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

# clone / pull the lmeh eval data
TOKEN = os.environ.get("TOKEN", None)
login(token=TOKEN)
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)

all_columns = ['R','type', 'Model','open?', 'avg_sea ⬇️', 'en', 'zh', 'id', 'th', 'vi', 'avg', 'params(B)']
show_columns = ['R', 'Model','type','open?','params(B)', 'avg_sea ⬇️', 'en', 'zh', 'id', 'th', 'vi', 'avg', ]
TYPES = ['number', 'markdown', 'str', 'str', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number']
# Load the data from the csv file
csv_path = f'{EVAL_RESULTS_PATH}/SeaExam_results_20240425.csv'
df_m3exam, df_mmlu, df_avg = load_data(csv_path)
# df_m3exam = df_m3exam.copy()[show_columns]
# df_mmlu = df_mmlu.copy()[show_columns]
df_avg_init = df_avg.copy()[df_avg['type'] == '🔶 chat'][show_columns]
df_m3exam_init = df_m3exam.copy()[df_m3exam['type'] == '🔶 chat'][show_columns]
df_mmlu_init = df_mmlu.copy()[df_mmlu['type'] == '🔶 chat'][show_columns]

# data_types = ['number', 'str', 'markdown','str', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number']
# map_columns = {'rank':'R','type':'type', 'Model':'Model','open?':'open?', 'avg_sea':'avg_sea ⬇️', 'en':'en', 'zh':'zh', 'id':'id', 'th':'th', 'vi':'vi', 'avg':'avg', 'params':'params(B)'}
# map_types = {'rank': 'number', 'type': 'str', 'Model': 'markdown', 'open?': 'str', 'avg_sea': 'number', 'en': 'number', 'zh': 'number', 'id': 'number', 'th': 'number', 'vi': 'number', 'avg': 'number', 'params': 'number'}
# Searching and filtering
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': 'Y', 'closed': 'N'}
    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:
        with gr.TabItem("🏅 Overall", elem_id="llm-benchmark-Sum", id=0):
            with gr.Row():
                with gr.Column():
                    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",
                        )   
                    # with gr.Row():
                    #     with gr.Column():
                    #         shown_columns = gr.CheckboxGroup(
                    #             choices=["rank","type", "Model","open?", "avg_sea", "en", "zh", "id", "th", "vi", "avg", "params"],
                    #             value=["rank", "type", "Model", "avg_sea", "en", "zh", "id", "th", "vi", "avg", "params"],
                    #             label="Select model types to show",
                    #             elem_id="column-select",
                    #             interactive=True,
                    #         )

            # 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_avg_init,
                # [[map_columns[k] for k in shown_columns.value]],
                # 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,
                # datatype=['number', 'str', 'markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
                # datatype=[map_types[k] for k in shown_columns.value],
                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,
                    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]:
                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("M3Exam", elem_id="llm-benchmark-M3Exam", id=1):
            with gr.Row():
                with gr.Column():
                    search_bar = gr.Textbox(
                        placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
                        show_label=False,
                        elem_id="search-bar",
                    )
                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_m3exam_init,
                interactive=False,
                visible=True,
                # datatype=['number', 'str', 'markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
                datatype=TYPES,
            )

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

            search_bar.submit(
                update_table,
                [   
                    hidden_leaderboard_table_for_search, 
                    type_query,
                    open_query,
                    search_bar,
                ],
                leaderboard_table,
            )
            for selector in [type_query, open_query]:
                selector.change(
                    update_table,
                    [   
                        hidden_leaderboard_table_for_search, 
                        type_query,
                        open_query,
                        search_bar,
                    ],
                    leaderboard_table,
                )

        with gr.TabItem("MMLU", elem_id="llm-benchmark-MMLU", id=2):
            with gr.Row():
                with gr.Column():
                    search_bar = gr.Textbox(
                        placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
                        show_label=False,
                        elem_id="search-bar",
                    )
                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_mmlu_init,
                interactive=False,
                visible=True,
                # datatype=['number', 'str', 'markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
                datatype=TYPES,
            )

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

            search_bar.submit(
                update_table,
                [   
                    hidden_leaderboard_table_for_search, 
                    type_query,
                    open_query,
                    search_bar,
                ],
                leaderboard_table,
            )
            for selector in [type_query, open_query]:
                selector.change(
                    update_table,
                    [   
                        hidden_leaderboard_table_for_search, 
                        type_query,
                        open_query,
                        search_bar,
                    ],
                    leaderboard_table,
                )

        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=3):
            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)