Create predict.py

predict.py

@@ -0,0 +1,25 @@
+from fastapi import HTTPException
+import pandas as pd
+import joblib
+from validate import TransactionData
+from utils import create_text_input
+
+MODEL_PATH = "models/logreg_model.pkl"
+
+def predict(request: TransactionData):
+    try:
+        model = joblib.load(MODEL_PATH)
+        input_df = pd.DataFrame([request.dict()]).fillna("")
+        text_input = create_text_input(input_df.iloc[0])
+        prediction = model.predict([text_input])[0]
+
+        return {
+            "Maker_Action": prediction[0],
+            "Escalation_Level": prediction[1],
+            "Risk_Category": prediction[2],
+            "Risk_Drivers": prediction[3],
+            "Investigation_Outcome": prediction[4],
+            "Alert_Status": prediction[5]
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))