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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,
BENCHMARKS_TEXT,
TITLE,
EVALUATION_QUEUE_TEXT
)
from src.display.css_html_js import custom_css
from src.leaderboard.read_evals import get_raw_eval_results, get_leaderboard_df
from src.envs import API, EVAL_RESULTS_PATH, REPO_ID, RESULTS_REPO, TOKEN
from utils import update_table, update_metric, update_table_long_doc, upload_file, get_default_cols, submit_results
from src.benchmarks import DOMAIN_COLS_QA, LANG_COLS_QA, DOMAIN_COLS_LONG_DOC, LANG_COLS_LONG_DOC, metric_list
from src.display.utils import TYPES_QA, TYPES_LONG_DOC
def restart_space():
API.restart_space(repo_id=REPO_ID)
try:
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 = get_raw_eval_results(f"{EVAL_RESULTS_PATH}/AIR-Bench_24.04")
original_df_qa = get_leaderboard_df(
raw_data, task='qa', metric='ndcg_at_3')
original_df_long_doc = get_leaderboard_df(
raw_data, task='long-doc', metric='ndcg_at_3')
print(f'raw data: {len(raw_data)}')
print(f'QA data loaded: {original_df_qa.shape}')
print(f'Long-Doc data loaded: {len(original_df_long_doc)}')
leaderboard_df_qa = original_df_qa.copy()
shown_columns_qa = get_default_cols('qa', leaderboard_df_qa.columns, add_fix_cols=True)
leaderboard_df_qa = leaderboard_df_qa[shown_columns_qa]
leaderboard_df_long_doc = original_df_long_doc.copy()
shown_columns_long_doc = get_default_cols('long-doc', leaderboard_df_long_doc.columns, add_fix_cols=True)
leaderboard_df_long_doc = leaderboard_df_long_doc[shown_columns_long_doc]
def update_metric_qa(
metric: str,
domains: list,
langs: list,
reranking_model: list,
query: str,
):
return update_metric(raw_data, 'qa', metric, domains, langs, reranking_model, query)
def update_metric_long_doc(
metric: str,
domains: list,
langs: list,
reranking_model: list,
query: str,
):
return update_metric(raw_data, "long-doc", metric, domains, langs, reranking_model, query)
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="qa-benchmark-tab-table", id=0):
with gr.Row():
with gr.Column():
# search bar for model name
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",
)
# select the metric
selected_metric = gr.Dropdown(
choices=metric_list,
value=metric_list[1],
label="Select the metric",
interactive=True,
elem_id="metric-select",
)
with gr.Column(min_width=320):
# 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.Dropdown(
choices=LANG_COLS_QA,
value=LANG_COLS_QA,
label="Select the languages",
elem_id="language-column-select",
multiselect=True,
interactive=True
)
# select reranking model
reranking_models = list(frozenset([eval_result.reranking_model for eval_result in raw_data]))
with gr.Row():
selected_rerankings = gr.CheckboxGroup(
choices=reranking_models,
value=reranking_models,
label="Select the reranking models",
elem_id="reranking-select",
interactive=True
)
leaderboard_table = gr.components.Dataframe(
value=leaderboard_df_qa,
datatype=TYPES_QA,
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=leaderboard_df_qa,
datatype=TYPES_QA,
# headers=COLS,
# datatype=TYPES,
visible=False,
)
# Set search_bar listener
search_bar.submit(
update_table,
[
hidden_leaderboard_table_for_search,
selected_domains,
selected_langs,
selected_rerankings,
search_bar,
],
leaderboard_table,
)
# Set column-wise listener
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,
)
# set metric listener
selected_metric.change(
update_metric_qa,
[
selected_metric,
selected_domains,
selected_langs,
selected_rerankings,
search_bar,
],
leaderboard_table,
queue=True
)
with gr.TabItem("Long Doc", elem_id="long-doc-benchmark-tab-table", id=1):
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-long-doc",
)
# select the metric
selected_metric = gr.Dropdown(
choices=metric_list,
value=metric_list[1],
label="Select the metric",
interactive=True,
elem_id="metric-select-long-doc",
)
with gr.Column(min_width=320):
# select domain
with gr.Row():
selected_domains = gr.CheckboxGroup(
choices=DOMAIN_COLS_LONG_DOC,
value=DOMAIN_COLS_LONG_DOC,
label="Select the domains",
elem_id="domain-column-select-long-doc",
interactive=True,
)
# select language
with gr.Row():
selected_langs = gr.Dropdown(
choices=LANG_COLS_LONG_DOC,
value=LANG_COLS_LONG_DOC,
label="Select the languages",
elem_id="language-column-select-long-doc",
multiselect=True,
interactive=True
)
# select reranking model
reranking_models = list(frozenset([eval_result.reranking_model for eval_result in raw_data]))
with gr.Row():
selected_rerankings = gr.CheckboxGroup(
choices=reranking_models,
value=reranking_models,
label="Select the reranking models",
elem_id="reranking-select-long-doc",
interactive=True
)
leaderboard_table_long_doc = gr.components.Dataframe(
value=leaderboard_df_long_doc,
datatype=TYPES_LONG_DOC,
elem_id="leaderboard-table-long-doc",
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=leaderboard_df_long_doc,
datatype=TYPES_LONG_DOC,
visible=False,
)
# Set search_bar listener
search_bar.submit(
update_table_long_doc,
[
hidden_leaderboard_table_for_search,
selected_domains,
selected_langs,
selected_rerankings,
search_bar,
],
leaderboard_table_long_doc,
)
# Set column-wise listener
for selector in [
selected_domains, selected_langs, selected_rerankings
]:
selector.change(
update_table_long_doc,
[
hidden_leaderboard_table_for_search,
selected_domains,
selected_langs,
selected_rerankings,
search_bar,
],
leaderboard_table_long_doc,
queue=True,
)
# set metric listener
selected_metric.change(
update_metric_long_doc,
[
selected_metric,
selected_domains,
selected_langs,
selected_rerankings,
search_bar,
],
leaderboard_table_long_doc,
queue=True
)
with gr.TabItem("🚀Submit here!", elem_id="submit-tab-table", id=2):
with gr.Column():
with gr.Row():
gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
with gr.Row():
gr.Markdown("## ✉️Submit your model here!", elem_classes="markdown-text")
with gr.Row():
with gr.Column():
benchmark_version = gr.Dropdown(
["AIR-Bench_24.04",],
value="AIR-Bench_24.04",
interactive=True,
label="AIR-Bench Version")
with gr.Column():
model_name = gr.Textbox(label="Model name")
with gr.Column():
model_url = gr.Textbox(label="Model URL")
with gr.Row():
upload_button = gr.UploadButton("Upload search results", file_count="single")
with gr.Row():
file_output = gr.File()
with gr.Row():
submit_button = gr.Button("Submit")
with gr.Row():
submission_result = gr.Markdown()
upload_button.upload(
upload_file,
[
upload_button,
model_name,
model_url,
benchmark_version,
],
file_output)
submit_button.click(
submit_results,
[
file_output,
model_name,
model_url
],
submission_result,
show_progress="hidden"
)
with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=3):
gr.Markdown(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()
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