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leaderboard / app.py
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feat: enable the search bar and reranking selector
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import gradio as gr
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
from src.about import (
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT,
TITLE,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
QA_BENCHMARK_COLS,
COLS,
TYPES,
AutoEvalColumnQA,
fields
)
from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
from src.populate import get_leaderboard_df
from utils import update_table
from src.benchmarks import DOMAIN_COLS_QA, LANG_COLS_QA, metric_list
def restart_space():
API.restart_space(repo_id=REPO_ID)
# try:
# print(EVAL_REQUESTS_PATH)
# snapshot_download(
# repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30,
# token=TOKEN
# )
# except Exception:
# restart_space()
# try:
# print(EVAL_RESULTS_PATH)
# snapshot_download(
# repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30,
# token=TOKEN
# )
# except Exception:
# restart_space()
raw_data_qa, original_df_qa = get_leaderboard_df(
EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, QA_BENCHMARK_COLS, task='qa', metric='ndcg_at_3')
print(f'data loaded: {len(raw_data_qa)}, {original_df_qa.shape}')
leaderboard_df = original_df_qa.copy()
# (
# finished_eval_queue_df,
# running_eval_queue_df,
# pending_eval_queue_df,
# ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
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("QA", elem_id="llm-benchmark-tab-table", id=0):
with gr.Row():
with gr.Column():
with gr.Row():
search_bar = gr.Textbox(
placeholder=" ๐Ÿ” Search for your model (separate multiple queries with `;`) and press ENTER...",
show_label=False,
elem_id="search-bar",
)
# select domain
with gr.Row():
selected_domains = gr.CheckboxGroup(
choices=DOMAIN_COLS_QA,
value=DOMAIN_COLS_QA,
label="Select the domains",
elem_id="domain-column-select",
interactive=True,
)
# select language
with gr.Row():
selected_langs = gr.CheckboxGroup(
choices=LANG_COLS_QA,
value=LANG_COLS_QA,
label="Select the languages",
elem_id="language-column-select",
interactive=True
)
# select reranking models
reranking_models = list(frozenset([eval_result.reranking_model for eval_result in raw_data_qa]))
with gr.Row():
selected_rerankings = gr.CheckboxGroup(
choices=reranking_models,
value=reranking_models,
label="Select the reranking models",
elem_id="reranking-select",
interactive=True
)
with gr.Column(min_width=320):
selected_metric = gr.Dropdown(
choices=metric_list,
value=metric_list[0],
label="Select the metric",
interactive=True,
elem_id="metric-select",
)
# reload the leaderboard_df and raw_data when selected_metric is changed
leaderboard_table = gr.components.Dataframe(
value=leaderboard_df,
# headers=shown_columns,
# datatype=TYPES,
elem_id="leaderboard-table",
interactive=False,
visible=True,
)
# Dummy leaderboard for handling the case when the user uses backspace key
hidden_leaderboard_table_for_search = gr.components.Dataframe(
value=original_df_qa,
# headers=COLS,
# datatype=TYPES,
visible=False,
)
search_bar.submit(
update_table,
[
hidden_leaderboard_table_for_search,
selected_domains,
selected_langs,
selected_rerankings,
search_bar,
],
leaderboard_table,
)
for selector in [
selected_domains, selected_langs, selected_rerankings
]:
selector.change(
update_table,
[
hidden_leaderboard_table_for_search,
selected_domains,
selected_langs,
selected_rerankings,
search_bar,
],
leaderboard_table,
queue=True,
)
with gr.TabItem("๐Ÿ“ About", elem_id="llm-benchmark-tab-table", id=2):
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch()