app.py

# --- top of file imports (keep your existing imports) ---
import os, sys, traceback, joblib, numpy as np, pandas as pd, gradio as gr

MODEL_PATH = "model4_random_forest.pkl"

# keep your load_model_joblib() implementation unchanged here
# (paste the function you already have)

_model = None  # global cache

def get_model():
    global _model
    if _model is not None:
        return _model
    try:
        _model = load_model_joblib(MODEL_PATH)
        print("Model loaded OK. Type:", type(_model), file=sys.stderr)
        return _model
    except Exception as e:
        print("ERROR LOADING MODEL AT RUNTIME:", e, file=sys.stderr)
        traceback.print_exc()
        _model = None
        return None

def predict(step, tx_type, amount, oldbalanceOrg, oldbalanceDest):
    try:
        model = get_model()
        if model is None:
            raise RuntimeError("Model unavailable on the server. Check Space logs for load error.")
        # ... rest of your predict body unchanged ...
        # map type -> tx_mapped
        if isinstance(tx_type, str):
            tx_str = tx_type.strip().upper()
            if tx_str in TYPE_MAP:
                tx_mapped = TYPE_MAP[tx_str]
            else:
                try:
                    tx_mapped = int(tx_type)
                except:
                    raise ValueError(f"Unknown type: {tx_type}. Expected one of {list(TYPE_MAP.keys())} or integer 0-4.")
        else:
            tx_mapped = int(tx_type)

        df = pd.DataFrame([{
            "step": int(step),
            "type": int(tx_mapped),
            "amount": float(amount),
            "oldbalanceOrg": float(oldbalanceOrg),
            "oldbalanceDest": float(oldbalanceDest)
        }])
        X = df[["step","type","amount","oldbalanceOrg","oldbalanceDest"]].values
        preds = model.predict(X)
        pred0 = preds[0].item() if hasattr(preds[0], "item") else preds[0]
        probs = model.predict_proba(X)[0].tolist() if hasattr(model, "predict_proba") else None
        return {"prediction": int(pred0), "probabilities": probs}
    except Exception as e:
        tb = traceback.format_exc()
        print("PREDICTION ERROR:", tb, file=sys.stderr)
        return {"error": str(e)}