app.py

import gradio as gr
import pandas as pd
import os
from huggingface_hub import snapshot_download, login
from apscheduler.schedulers.background import BackgroundScheduler

from src.display.about import (
    CITATION_BUTTON_LABEL,
    CITATION_BUTTON_TEXT,
    CONTACT_TEXT,
    EVALUATION_QUEUE_TEXT,
    INTRODUCTION_TEXT,
    LLM_BENCHMARKS_TEXT,
    TITLE,
    SUB_TITLE,
)
from src.display.css_html_js import custom_css
from src.envs import API
from src.leaderboard.load_results import load_data


def restart_space():
    API.restart_space(repo_id="Auto-Arena/Leaderboard")


csv_path = f"./src/results/auto-arena-llms-results-20240624.csv"
csv_path_chinese = f"./src/results/auto-arena-llms-results-chinese-20240531.csv"
df_results = load_data(csv_path).sort_values(by="Rank")
df_results_chinese = load_data(csv_path_chinese)

all_columns = ['Rank', 'Model', 'From', 'Open?', 'Params(B)', 'Cost', 'Score']
show_columns = ['Rank', 'Model', 'From', 'Open?', 'Params(B)', 'Cost', 'Score']
TYPES = ['number', 'markdown', 'str', 'str', 'str', 'str', 'number']

df_results_init = df_results.copy()[show_columns]
df_results_chinese_init = df_results_chinese.copy()[show_columns]

def update_table(
    hidden_df: pd.DataFrame,
    # columns: list,
    #type_query: list,
    open_query: list,
    # precision_query: str,
    # size_query: list,
    # show_deleted: bool,
    query: str,
):
    # filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
    # filtered_df = filter_queries(query, filtered_df)
    # df = select_columns(filtered_df, columns)
    filtered_df = hidden_df.copy()
    
    # filtered_df = filtered_df[filtered_df['type'].isin(type_query)]
    map_open = {'open': 'Yes', 'closed': 'No'}
    filtered_df = filtered_df[filtered_df['Open?'].isin([map_open[o] for o in open_query])]

    filtered_df = filter_queries(query, filtered_df)
    # filtered_df = filtered_df[[map_columns[k] for k in columns]]
    # deduplication
    # df = df.drop_duplicates(subset=["Model"])
    df = filtered_df.drop_duplicates()
    df = df[show_columns]
    return df


def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
    return df[(df['Model'].str.contains(query, case=False))]


def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
    final_df = []
    if query != "":
        queries = [q.strip() for q in query.split(";")]
        for _q in queries:
            _q = _q.strip()
            if _q != "":
                temp_filtered_df = search_table(filtered_df, _q)
                if len(temp_filtered_df) > 0:
                    final_df.append(temp_filtered_df)
        if len(final_df) > 0:
            filtered_df = pd.concat(final_df)

    return filtered_df


demo = gr.Blocks(css=custom_css)
with demo:
    gr.HTML(TITLE)
    gr.HTML(SUB_TITLE)
    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
    
    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        # the first tab
        with gr.TabItem("English", elem_id="llm-benchmark-Sum", id=0):
            # meta-info
            with gr.Row():
                with gr.Column():
                    search_bar = gr.Textbox(
                        placeholder=" 🔍 Search for models you are interested in (separate multiple models with `;`) and press ENTER...",
                        show_label=False,
                        elem_id="search-bar",
                    ) 
            # with gr.Row():
                # with gr.Column():
                #     type_query = gr.CheckboxGroup(
                #         choices=["🟢 base", "🔶 chat"],
                #         value=["🔶 chat" ],
                #         label="model types to show",
                #         elem_id="type-select",
                #         interactive=True,
                #     )
                with gr.Column():
                    open_query = gr.CheckboxGroup(
                        choices=["open", "closed"],
                        value=["open", "closed"],
                        label="open-source OR closed-source models?",
                        elem_id="open-select",
                        interactive=True,
                    )
            
            leaderboard_table = gr.components.Dataframe(
                value = df_results,
                datatype = TYPES,
                elem_id = "leaderboard-table",
                interactive = False,
                visible=True,
                # column_widths=["20%", "6%", "8%", "6%", "8%", "8%", "6%", "6%", "6%", "6%", "6%"],
            )

            gr.Markdown("The \"Cost\" column is calculated as USD / Million tokens of output.")

            hidden_leaderboard_table_for_search = gr.components.Dataframe(
                value=df_results_init,
                # elem_id="leaderboard-table",
                interactive=False,
                visible=False,
            )

            search_bar.submit(
                update_table,
                [   
                    # df_avg,
                    hidden_leaderboard_table_for_search,
                    # shown_columns,
                    #type_query,
                    open_query,
                    # filter_columns_type,
                    # filter_columns_precision,
                    # filter_columns_size,
                    # deleted_models_visibility,
                    search_bar,
                ],
                leaderboard_table,
            )

            #for selector in [type_query, open_query]:
            for selector in [open_query]:
                selector.change(
                    update_table,
                    [   
                        # df_avg,
                        hidden_leaderboard_table_for_search,
                        # shown_columns,
                        #type_query,
                        open_query,
                        # filter_columns_type,
                        # filter_columns_precision,
                        # filter_columns_size,
                        # deleted_models_visibility,
                        search_bar,
                    ],
                    leaderboard_table,
                )

        with gr.TabItem("Chinese", elem_id="llm-benchmark-Sum", id=1):
            # meta-info
            with gr.Row():
                with gr.Column():
                    search_bar = gr.Textbox(
                        placeholder=" 🔍 Search for models you are interested in (separate multiple models with `;`) and press ENTER...",
                        show_label=False,
                        elem_id="search-bar",
                    ) 
            # with gr.Row():
                # with gr.Column():
                #     type_query = gr.CheckboxGroup(
                #         choices=["🟢 base", "🔶 chat"],
                #         value=["🔶 chat" ],
                #         label="model types to show",
                #         elem_id="type-select",
                #         interactive=True,
                #     )
                with gr.Column():
                    open_query = gr.CheckboxGroup(
                        choices=["open", "closed"],
                        value=["open", "closed"],
                        label="open-source OR closed-source models?",
                        elem_id="open-select",
                        interactive=True,
                    )
            
            leaderboard_table = gr.components.Dataframe(
                value = df_results_chinese,
                datatype = TYPES,
                elem_id = "leaderboard-table",
                interactive = False,
                visible=True,
                # column_widths=["20%", "6%", "8%", "6%", "8%", "8%", "6%", "6%", "6%", "6%", "6%"],
            )

            gr.Markdown("The \"Cost\" column is calculated as USD / Million tokens of output.")

            hidden_leaderboard_table_for_search = gr.components.Dataframe(
                value=df_results_chinese_init,
                # elem_id="leaderboard-table",
                interactive=False,
                visible=False,
            )

            search_bar.submit(
                update_table,
                [   
                    # df_avg,
                    hidden_leaderboard_table_for_search,
                    # shown_columns,
                    #type_query,
                    open_query,
                    # filter_columns_type,
                    # filter_columns_precision,
                    # filter_columns_size,
                    # deleted_models_visibility,
                    search_bar,
                ],
                leaderboard_table,
            )

            #for selector in [type_query, open_query]:
            for selector in [open_query]:
                selector.change(
                    update_table,
                    [   
                        # df_avg,
                        hidden_leaderboard_table_for_search,
                        # shown_columns,
                        #type_query,
                        open_query,
                        # filter_columns_type,
                        # filter_columns_precision,
                        # filter_columns_size,
                        # deleted_models_visibility,
                        search_bar,
                    ],
                    leaderboard_table,
                )

        # with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=1):
        #     gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
    
    # with gr.Row():
    #     with gr.Accordion("📙 Citation", open=False):
    #         citation_button = gr.Textbox(
    #             value=CITATION_BUTTON_TEXT,
    #             label=CITATION_BUTTON_LABEL,
    #             lines=20,
    #             elem_id="citation-button",
    #             show_copy_button=True,
    #         )
    
    gr.Markdown(CONTACT_TEXT, elem_classes="markdown-text")

demo.launch()

scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch(share=True)