Update app.py

app.py

@@ -7,20 +7,10 @@ from huggingface_hub import hf_hub_download
 REPO = "gabrielnkl/model-fraud-detect"
 MODEL = "modelo_fraude.pkl"
 
-# download model
 model_path = hf_hub_download(repo_id=REPO, filename=MODEL)
-
-# load sklearn model
 model = joblib.load(model_path)
 
-TYPE_MAP = {
-    "PAYMENT": 0,
-    "TRANSFER": 1,
-    "CASH_OUT": 2,
-    "DEBIT": 3,
-    "CASH_IN": 4
-}
-
+TYPES = ["TRANSFER","CASH_OUT","PAYMENT","DEBIT","CASH_IN"]
 
 def predict(json_input):
 
@@ -30,9 +20,26 @@ def predict(json_input):
         amount = float(d["amount"])
         old = float(d["oldbalanceOrg"])
         new = float(d["newbalanceOrig"])
-        t = TYPE_MAP[d["type"]]
+        t = d["type"]
+
+        # engineered features
+        errorBalanceOrig = old - new - amount
+        balanceDifference = old - new
+
+        # base numerical features
+        features = [
+            amount,
+            old,
+            new,
+            errorBalanceOrig,
+            balanceDifference
+        ]
+
+        # one-hot type encoding (fixed order)
+        for typ in TYPES:
+            features.append(1.0 if t == typ else 0.0)
 
-        X = np.array([[amount, old, new, t]], dtype=float)
+        X = np.array([features], dtype=float)
 
         prob = float(model.predict_proba(X)[0][1])
         pred = int(model.predict(X)[0])
@@ -55,4 +62,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch(server_name="0.0.0.0", server_port=7860)
+    iface.launch(server_name="0.0.0.0", server_port=7860)