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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)
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