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)}