1. Removed redundant code

2. Optimized imports
3. Removed unnecessary comments

app.py

@@ -329,7 +329,6 @@ with demo:
             getattr(comp, fn)(
                 core.update_df,
                 [gr.State(value=0), shown_tasks, search_bar, langs_bar, model_sizes, gr.State(value=True), model_types],
-                # [shown_tasks, search_bar, langs_bar, model_types, gr.State(value=True)],
                 leaderboard_table,
             )
 

core.py

@@ -1,13 +1,12 @@
 import itertools
 import os
 
-import gradio as gr
 import numpy as np
 import pandas as pd
 from datasets import load_dataset
 
 import style
-from style import T_SYMBOLS, MT_BENCH_LANG_SYMBOLS, LANG_SYMBOLS
+from style import T_SYMBOLS
 from utils import add_model_hyperlink
 
 ZERO_SHOT_ONLY = ["BELEBELE", "MT-Bench"]
@@ -154,72 +153,6 @@ def update_df(
         return sort_cols(df, fewshot)
 
 
-def update_fewshot_status(current_selected_tab, is_fewshot_current):
-    if current_selected_tab == 0:
-        is_fewshot_new = is_fewshot_current
-        fewshot_available = True
-    elif current_selected_tab == 1:
-        is_fewshot_new = False
-        fewshot_available = False
-    elif current_selected_tab == 2:
-        is_fewshot_new = False
-        fewshot_available = False
-    else:
-        raise ValueError(f"Unknown tab id {current_selected_tab}")
-
-    return is_fewshot_new, fewshot_available
-
-
-def update_task_groups_and_fewshot(current_selected_tab: int, model_types, langs_bar,
-                                   is_fewshot_current: bool = False, ):
-    selected_task_type = get_selected_task_type(current_selected_tab)
-    available_tasks = get_available_task_groups(selected_task_type, is_fewshot_current)
-    new_selected_tasks = available_tasks.copy()
-
-    tasks_checkbox_group_update = gr.CheckboxGroup(
-        choices=available_tasks,
-        value=new_selected_tasks,
-    )
-
-    is_fewshot_new, fewshot_available = update_fewshot_status(current_selected_tab, is_fewshot_current)
-
-    # fewshot_radio_update = gr.Radio(
-    #     value=is_fewshot_new,
-    #     interactive=fewshot_available,
-    # )
-
-    if current_selected_tab == 2:
-        model_types = gr.CheckboxGroup(
-            value=[T_SYMBOLS['chat']],
-            interactive=False
-        )
-        langs_bar = gr.CheckboxGroup(
-            choices=[(MT_BENCH_LANG_SYMBOLS.get(l, l), l) for l in mt_bench_language_list],
-            value=mt_bench_language_list,
-            interactive=True,
-        )
-    else:
-        model_types = gr.CheckboxGroup(
-            label="Select model type",
-            choices=[
-                (
-                    f"Pretrained {T_SYMBOLS['pretrained']}",
-                    T_SYMBOLS["pretrained"],
-                ),
-                (f"Chat {T_SYMBOLS['chat']}", T_SYMBOLS["chat"]),
-            ],
-            value=list(T_SYMBOLS.values()),
-            interactive=True
-        )
-        langs_bar = gr.CheckboxGroup(
-            choices=[(LANG_SYMBOLS.get(l, l), l) for l in languages_list],
-            value=languages_list,
-            interactive=True,
-        )
-
-    return [tasks_checkbox_group_update, current_selected_tab, model_types, langs_bar]
-
-
 def get_selected_task_type(task_type_id):
     task_types = {0: "accuracy", 1: "misc", 2: "mtbench_score", 3: "accuracy"}
     selected_task_type = task_types[task_type_id]