Update app.py

app.py

@@ -5,27 +5,18 @@ import numpy as np
 sess = ort.InferenceSession("collusion_xgb_model.onnx")
 
 def predict(amount, user_txn, driver_txn, pair_count, hour, day):
-    X = np.array([[amount, user_txn, driver_txn, pair_count, hour, day]], dtype=np.float32)
+    X = np.array(
+        [[amount, user_txn, driver_txn, pair_count, hour, day]],
+        dtype=np.float32
+    )
+
     outputs = sess.run(None, {sess.get_inputs()[0].name: X})
-    scores = outputs[0]
 
-    if scores.ndim == 2 and scores.shape[1] == 2:
-        fraud_prob = scores[0][1]
-    else:
-        fraud_prob = scores[0][0]
+    # IMPORTANT:
+    # outputs[0] = label (0 or 1)
+    # outputs[1] = probability [p0, p1]
+    probs = outputs[1]
 
-    return float(fraud_prob)
+    fraud_prob = probs[0][1]   # class-1 (fraud)
 
-gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Number(label="Amount"),
-        gr.Number(label="User Txn Count"),
-        gr.Number(label="Driver Txn Count"),
-        gr.Number(label="User-Driver Pair Count"),
-        gr.Number(label="Hour"),
-        gr.Number(label="Day of Week"),
-    ],
-    outputs=gr.Number(label="Fraud Probability"),
-    title="Collusion Fraud Detection (ONNX)"
-).launch()
+    return float(fraud_prob)