import gradio as gr
import joblib
import numpy as np
import json
from huggingface_hub import hf_hub_download

REPO = "gabrielnkl/model-fraud-detect"
MODEL = "modelo_fraude.pkl"

model_path = hf_hub_download(repo_id=REPO, filename=MODEL)
model = joblib.load(model_path)

TYPES = ["TRANSFER","CASH_OUT","PAYMENT","DEBIT","CASH_IN"]

THRESHOLD = 0.8   # <<<<<< FRAUD probability cutoff


def predict(json_input):

    try:
        d = json.loads(json_input)

        amount = float(d["amount"])
        old = float(d["oldbalanceOrg"])
        new = float(d["newbalanceOrig"])
        t = d["type"]

        # engineered features
        errorBalanceOrig = old - new - amount
        balanceDifference = old - new

        features = [
            amount,
            old,
            new,
            errorBalanceOrig,
            balanceDifference
        ]

        # one-hot encode transaction type
        for typ in TYPES:
            features.append(1.0 if t == typ else 0.0)

        X = np.array([features], dtype=float)

        prob = float(model.predict_proba(X)[0][1])

        # NEW RULE:
        # classify as fraud ONLY IF prob > 0.80
        pred = int(prob > THRESHOLD)

        return {
            "prediction": pred,
            "fraud_probability": round(prob, 4),
            "threshold": THRESHOLD
        }

    except Exception as e:
        return {"error": str(e)}


iface = gr.Interface(
    fn=predict,
    inputs=gr.Textbox(lines=8, label="JSON Input"),
    outputs="json",
    title="Fraud Detection API (Threshold-Calibrated)",
    description="Flags transactions as fraud only when probability exceeds 0.80"
)

if __name__ == "__main__":
    iface.launch(ssr_mode=True)