Create app.py

app.py

@@ -0,0 +1,43 @@
+import os
+import json
+import joblib
+import numpy as np
+import onnxruntime as ort
+from fastapi import FastAPI
+
+MODEL_PATH = "models/fraud_model.onnx"
+SCALER_PATH = "models/scaler.joblib"
+META_PATH = "models/metadata.json"
+
+# Fail fast if files missing
+for p in [MODEL_PATH, SCALER_PATH, META_PATH]:
+    if not os.path.exists(p):
+        raise FileNotFoundError(f"Missing file: {p}")
+
+scaler = joblib.load(SCALER_PATH)
+
+with open(META_PATH) as f:
+    meta = json.load(f)
+
+num_features = meta["num_features"]
+
+session = ort.InferenceSession(
+    MODEL_PATH,
+    providers=["CPUExecutionProvider"]
+)
+
+app = FastAPI(title="Fraud Detection API")
+
+@app.get("/")
+def health():
+    return {"status": "running"}
+
+@app.post("/predict")
+def predict(payload: dict):
+    x = np.array([[payload[f] for f in num_features]], dtype=np.float32)
+    x = scaler.transform(x)
+
+    input_name = session.get_inputs()[0].name
+    prob = session.run(None, {input_name: x})[0][0][0]
+
+    return {"fraud_probability": float(prob)}