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import subprocess
import gradio as gr
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 (
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, 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)
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, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
leaderboard_df = original_df.copy()
(
finished_eval_queue_df,
running_eval_queue_df,
pending_eval_queue_df,
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
# Searching and filtering
def update_table(
hidden_df: pd.DataFrame,
columns: list,
type_query: list,
precision_query: str,
size_query: list,
show_deleted: bool,
query: str,
):
filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
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.model.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,
]
# 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]
]
return filtered_df
def filter_queries(query: str, filtered_df: pd.DataFrame) -> 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)
filtered_df = filtered_df.drop_duplicates(
subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name]
)
return filtered_df
def filter_models(
df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
) -> pd.DataFrame:
# Show all models
if show_deleted:
filtered_df = df
else: # Show only still on the hub models
filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
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
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("UnlearnDiffAtk Benchmark", elem_id="UnlearnDiffAtk-benchmark-tab-table", id=0):
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",
)
# with gr.Column(min_width=320):
with gr.Row():
shown_columns_1 = gr.CheckboxGroup(
choices=["Church","Parachute","Tench", "Garbage Truck"],
label="Undersirable Objects",
elem_id="column-object",
interactive=True,
)
with gr.Row():
shown_columns_2 = gr.CheckboxGroup(
choices=["Van Gogh"],
label="Undersirable Styles",
elem_id="column-style",
interactive=True,
)
with gr.Row():
shown_columns_3 = gr.CheckboxGroup(
choices=["Violence","Illegal Activity","Nudity"],
label="Undersirable Concepts (Outputs that may be offensive in nature)",
elem_id="column-select",
interactive=True,
)
with gr.Row():
shown_columns_4 = gr.Slider(
1, 100, value=40,
step=1, label="Attacking Steps", info="Choose between 1 and 100",
interactive=True,)
# with gr.Row():
# shown_columns = gr.CheckboxGroup(
# choices=[
# c.name
# for c in fields(AutoEvalColumn)
# if not c.hidden and not c.never_hidden
# ],
# 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.Row():
# deleted_models_visibility = gr.Checkbox(
# value=False, label="Show gated/private/deleted models", interactive=True
# )
# with gr.Column(min_width=320):
# #with gr.Box(elem_id="box-filter"):
# filter_columns_type = gr.CheckboxGroup(
# label="Unlearning 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",
# )
# leaderboard_table = gr.components.Dataframe(
# value=leaderboard_df[
# [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
# + shown_columns.value
# ],
# 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,
# )
# # Dummy leaderboard for handling the case when the user uses backspace key
# hidden_leaderboard_table_for_search = gr.components.Dataframe(
# value=original_df[COLS],
# 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,
# deleted_models_visibility,
# search_bar,
# ],
# leaderboard_table,
# )
# for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility]:
# selector.change(
# update_table,
# [
# hidden_leaderboard_table_for_search,
# shown_columns,
# filter_columns_type,
# filter_columns_precision,
# filter_columns_size,
# deleted_models_visibility,
# 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")
# 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")
# with gr.Column():
# with gr.Accordion(
# f"✅ 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"🔄 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"⏳ Pending 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")
# 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="float16",
# 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,
# weight_type,
# model_type,
# ],
# submission_result,
# )
with gr.Row():
with gr.Accordion("📙 Citation", open=True):
citation_button = gr.Textbox(
value=CITATION_BUTTON_TEXT,
label=CITATION_BUTTON_LABEL,
lines=10,
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() |