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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)}
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