Update app.py

app.py

@@ -194,26 +194,36 @@ def predict_scores(df):
 
     # 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
+        available_models = ag.model_names()
     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}")
 
+    # Access AutoGluon's internal learner, which supports the `models` argument
+    learner = ag._learner
+
     try:
-        proba = ag.predict_proba(X, models=safe_models)
+        # Get probabilities from the safe models only
+        proba = learner.predict_proba(X, models=safe_models)
         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)
-        proba = ag.predict_proba(X, models=safe_models)
+        print("[WARN] FastAI submodel failed — retrying with safe models only:", e)
+        proba = learner.predict_proba(X, models=safe_models)
         if isinstance(proba, pd.DataFrame) and (1 in proba.columns):
             return proba[1]
 
-    # 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)
+    # Fallback if probabilities aren’t available
+    try:
+        preds = learner.predict(X, models=safe_models)
+        s = pd.Series(preds).astype(float)
+        rng = (s.quantile(0.95) - s.quantile(0.05)) or 1.0
+        return ((s - s.quantile(0.05)) / rng).clip(0, 1)
+    except Exception as e:
+        print("[ERROR] Predictor fallback failed:", e)
+        return pd.Series(np.zeros(len(df)))
+
 
 def pick_events(df,score,fps):
     z=rolling_z(score,45); strong=(z>4.0); keep=strong.rolling(3,min_periods=1).sum()>=2