fix layout and prediction token

text_classification_ui_helpers.py

@@ -21,7 +21,6 @@ from text_classification import (
     preload_hf_inference_api,
     get_example_prediction,
     get_labels_and_features_from_dataset,
-    check_hf_token_validity,
     HuggingFaceInferenceAPIResponse,
 )
 from wordings import (
@@ -271,11 +270,9 @@ def align_columns_and_show_prediction(
     dropdown_placement = [
         gr.Dropdown(visible=False) for _ in range(MAX_LABELS + MAX_FEATURES)
     ]
-    
-    hf_token = os.environ.get(HF_WRITE_TOKEN, default="")
 
     prediction_input, prediction_response = get_example_prediction(
-        model_id, dataset_id, dataset_config, dataset_split, hf_token
+        model_id, dataset_id, dataset_config, dataset_split, oauth_token.token
     )
 
     if prediction_input is None or prediction_response is None:
@@ -412,12 +409,6 @@ def construct_label_and_feature_mapping(all_mappings, ds_labels, ds_features, la
       feature_mapping.update({"label": label_keys[0]})
     return label_mapping, feature_mapping
 
-def show_hf_token_info(token):
-    valid = check_hf_token_validity(token)
-    if not valid:
-        return gr.update(visible=True)
-    return gr.update(visible=False)
-
 def try_submit(m_id, d_id, config, split, uid, profile: gr.OAuthProfile | None, oath_token: gr.OAuthToken | None):
     all_mappings = read_column_mapping(uid)
     if not check_column_mapping_keys_validity(all_mappings):

wordings.py

@@ -6,9 +6,11 @@ INTRODUCTION_MD = """
                 You can also checkout our library documentation <a href="https://docs.giskard.ai/en/latest/getting_started/quickstart/index.html">here</a>.
                 """
 CONFIRM_MAPPING_DETAILS_MD = """
+                            <div style="display: flex; justify-content: center;"><h1>
                             <h1 style="text-align: center;">
                             Confirm Pre-processing Details
                             </h1>
+                            </div>
                             Make sure the output variable's labels and the input variable's name are accurately mapped across both the dataset and the model. You can select the output variable's labels from the dropdowns below.
                             """
 CONFIRM_MAPPING_DETAILS_FAIL_MD = """