr.Number(label="Recipient's Balance Before", info="Recipient balance before transaction"),
+        gr.Number(label="Recipient's Balance After", info="Recipient balance after transaction"),
+        gr.Number(label="Recipient Account Frequency", info="Number of prior transactions to recipient"),
+        gr.Number(label="Recipient Fraud Rate", info="Historical fraud rate for recipient (0–1)"),
+        gr.Radio(["CASH_OUT", "TRANSFER", "PAYMENT", "CASH_IN"], label="Transaction Type", info="CASH_OUT & TRANSFER are riskier"),
+        gr.Radio([0, 1], label="Sender Balance Empty Before?", info="1 = Yes, 0 = No"),
+        gr.Radio([0, 1], label="Sender Balance Empty After?", info="1 = Yes, 0 = No"),
+        gr.Radio([0, 1], label="Recipient Balance Empty Before?", info="1 = Yes, 0 = No"),
+        gr.Radio([0, 1], label="Recipient Balance Empty After?", info="1 = Yes, 0 = No"),
+    ],
+    outputs="text",
+    title="💸 Fraud Detection App (Random Forest)",
+    description="Enter transaction data to predict the likelihood of fraud using a trained ML model.",
+)
+
+demo.launch()