Update app.py

app.py

@@ -2,39 +2,58 @@ import onnxruntime as ort
 import gradio as gr
 import numpy as np
 
-# Load ONNX model
+# ------------------------------------------------
+# Load ONNX model (CPU only)
+# ------------------------------------------------
 sess = ort.InferenceSession(
     "collusion_xgb_model.onnx",
     providers=["CPUExecutionProvider"]
 )
 
-# Detect probability output safely
+# ------------------------------------------------
+# Detect probability output tensor safely
+# ------------------------------------------------
 output_info = sess.get_outputs()
 PROB_INDEX = None
+
 for i, out in enumerate(output_info):
     if len(out.shape) == 2 and out.shape[1] == 2:
         PROB_INDEX = i
         break
 
 if PROB_INDEX is None:
-    raise RuntimeError("Probability output not found in ONNX model")
+    raise RuntimeError("❌ Probability output not found in ONNX model")
 
 print(f"✅ Using output index {PROB_INDEX} as probability tensor")
 
+# ------------------------------------------------
+# Risk bucket logic (PRODUCT FRIENDLY)
+# ------------------------------------------------
+def risk_bucket(p):
+    if p >= 0.05:
+        return "HIGH RISK"
+    elif p >= 0.01:
+        return "MEDIUM RISK"
+    else:
+        return "LOW RISK"
 
+# ------------------------------------------------
+# Prediction function
+# ------------------------------------------------
 def predict(amount, user_txn, driver_txn, pair_count, hour, day):
-    # IMPORTANT: feature order MUST match training
-    # Training features:
-    # [amount, user_txn_count, driver_txn_count, user_driver_pair_count, hour, day_of_week]
+    """
+    Feature order MUST match training:
+    [amount, user_txn_count, driver_txn_count, user_driver_pair_count, hour, day_of_week]
+    """
 
     X = np.array(
         [[
-            amount,
-            user_txn,
-            driver_txn,
-            pair_count,
-            hour,
-            day
+            float(amount),
+            float(user_txn),
+            float(driver_txn),
+            float(pair_count),
+            float(hour),
+            float(day)
         ]],
         dtype=np.float32
     )
@@ -42,10 +61,17 @@ def predict(amount, user_txn, driver_txn, pair_count, hour, day):
     outputs = sess.run(None, {sess.get_inputs()[0].name: X})
     probs = outputs[PROB_INDEX]
 
-    fraud_prob = probs[0][1]  # class-1 probability
-    return float(fraud_prob)
+    fraud_prob = float(probs[0][1])  # class-1 probability
+
+    # Convert to interpretable risk
+    risk = risk_bucket(fraud_prob)
+    risk_score = int(fraud_prob * 1000)  # scaled score for visibility
 
+    return fraud_prob, risk_score, risk
 
+# ------------------------------------------------
+# Gradio UI
+# ------------------------------------------------
 gr.Interface(
     fn=predict,
     inputs=[
@@ -53,9 +79,17 @@ gr.Interface(
         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"),
+        gr.Number(label="Hour (0–23)"),
+        gr.Number(label="Day of Week (0=Mon, 6=Sun)"),
+    ],
+    outputs=[
+        gr.Number(label="Fraud Probability"),
+        gr.Number(label="Risk Score (0–1000)"),
+        gr.Text(label="Risk Level"),
     ],
-    outputs=gr.Number(label="Fraud Probability"),
     title="Collusion Fraud Detection (ONNX)",
+    description=(
+        "This model detects potential user–driver collusion. "
+        "Fraud probability is a ranking signal; risk score and bucket are used for decisions."
+    ),
 ).launch()