Update app.py

app.py

@@ -162,9 +162,8 @@ def pearson_r(y_true, y_pred) -> float:
     p = np.asarray(y_pred, dtype=float)
     
     # Check for constant arrays, where std dev would be zero.
-    # We must check both true and predicted arrays.
     if a.size < 2 or np.all(a == a[0]) or np.all(p == p[0]):
-    return float("nan")
+        return float("nan")
         
     return float(np.corrcoef(a, p)[0, 1])