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import gradio as gr | |
from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns | |
import pandas as pd | |
from apscheduler.schedulers.background import BackgroundScheduler | |
from huggingface_hub import snapshot_download | |
from src.about import ( | |
CITATION_BUTTON_LABEL, | |
CITATION_BUTTON_TEXT, | |
INTRODUCTION_TEXT, | |
LLM_BENCHMARKS_TEXT, | |
TITLE, | |
) | |
from src.display.css_html_js import custom_css | |
from src.display.utils import ( | |
BENCHMARK_COLS, | |
COLS, | |
EVAL_COLS, | |
EVAL_TYPES, | |
AutoEvalColumn, | |
ModelType, | |
fields, | |
WeightType, | |
Precision, | |
) | |
from src.envs import API, EVAL_RESULTS_PATH_CDM, EVAL_RESULTS_PATH_CDM_FI, REPO_ID, RESULTS_REPO_CDM, RESULTS_REPO_CDM_FI, TOKEN | |
from src.populate import get_leaderboard_df | |
def restart_space(): | |
API.restart_space(repo_id=REPO_ID) | |
### Space initialisation | |
try: | |
print(EVAL_RESULTS_PATH_CDM) | |
snapshot_download( | |
repo_id=RESULTS_REPO_CDM, | |
local_dir=EVAL_RESULTS_PATH_CDM, | |
repo_type="dataset", | |
tqdm_class=None, | |
etag_timeout=30, | |
token=TOKEN, | |
) | |
except Exception: | |
restart_space() | |
try: | |
print(EVAL_RESULTS_PATH_CDM_FI) | |
snapshot_download( | |
repo_id=RESULTS_REPO_CDM_FI, | |
local_dir=EVAL_RESULTS_PATH_CDM_FI, | |
repo_type="dataset", | |
tqdm_class=None, | |
etag_timeout=30, | |
token=TOKEN, | |
) | |
except Exception: | |
restart_space() | |
LEADERBOARD_DF_CDM = get_leaderboard_df(EVAL_RESULTS_PATH_CDM, COLS, BENCHMARK_COLS) | |
LEADERBOARD_DF_CDM_FI = get_leaderboard_df(EVAL_RESULTS_PATH_CDM_FI, COLS, BENCHMARK_COLS) | |
def init_leaderboard(dataframe): | |
if dataframe is None or dataframe.empty: | |
raise ValueError("Leaderboard DataFrame is empty or None.") | |
return Leaderboard( | |
value=dataframe, | |
datatype=[c.type for c in fields(AutoEvalColumn)], | |
select_columns=SelectColumns( | |
default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default], | |
cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden], | |
label="Select Columns to Display:", | |
), | |
search_columns=[AutoEvalColumn.model.name], | |
hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden], | |
filter_columns=[ | |
# ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"), | |
# ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"), | |
# ColumnFilter( | |
# AutoEvalColumn.params.name, | |
# type="slider", | |
# min=0.01, | |
# max=150, | |
# label="Select the number of parameters (B)", | |
# ), | |
ColumnFilter(AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True), | |
], | |
bool_checkboxgroup_label="Hide models", | |
interactive=False, | |
) | |
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("MIMIC CDM", elem_id="llm-benchmark-tab-table", id=0): | |
leaderboard = init_leaderboard(LEADERBOARD_DF_CDM) | |
with gr.TabItem("MIMIC CDM FI", elem_id="llm-benchmark-tab-table", id=1): | |
leaderboard = init_leaderboard(LEADERBOARD_DF_CDM_FI) | |
with gr.TabItem("π About", elem_id="llm-benchmark-tab-table", id=2): | |
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") | |
with gr.Row(): | |
with gr.Accordion("π 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, | |
) | |
scheduler = BackgroundScheduler() | |
scheduler.add_job(restart_space, "interval", seconds=1800) | |
scheduler.start() | |
demo.queue(default_concurrency_limit=40).launch(share=True) | |