removed y_attribute as output of compute

app.py

@@ -13,7 +13,7 @@ from sklearn.model_selection import cross_val_score
 
 def compute(file, y_attribute, cv_folds):
     if file is None:
-        return 'Please upload a .arff file', y_attribute
+        return 'Please upload a .arff file'
     if file.name.endswith('.arff'):
         dataset = openml.datasets.OpenMLDataset('t', 'test', data_file=file.name)
         X_, _, categorical_indicator_, attribute_names_ = dataset.get_data(
@@ -23,7 +23,7 @@ def compute(file, y_attribute, cv_folds):
         X, y, categorical_indicator_, attribute_names_ = dataset.get_data(
             dataset_format="array", target=y_attribute)
     else:
-        return 'Please upload a .arff file', y_attribute
+        return 'Please upload a .arff file'
 
     order = np.arange(y.shape[0])
     np.random.seed(13)
@@ -40,7 +40,7 @@ def compute(file, y_attribute, cv_folds):
     # print(file, type(file))
     return f"ROC AUC OVO Cross Val mean is {sum(scores) / len(scores)} from {scores}. " + (
         "The PFN is only trained for datasets with up to 1024 training examples and it had to extrapolate to greater datasets for this evaluation." if len(
-            y) // cv_folds > 1024 else ""), y_attribute
+            y) // cv_folds > 1024 else "")
 
 
 def upload_file(file):
@@ -76,6 +76,6 @@ with gr.Blocks() as demo:
     # out_table = gr.DataFrame()
     inp_file.change(fn=upload_file, inputs=inp_file, outputs=[out_text, y_attribute])
 
-    btn.click(fn=compute, inputs=[inp_file, y_attribute, cv_folds], outputs=[out_text, y_attribute])
+    btn.click(fn=compute, inputs=[inp_file, y_attribute, cv_folds], outputs=[out_text])
 
 demo.launch()