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#!/usr/bin/env python
import os
import datetime
import socket
import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
from src.display.about import (
CITATION_BUTTON_LABEL,
CITATION_BUTTON_TEXT,
EVALUATION_QUEUE_TEXT,
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT,
LLM_BENCHMARKS_DETAILS,
FAQ_TEXT,
TITLE
)
from src.display.css_html_js import custom_css
from src.display.utils import (
BENCHMARK_COLS,
COLS,
EVAL_COLS,
EVAL_TYPES,
NUMERIC_INTERVALS,
TYPES,
AutoEvalColumn,
ModelType,
fields,
WeightType,
Precision
)
from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, H4_TOKEN, IS_PUBLIC, QUEUE_REPO, REPO_ID, RESULTS_REPO
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval
from src.utils import get_dataset_summary_table
def ui_snapshot_download(repo_id, local_dir, repo_type, tqdm_class, etag_timeout):
try:
print(local_dir)
snapshot_download(repo_id=repo_id, local_dir=local_dir, repo_type=repo_type, tqdm_class=tqdm_class, etag_timeout=etag_timeout)
except Exception as e:
restart_space()
def restart_space():
API.restart_space(repo_id=REPO_ID, token=H4_TOKEN)
def init_space():
dataset_df = get_dataset_summary_table(file_path='blog/Hallucination-Leaderboard-Summary.csv')
if socket.gethostname() not in {'neuromancer'}:
ui_snapshot_download(repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30)
ui_snapshot_download(repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30)
raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
return dataset_df, original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df
dataset_df, original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space()
leaderboard_df = original_df.copy()
# Searching and filtering
def update_table(hidden_df: pd.DataFrame,
columns: list,
type_query: list,
precision_query: list,
size_query: list,
query: str):
filtered_df = filter_models(hidden_df, type_query, size_query, precision_query)
filtered_df = filter_queries(query, filtered_df)
df = select_columns(filtered_df, columns)
return df
def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))]
def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
# always_here_cols = [AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name]
always_here_cols = [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
dummy_col = [AutoEvalColumn.dummy.name]
# We use COLS to maintain sorting
filtered_df = df[
# always_here_cols + [c for c in COLS if c in df.columns and c in columns] + [AutoEvalColumn.dummy.name]
always_here_cols + [c for c in COLS if c in df.columns and c in columns] + dummy_col
]
return filtered_df
def filter_queries(query: str, filtered_df: 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)
subset = [AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name]
filtered_df = filtered_df.drop_duplicates(subset=subset)
return filtered_df
def filter_models(df: pd.DataFrame,
type_query: list,
size_query: list,
precision_query: list) -> pd.DataFrame:
# Show all models
filtered_df = df
type_emoji = [t[0] for t in type_query]
filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
filtered_df = filtered_df.loc[mask]
return filtered_df
# triggered only once at startup => read query parameter if it exists
def load_query(request: gr.Request):
query = request.query_params.get("query") or ""
return 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("Hallucinations Benchmark",
elem_id="llm-benchmark-tab-table",
id=0):
with gr.Row():
with gr.Column():
with gr.Row():
search_bar = gr.Textbox(placeholder=" π Model search (separate multiple queries with `;`)",
show_label=False,
elem_id="search-bar")
with gr.Row():
shown_columns = gr.CheckboxGroup(
choices=[
c.name
for c in fields(AutoEvalColumn)
if not c.hidden and not c.never_hidden and not c.dummy
],
value=[
c.name
for c in fields(AutoEvalColumn)
if c.displayed_by_default and not c.hidden and not c.never_hidden
],
label="Select columns to show",
elem_id="column-select",
interactive=True)
with gr.Column(min_width=320):
filter_columns_type = gr.CheckboxGroup(
label="Model types",
choices=[t.to_str() for t in ModelType],
value=[t.to_str() for t in ModelType],
interactive=True,
elem_id="filter-columns-type")
filter_columns_precision = gr.CheckboxGroup(
label="Precision",
choices=[i.value.name for i in Precision],
value=[i.value.name for i in Precision],
interactive=True,
elem_id="filter-columns-precision")
filter_columns_size = gr.CheckboxGroup(
label="Model sizes (in billions of parameters)",
choices=list(NUMERIC_INTERVALS.keys()),
value=list(NUMERIC_INTERVALS.keys()),
interactive=True,
elem_id="filter-columns-size")
# breakpoint()
leaderboard_table = gr.components.Dataframe(
value=leaderboard_df[
[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value + [AutoEvalColumn.dummy.name]
] if leaderboard_df.empty is False else leaderboard_df,
headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
datatype=TYPES,
elem_id="leaderboard-table",
interactive=False,
visible=True,
column_widths=["2%", "20%"])
# Dummy leaderboard for handling the case when the user uses backspace key
hidden_leaderboard_table_for_search = gr.components.Dataframe(
value=original_df[COLS] if original_df.empty is False else original_df,
headers=COLS,
datatype=TYPES,
visible=False)
search_bar.submit(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_size,
search_bar,
],
leaderboard_table)
# Check query parameter once at startup and update search bar
demo.load(load_query, inputs=[], outputs=[search_bar])
for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size]:
selector.change(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_size,
search_bar,
],
leaderboard_table,
queue=True)
with gr.TabItem("About", elem_id="llm-benchmark-tab-table", id=2):
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
dataset_table = gr.components.Dataframe(
value=dataset_df,
headers=list(dataset_df.columns),
datatype=['str', 'markdown', 'str', 'str', 'str'],
elem_id="dataset-table",
interactive=False,
visible=True,
column_widths=["15%", "20%"])
gr.Markdown(LLM_BENCHMARKS_DETAILS, elem_classes="markdown-text")
gr.Markdown(FAQ_TEXT, elem_classes="markdown-text")
with gr.TabItem("Submit a model ", elem_id="llm-benchmark-tab-table", id=3):
with gr.Column():
with gr.Row():
gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
with gr.Column():
with gr.Accordion(f"β
Finished Evaluations ({len(finished_eval_queue_df)})", open=False):
with gr.Row():
finished_eval_table = gr.components.Dataframe(
value=finished_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
row_count=5)
with gr.Accordion(f"π Running Evaluation Queue ({len(running_eval_queue_df)})", open=False):
with gr.Row():
running_eval_table = gr.components.Dataframe(
value=running_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
row_count=5)
with gr.Accordion(f"β³ Scheduled Evaluation Queue ({len(pending_eval_queue_df)})", open=False):
with gr.Row():
pending_eval_table = gr.components.Dataframe(
value=pending_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
row_count=5)
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")
private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC)
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.name for i in Precision if i != Precision.Unknown],
label="Precision",
multiselect=False,
value="float32",
interactive=True)
weight_type = gr.Dropdown(
choices=[i.value.name 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,
private,
weight_type,
model_type,
],
submission_result)
with gr.Row():
with gr.Accordion("Citing this leaderboard", 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=6 * 60 * 60)
def launch_backend():
import subprocess
from src.backend.envs import DEVICE
if DEVICE not in {'cpu'}:
_ = subprocess.run(["python", "backend-cli.py"])
# scheduler.add_job(launch_backend, "interval", seconds=120)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch()
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