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','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','Pre-ASR','FID','CLIP-Score']
show_columns = ['Unlearned_Methods','Pre-ASR','Pre-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)