Remove run_local and add tips

app_text_classification.py

@@ -7,7 +7,6 @@ from text_classification_ui_helpers import (
     get_related_datasets_from_leaderboard,
     align_columns_and_show_prediction,
     check_dataset,
-    deselect_run_inference,
     precheck_model_ds_enable_example_btn,
     select_run_mode,
     try_submit,
@@ -81,14 +80,37 @@ def get_demo():
                         column_mappings.append(gr.Dropdown(visible=False))
 
     with gr.Accordion(label="Model Wrap Advance Config", open=True):
-        run_inference = gr.Checkbox(value=True, label="Run with Inference API")
+        run_inference = gr.Checkbox(
+            value=True,
+            label="Run with HF Inference API"
+        )
+        gr.HTML(
+            value="""
+                We recommend to use
+                <a href="https://huggingface.co/docs/api-inference/detailed_parameters#text-classification-task">
+                    Hugging Face Inference API
+                </a>
+                for the evaluation,
+                which requires your <a href="https://huggingface.co/settings/tokens">HF token</a>.
+                <br/>
+                Otherwise, an
+                <a href="https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.TextClassificationPipeline">
+                    HF pipeline 
+                </a>
+                will be created and run in this Space. It takes more time to get the result.
+                <br/>
+                <b>
+                Do not worry, your HF token is only used in this Space for your evaluation.
+                </b>
+            """,
+        )
         inference_token = gr.Textbox(
+            placeholder="hf-xxxxxxxxxxxxxxxxxxxx",
             value="",
             label="HF Token for Inference API",
             visible=True,
             interactive=True,
         )
-        run_local = gr.Checkbox(value=False, label="Run Locally with Pipeline [Slow]")
 
 
     with gr.Accordion(label="Scanner Advance Config (optional)", open=False):
@@ -144,13 +166,7 @@ def get_demo():
     run_inference.change(
         select_run_mode,
         inputs=[run_inference],
-        outputs=[inference_token, run_local],
-    )
-
-    run_local.change(
-        deselect_run_inference,
-        inputs=[run_local],
-        outputs=[inference_token, run_inference],
+        outputs=[inference_token],
     )
 
     gr.on(
@@ -230,7 +246,6 @@ def get_demo():
             dataset_id_input,
             dataset_config_input,
             dataset_split_input,
-            run_local,
             run_inference,
             inference_token,
             uid_label,
@@ -248,7 +263,6 @@ def get_demo():
     gr.on(
         triggers=[
             run_inference.input,
-            run_local.input,
             inference_token.input,
             scanners.input,
         ],

text_classification_ui_helpers.py

@@ -105,16 +105,9 @@ def check_dataset(dataset_id, dataset_config=None, dataset_split=None):
 
 def select_run_mode(run_inf):
     if run_inf:
-        return (gr.update(visible=True), gr.update(value=False))
+        return gr.update(visible=True)
     else:
-        return (gr.update(visible=False), gr.update(value=True))
-
-
-def deselect_run_inference(run_local):
-    if run_local:
-        return (gr.update(visible=False), gr.update(value=False))
-    else:
-        return (gr.update(visible=True), gr.update(value=True))
+        return gr.update(visible=False)
 
 
 def write_column_mapping_to_config(uid, *labels):
@@ -320,7 +313,7 @@ def construct_label_and_feature_mapping(all_mappings):
     return label_mapping, feature_mapping
 
 
-def try_submit(m_id, d_id, config, split, local, inference, inference_token, uid):
+def try_submit(m_id, d_id, config, split, inference, inference_token, uid):
     all_mappings = read_column_mapping(uid)
     check_column_mapping_keys_validity(all_mappings)
     label_mapping, feature_mapping = construct_label_and_feature_mapping(all_mappings)
@@ -329,8 +322,7 @@ def try_submit(m_id, d_id, config, split, local, inference, inference_token, uid
     if os.environ.get("SPACE_ID") == "giskardai/giskard-evaluator":
         leaderboard_dataset = LEADERBOARD
 
-    if local:
-        inference_type = "hf_pipeline"
+    inference_type = "hf_pipeline"
     if inference and inference_token:
         inference_type = "hf_inference_api"