app.py

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

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

# clone / pull the lmeh eval data
TOKEN = os.environ.get("TOKEN", None)
RESULTS_REPO = f"SeaLLMs/SeaExam-results"
CACHE_PATH=os.getenv("HF_HOME", ".")
EVAL_RESULTS_PATH = os.path.join(CACHE_PATH, "eval-results")
print(EVAL_RESULTS_PATH)
snapshot_download(
    repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", 
    token=TOKEN
)

def restart_space():
    API.restart_space(repo_id="SeaLLMs/SeaExam_leaderboard", token=TOKEN)

# Load the data from the csv file
csv_path = f'{EVAL_RESULTS_PATH}/SeaExam_results.csv'
df_m3exam, df_mmlu, df_avg = load_data(csv_path)

# Searching and filtering
def update_table(
    hidden_df: pd.DataFrame,
    # columns: list,
    # type_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()
    df = filter_queries(query, hidden_df)
    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.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("🏅 Overall", elem_id="llm-benchmark-Sum", id=0):
            with gr.Row():
                search_bar = gr.Textbox(
                    placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
                    show_label=False,
                    elem_id="search-bar",
                )
            
            leaderboard_table = gr.components.Dataframe(
                value=df_avg,
                # value=leaderboard_df[
                #     [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
                #     + shown_columns.value
                #     + [AutoEvalColumn.dummy.name]
                # ],
                # headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
                # datatype=TYPES,
                elem_id="leaderboard-table",
                interactive=False,
                visible=True,
                # column_widths=["20%", "6%", "8%", "6%", "8%", "8%", "6%", "6%", "6%", "6%", "6%"],
            )

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

            search_bar.submit(
                update_table,
                [   
                    # df_avg,
                    hidden_leaderboard_table_for_search,
                    # shown_columns,
                    # filter_columns_type,
                    # filter_columns_precision,
                    # filter_columns_size,
                    # deleted_models_visibility,
                    search_bar,
                ],
                leaderboard_table,
            )
        with gr.TabItem("🏅 M3Exam", elem_id="llm-benchmark-M3Exam", id=1):
            with gr.Row():
                search_bar = gr.Textbox(
                    placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
                    show_label=False,
                    elem_id="search-bar",
                )

            leaderboard_table = gr.components.Dataframe(
                value=df_m3exam,
                interactive=False,
                visible=True,
            )

            hidden_leaderboard_table_for_search = gr.components.Dataframe(
                value=df_m3exam,
                interactive=False,
                visible=False,
            )

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

        with gr.TabItem("🏅 MMLU", elem_id="llm-benchmark-MMLU", id=2):
            leaderboard_table = gr.components.Dataframe(
                value=df_mmlu,
                interactive=False,
                visible=True,
            )
        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=3):
            gr.Markdown(LLM_BENCHMARKS_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)