Update app.py

app.py

@@ -9,25 +9,19 @@ import numpy as np
 import pandas as pd
 
 
-basic_component_values = [None] * 6
 leader_component_values = [None]
+space = "   "
 
 def make_default_md(arena_df, elo_results):
-    total_votes = sum(arena_df["num_battles"]) // 2
-    total_models = len(arena_df)
-
     leaderboard_md = f"""
 # NeurIPS LLM Merging Competition Leaderboard
-[Website](https://llm-merging.github.io/index) | [Starter Kit (Github)](https://github.com/llm-merging/LLM-Merging) | [Discord](https://discord.com/invite/dPBHEVnV) |
+[Website](https://llm-merging.github.io/index) | [Starter Kit (Github)](https://github.com/llm-merging/LLM-Merging) | [Discord](https://discord.com/invite/dPBHEVnV)
 
 """
     return leaderboard_md
 
-
 def make_arena_leaderboard_md(arena_df):
-    total_votes = sum(arena_df["num_battles"]) // 2
     total_models = len(arena_df)
-    space = "   "
     leaderboard_md = f"""
 Three benchmarks are displayed: **Test Task 1**, **Test Task 2**, **Test Task 3**.
 
@@ -39,13 +33,10 @@ Total #models: **{total_models}**.{space} Last updated: June 1, 2024.
     return leaderboard_md
 
 def make_category_arena_leaderboard_md(arena_df, arena_subset_df, name="Overall"):
-    total_votes = sum(arena_df["num_battles"]) // 2
     total_models = len(arena_df)
-    space = "   "
-    total_subset_votes = sum(arena_subset_df["num_battles"]) // 2
     total_subset_models = len(arena_subset_df)
     leaderboard_md = f"""### {cat_name_to_explanation[name]}
-#### [Coverage] {space} #models: **{total_subset_models} ({round(total_subset_models/total_models *100)}%)** {space} #votes: **{"{:,}".format(total_subset_votes)} ({round(total_subset_votes/total_votes * 100)}%)**{space}
+#### [Coverage] {space} #models: **{total_subset_models} ({round(total_subset_models/total_models *100)}%)**{space}
 """
     return leaderboard_md
 
@@ -59,43 +50,6 @@ Last updated: {elo_results["last_updated_datetime"]}
     return leaderboard_md
 
 
-def update_elo_components(max_num_files, elo_results_file):
-    log_files = get_log_files(max_num_files)
-
-    # Leaderboard
-    if elo_results_file is None:  # Do live update
-        battles = clean_battle_data(log_files)
-        elo_results = report_elo_analysis_results(battles)
-        leader_component_values[0] = make_leaderboard_md_live(elo_results)
-
-    # Basic stats
-    basic_stats = report_basic_stats(log_files)
-    md0 = f"Last updated: {basic_stats['last_updated_datetime']}"
-
-    md1 = "### Action Histogram\n"
-    md1 += basic_stats["action_hist_md"] + "\n"
-
-    md2 = "### Anony. Vote Histogram\n"
-    md2 += basic_stats["anony_vote_hist_md"] + "\n"
-
-    md3 = "### Model Call Histogram\n"
-    md3 += basic_stats["model_hist_md"] + "\n"
-
-    md4 = "### Model Call (Last 24 Hours)\n"
-    md4 += basic_stats["num_chats_last_24_hours"] + "\n"
-
-    basic_component_values[0] = md0
-    basic_component_values[1] = basic_stats["chat_dates_bar"]
-    basic_component_values[2] = md1
-    basic_component_values[3] = md2
-    basic_component_values[4] = md3
-    basic_component_values[5] = md4
-
-
-def model_hyperlink(model_name, link):
-    return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
-
-
 def load_leaderboard_table_csv(filename, add_hyperlink=False):
     lines = open(filename).readlines()
     heads = [v.strip() for v in lines[0].split(",")]
@@ -127,31 +81,10 @@ def load_leaderboard_table_csv(filename, add_hyperlink=False):
                         v = np.nan
                 item[h] = v
             if add_hyperlink:
-                item["Model"] = model_hyperlink(item["Model"], item["Link"])
+                item["Model"] = f'<a target="_blank" href="{item["Link"]}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{item["Model"]}</a>'
         rows.append(item)
-
     return rows
 
-
-def build_basic_stats_tab():
-    empty = "Loading ..."
-    basic_component_values[:] = [empty, None, empty, empty, empty, empty]
-
-    md0 = gr.Markdown(empty)
-    gr.Markdown("#### Figure 1: Number of model calls and votes")
-    plot_1 = gr.Plot(show_label=False)
-    with gr.Row():
-        with gr.Column():
-            md1 = gr.Markdown(empty)
-        with gr.Column():
-            md2 = gr.Markdown(empty)
-    with gr.Row():
-        with gr.Column():
-            md3 = gr.Markdown(empty)
-        with gr.Column():
-            md4 = gr.Markdown(empty)
-    return [md0, plot_1, md1, md2, md3, md4]
-
 def get_full_table(arena_df, model_table_df):
     values = []
     for i in range(len(model_table_df)):