|
import gradio as gr |
|
import pandas as pd |
|
from src.display.about import ( |
|
CITATION_BUTTON_LABEL, |
|
CITATION_BUTTON_TEXT, |
|
EVALUATION_QUEUE_TEXT, |
|
INTRODUCTION_TEXT, |
|
LLM_BENCHMARKS_TEXT, |
|
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 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, token=H4_TOKEN) |
|
|
|
|
|
raw_data = dummydf() |
|
methods = list(set(raw_data['Method'])) |
|
metrics = ["Style-UA", "Style-IRA", "Style-CRA", "Object-UA", "Object-IRA", "Object-CRA", "FID", "Time (s)", "Storage (GB)", "Memory (GB)"] |
|
|
|
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"] |
|
|
|
|
|
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: |
|
|
|
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: |
|
with gr.Row(): |
|
gr.Image("./assets/logo.png", height="200px", width="200px", scale=0.1, |
|
show_download_button=False, container=False) |
|
gr.HTML(TITLE, elem_id="title") |
|
|
|
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") |
|
with gr.Tabs(elem_classes="tab-buttons") as tabs: |
|
with gr.TabItem("🏅 UnlearnCanvas Benchmark", elem_id="llm-benchmark-tab-table", id=0): |
|
with gr.Row(): |
|
with gr.Column(): |
|
with gr.Row(): |
|
model1_column = gr.CheckboxGroup( |
|
label="Unlearning Methods", |
|
choices=methods, |
|
interactive=True, |
|
elem_id="column-select", |
|
) |
|
|
|
with gr.Row(): |
|
shown_columns_1 = gr.CheckboxGroup( |
|
choices=["Style-UA", "Style-IRA", "Style-CRA", "Object-UA", "Object-IRA", "Object-CRA"], |
|
label="Style / Object Unlearning Effectiveness", |
|
elem_id="column-select", |
|
interactive=True, |
|
) |
|
|
|
with gr.Row(): |
|
shown_columns_2 = gr.CheckboxGroup( |
|
choices=["FID"], |
|
label="Image Quality", |
|
elem_id="column-select", |
|
interactive=True, |
|
) |
|
|
|
with gr.Row(): |
|
shown_columns_3 = gr.CheckboxGroup( |
|
choices=["Time (s)", "Memory (GB)", "Storage (GB)"], |
|
label="Resource Costs", |
|
elem_id="column-select", |
|
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.TabItem("🚀 Model Submit", elem_id="llm-benchmark-tab-table", id=1): |
|
gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text") |
|
gr.Markdown(FAQ_TEXT, elem_classes="markdown-text") |
|
|
|
with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2): |
|
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") |
|
gr.Markdown(FAQ_TEXT, elem_classes="markdown-text") |
|
|
|
with gr.Row(): |
|
with gr.Accordion("📙 Citation", open=True): |
|
citation_button = gr.Textbox( |
|
value=CITATION_BUTTON_TEXT, |
|
label=CITATION_BUTTON_LABEL, |
|
lines=8, |
|
elem_id="citation-button", |
|
show_copy_button=True, |
|
) |
|
|
|
|
|
demo.launch() |
|
|
|
|
