app.py

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','Source', 'Diffusion_Models','Pre-ASR', 'Post-ASR','FID']
    columns_sorted = ['Unlearned_Methods','Source', 'Diffusion_Models','Pre-ASR', 'Post-ASR','FID']
    
    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)
csv_path='./assets/object_parachute.csv'
df_results = load_data(csv_path)
methods = list(set(df_results['Unlearned_Methods']))
all_columns = ['Unlearned_Methods','Source', 'Diffusion_Models','Pre-ASR', 'Post-ASR','FID']
show_columns = ['Unlearned_Methods','Source', 'Diffusion_Models','Pre-ASR', 'Post-ASR','FID']
TYPES = ['str', 'markdown', 'str', 'number', 'number', 'number']

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()
    
    # 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,open_query)
    filtered_df = filter_queries(query, filtered_df)
    # 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 select_columns(df: pd.DataFrame, columns_1: list) -> pd.DataFrame:
    always_here_cols = ['Unlearned_Methods','Source', 'Diffusion_Models']
    
    # We use COLS to maintain sorting
    all_columns =['Pre-ASR','Post-ASR','FID']

    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")

    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'],
                        interactive=True,
                        elem_id="column-select",
                    )
                    with gr.Row():
                        open_query = gr.CheckboxGroup(
                        label="Model",
                        choices=["SD V1.4","SD V1.5", "SD V2.0"],
                        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,)
            gr.Markdown("### Unlearned Concepts Parachute")
            leaderboard_table = gr.components.Dataframe(
                value = df_results,
                datatype = TYPES,
                elem_id = "leaderboard-table",
                interactive = False,
                visible=True,
                # column_widths=["20%", "6%", "8%", "6%", "8%", "8%", "6%", "6%", "6%", "6%", "6%"],
            )

            # gr.Markdown("The \"Cost\" column is calculated as USD / Million tokens of output.")

            hidden_leaderboard_table_for_search = gr.components.Dataframe(
                value=df_results_init,
                # elem_id="leaderboard-table",
                interactive=False,
                visible=False,
            )

            search_bar.submit(
                update_table,
                [   
                    # df_avg,
                    hidden_leaderboard_table_for_search,
                    model1_column,
                    # shown_columns,
                    #type_query,
                    open_query,
                    # filter_columns_type,
                    # filter_columns_precision,
                    # filter_columns_size,
                    # deleted_models_visibility,
                    search_bar,
                ],
                leaderboard_table,
            )

            #for selector in [type_query, open_query]:
            for selector in [open_query,model1_column]:
                selector.change(
                    update_table,
                    [   
                        # df_avg,
                        hidden_leaderboard_table_for_search,
                        model1_column,
                        # shown_columns,
                        #type_query,
                        open_query,
                        # filter_columns_type,
                        # filter_columns_precision,
                        # filter_columns_size,
                        # deleted_models_visibility,
                        search_bar,
                    ],
                    leaderboard_table,
                )
    #                 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)