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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
from PIL import Image
from dummydatagen import dummy_data_for_plot, create_metric_plot_obj_1, dummydf
import copy
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)
raw_data = dummydf()
methods = list(set(raw_data['Method']))
metrics = ["Church","Parachute","Tench","Garbage Turch","Van Gogh","Violence","Illegal Activity","Nudity"]
def update_table(
hidden_df: pd.DataFrame,
columns_1: list,
columns_2: list,
columns_3: list,
model1: list,
):
filtered_df = select_columns(hidden_df, columns_1, columns_2, columns_3)
filtered_df = filter_model1(filtered_df, model1)
return filtered_df
def select_columns(df: pd.DataFrame, columns_1: list, columns_2: list, columns_3: list) -> pd.DataFrame:
always_here_cols = ["Method"]
# We use COLS to maintain sorting
all_columns = metrics
if (len(columns_1)+len(columns_2) + len(columns_3)) == 0:
filtered_df = df[
always_here_cols +
[c for c in all_columns if c in df.columns]
]
else:
filtered_df = df[
always_here_cols +
[c for c in all_columns if c in df.columns and (c in columns_1 or c in columns_2 or c in columns_3 ) ]
]
return filtered_df
def filter_model1(df: pd.DataFrame, model_query: list) -> pd.DataFrame:
# Show all models
if len(model_query) == 0:
return df
filtered_df = df
filtered_df = filtered_df[filtered_df["Method"].isin(model_query)]
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.Row():
model1_column = gr.CheckboxGroup(
label="Unlearning Methods",
choices=methods,
interactive=True,
elem_id="column-select",
)
# 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,)
leaderboard_table = gr.components.Dataframe(
value= raw_data,
elem_id="leaderboard-table",
interactive=False,
visible=True,
)
game_bench_df_for_search = gr.components.Dataframe(
value= raw_data,
elem_id="leaderboard-table",
interactive=False,
visible=False,
)
for selector in [shown_columns_1,shown_columns_2, shown_columns_3, model1_column]:
selector.change(
update_table,
[
game_bench_df_for_search,
shown_columns_1,
shown_columns_2,
shown_columns_3,
model1_column,
],
leaderboard_table,
queue=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().launch(share=True) |