Update app.py

app.py

@@ -192,8 +192,11 @@ def predict_scores(df):
     X = df[feats].copy()
     ag = ag_predictor()
 
-    # List available models and remove any FastAI learners that might break
-    available_models = ag.model_names
+    # List all models and exclude any FastAI learners (which can cause tabular DL issues)
+    try:
+        available_models = ag.model_names()   # ✅ parentheses for older AutoGluon versions
+    except Exception:
+        available_models = ag.model_names
     safe_models = [m for m in available_models if "fastai" not in m.lower()]
     print(f"[INFO] Using safe models: {safe_models}")
 
@@ -202,12 +205,12 @@ def predict_scores(df):
         if isinstance(proba, pd.DataFrame) and (1 in proba.columns):
             return proba[1]
     except Exception as e:
-        print("[WARN] AutoGluon fastai model failed; retrying with safe models only:", e)
+        print("[WARN] AutoGluon fastai model failed, retrying with safe models only:", e)
         proba = ag.predict_proba(X, models=safe_models)
         if isinstance(proba, pd.DataFrame) and (1 in proba.columns):
             return proba[1]
 
-    # Fallback in case probability data isn't available
+    # Fallback if no probabilities are returned
     s = pd.Series(ag.predict(X, models=safe_models)).astype(float)
     rng = (s.quantile(0.95) - s.quantile(0.05)) or 1.0
     return ((s - s.quantile(0.05)) / rng).clip(0, 1)