Handle 1D r_probs shape (batch=1 case)

app.py

@@ -133,7 +133,8 @@ def predict(sequence: str):
     pred = pred_texts[0]
     r = float(r_pred.cpu().tolist()[0] if torch.is_tensor(r_pred) else r_pred[0])
     r_probs_list = r_probs.cpu().tolist() if torch.is_tensor(r_probs) else list(r_probs)
-    p_pos = float(r_probs_list[0][1])
+    first = r_probs_list[0]
+    p_pos = float(first[1]) if isinstance(first, (list, tuple)) else float(r_probs_list[1])
     return pred, format_reliability(r), f"{p_pos:.4f}"