Update app.py

app.py

@@ -1,18 +1,17 @@
 import gradio as gr
-import torch
+import joblib
 import numpy as np
 import json
 from huggingface_hub import hf_hub_download
 
 REPO = "gabrielnkl/model-fraud-detect"
-MODEL = "fraud_detect_model.pth"
+MODEL = "modelo_fraude.pkl"
 
-# download once + cache
+# download model
 model_path = hf_hub_download(repo_id=REPO, filename=MODEL)
 
-# load model
-model = torch.load(model_path, map_location=torch.device("cpu"))
-model.eval()
+# load sklearn model
+model = joblib.load(model_path)
 
 TYPE_MAP = {
     "PAYMENT": 0,
@@ -22,6 +21,7 @@ TYPE_MAP = {
     "CASH_IN": 4
 }
 
+
 def predict(json_input):
 
     try:
@@ -32,11 +32,10 @@ def predict(json_input):
         new = float(d["newbalanceOrig"])
         t = TYPE_MAP[d["type"]]
 
-        x = np.array([[amount, old, new, t]], dtype=np.float32)
-        tensor = torch.from_numpy(x)
+        X = np.array([[amount, old, new, t]], dtype=float)
 
-        prob = float(model(tensor).item())
-        pred = int(prob > 0.5)
+        prob = float(model.predict_proba(X)[0][1])
+        pred = int(model.predict(X)[0])
 
         return {
             "prediction": pred,
@@ -46,11 +45,13 @@ def predict(json_input):
     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"
+    title="Fraud Detection API (Sklearn Model)",
+    description="Submit JSON payload for fraud scoring."
 )
 
 if __name__ == "__main__":