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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,
EVALUATION_QUEUE_TEXT,
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT,
TITLE,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
COLUMNS,
COLS,
BENCHMARK_COLS,
EVAL_COLS,
EVAL_TYPES,
ModelType,
WeightType,
Precision
)
from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval
def restart_space():
API.restart_space(repo_id=REPO_ID)
### Space initialization
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()
# Load the leaderboard DataFrame
LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
print("LEADERBOARD_DF Shape:", LEADERBOARD_DF.shape) # Debug
print("LEADERBOARD_DF Columns:", LEADERBOARD_DF.columns.tolist()) # Debug
# Load the evaluation queue DataFrames
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("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
if LEADERBOARD_DF.empty:
gr.Markdown("No evaluations have been performed yet. The leaderboard is currently empty.")
else:
default_selection = [col.name for col in COLUMNS if col.displayed_by_default]
print("Default Selection before ensuring 'model_name':", default_selection) # Debug
# Ensure "model_name" is included
if "model_name" not in default_selection:
default_selection.insert(0, "model_name")
print("Default Selection after ensuring 'model_name':", default_selection) # Debug
leaderboard = Leaderboard(
value=LEADERBOARD_DF,
datatype=[col.type for col in COLUMNS],
select_columns=SelectColumns(
default_selection=default_selection,
cant_deselect=[col.name for col in COLUMNS if col.never_hidden],
label="Select Columns to Display:",
),
search_columns=[col.name for col in COLUMNS if col.name in ["model_name", "license"]], # Updated to 'model_name'
hide_columns=[col.name for col in COLUMNS if col.hidden],
filter_columns=[
ColumnFilter("model_type", type="checkboxgroup", label="Model types"),
ColumnFilter("precision", type="checkboxgroup", label="Precision"),
ColumnFilter(
"still_on_hub", type="boolean", label="Deleted/incomplete", default=True
),
],
bool_checkboxgroup_label="Hide models",
interactive=False,
)
# No need to call leaderboard.render() since it's created within the Gradio context
with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
with gr.Column():
with gr.Row():
gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
# Since the evaluation queues are empty, display a message
with gr.Column():
gr.Markdown("Evaluations are performed immediately upon submission. There are no pending or running evaluations.")
with gr.Row():
gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
with gr.Row():
with gr.Column():
model_name_textbox = gr.Textbox(label="Model name")
revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
model_type = gr.Dropdown(
choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
label="Model type",
multiselect=False,
value=None,
interactive=True,
)
with gr.Column():
precision = gr.Dropdown(
choices=[i.value for i in Precision if i != Precision.Unknown],
label="Precision",
multiselect=False,
value="float16",
interactive=True,
)
weight_type = gr.Dropdown(
choices=[i.value for i in WeightType],
label="Weights type",
multiselect=False,
value="Original",
interactive=True,
)
base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
submit_button = gr.Button("Submit Eval")
submission_result = gr.Markdown()
submit_button.click(
add_new_eval,
[
model_name_textbox,
base_model_name_textbox,
revision_name_textbox,
precision,
weight_type,
model_type,
],
submission_result,
)
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,
)
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch()
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