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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 load_data(data_path): | |
| columns = ['Unlearned_Methods','Pre-ASR','Post-ASR','FID','CLIP-Score'] | |
| columns_sorted = ['Unlearned_Methods','Pre-ASR','Post-ASR','FID','CLIP-Score'] | |
| df = pd.read_csv(data_path).dropna() | |
| # df['Post-ASR'] = df['Post-ASR'].round(0) | |
| # rank according to the Score column | |
| df = df.sort_values(by='Post-ASR', ascending=False) | |
| # reorder the columns | |
| df = df[columns_sorted] | |
| return df | |
| 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) | |
| all_columns = ['Unlearned_Methods','Pre-ASR','Post-ASR','FID','CLIP-Score'] | |
| show_columns = ['Unlearned_Methods','Pre-ASR','Post-ASR','FID','CLIP-Score'] | |
| TYPES = ['str', 'number', 'number', 'number', 'number'] | |
| files = ['nudity','vangogh', 'church','garbage','parachute','tench'] | |
| csv_path='./assets/'+files[0]+'.csv' | |
| df_results = load_data(csv_path) | |
| methods = list(set(df_results['Unlearned_Methods'])) | |
| df_results_init = df_results.copy()[show_columns] | |
| def update_table( | |
| hidden_df: pd.DataFrame, | |
| model1_column: list, | |
| #type_query: list, | |
| #open_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) | |
| filtered_df = hidden_df.copy() | |
| # print(open_query) | |
| # filtered_df = filtered_df[filtered_df['Unlearned_Methods'].isin(open_query)] | |
| # map_open = {'open': 'Yes', 'closed': 'No'} | |
| # filtered_df = filtered_df[filtered_df['Open?'].isin([map_open[o] for o in open_query])] | |
| filtered_df=select_columns(filtered_df,model1_column) | |
| filtered_df = filter_queries(query, filtered_df) | |
| # map_open = {'SD V1.4', 'SD V1.5', 'SD V2.0'} | |
| # filtered_df = filtered_df[filtered_df["Diffusion_Models"].isin([o for o in open_query])] | |
| # filtered_df = filtered_df[[map_columns[k] for k in columns]] | |
| # deduplication | |
| # df = df.drop_duplicates(subset=["Model"]) | |
| df = filtered_df.drop_duplicates() | |
| # df = df[show_columns] | |
| return df | |
| def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame: | |
| return df[(df['Unlearned_Methods'].str.contains(query, case=False))] | |
| 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) | |
| return filtered_df | |
| def search_table_model(df: pd.DataFrame, query: str) -> pd.DataFrame: | |
| return df[(df['Diffusion_Models'].str.contains(query, case=False))] | |
| def filter_queries_model(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame: | |
| final_df = [] | |
| # if query != "": | |
| # queries = [q.strip() for q in query.split(";")] | |
| for _q in query: | |
| print(_q) | |
| if _q != "": | |
| temp_filtered_df = search_table_model(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) | |
| return filtered_df | |
| def select_columns(df: pd.DataFrame, columns_1: list) -> pd.DataFrame: | |
| always_here_cols = ['Unlearned_Methods'] | |
| # We use COLS to maintain sorting | |
| all_columns =['Pre-ASR','Post-ASR','FID','CLIP-Score'] | |
| if (len(columns_1)) == 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) ] | |
| ] | |
| return filtered_df | |
| demo = gr.Blocks(css=custom_css) | |
| with demo: | |
| gr.HTML(TITLE) | |
| gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") | |
| gr.Markdown(EVALUATION_QUEUE_TEXT,elem_classes="eval-text") | |
| gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="reference-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="Evaluation Metrics", | |
| choices=['Pre-ASR','Post-ASR','FID','CLIP-Score'], | |
| 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,) | |
| for i in range(len(files)): | |
| if files[i] == "church": | |
| name = "### [Unlearned Objects] "+" Church" | |
| csv_path = './assets/'+files[i]+'.csv' | |
| elif files[i] == 'garbage': | |
| name = "### [Unlearned Objects] "+" Garbage" | |
| csv_path = './assets/'+files[i]+'.csv' | |
| elif files[i] == 'tench': | |
| name = "### [Unlearned Objects] "+" Tench" | |
| csv_path = './assets/'+files[i]+'.csv' | |
| elif files[i] == 'parachute': | |
| name = "### [Unlearned Objects] "+" Parachute" | |
| csv_path = './assets/'+files[i]+'.csv' | |
| elif files[i] == 'vangogh': | |
| name = "### [Unlearned Style] "+" Van Gogh" | |
| csv_path = './assets/'+files[i]+'.csv' | |
| elif files[i] == 'nudity': | |
| name = "### Unlearned Concepts "+" Nudity" | |
| csv_path = './assets/'+files[i]+'.csv' | |
| # elif files[i] == 'violence': | |
| # name = "### Unlearned Concepts "+" Violence" | |
| # csv_path = './assets/'+files[i]+'.csv' | |
| # elif files[i] == 'illegal_activity': | |
| # name = "### Unlearned Concepts "+" Illgal Activity" | |
| # csv_path = './assets/'+files[i]+'.csv' | |
| gr.Markdown(name) | |
| df_results = load_data(csv_path) | |
| df_results_init = df_results.copy()[show_columns] | |
| leaderboard_table = gr.components.Dataframe( | |
| value = df_results, | |
| datatype = TYPES, | |
| elem_id = "leaderboard-table", | |
| interactive = False, | |
| visible=True, | |
| ) | |
| hidden_leaderboard_table_for_search = gr.components.Dataframe( | |
| #value=df_results_init, | |
| value=df_results, | |
| interactive=False, | |
| visible=False, | |
| ) | |
| search_bar.submit( | |
| update_table, | |
| [ | |
| hidden_leaderboard_table_for_search, | |
| model1_column, | |
| search_bar, | |
| ], | |
| leaderboard_table, | |
| ) | |
| for selector in [model1_column]: | |
| selector.change( | |
| update_table, | |
| [ | |
| hidden_leaderboard_table_for_search, | |
| model1_column, | |
| search_bar, | |
| ], | |
| leaderboard_table, | |
| ) | |
| 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) |