app.py

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 src.populate import get_evaluation_queue_df, get_leaderboard_df
# from src.submission.submit import add_new_eval
# from src.tools.collections import update_collections
# from src.tools.plots import (
#     create_metric_plot_obj,
#     create_plot_df,
#     create_scores_df,
# )
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)


def add_average_col(df):

    always_here_cols = [
        "Model", "Agent", "Opponent Model", "Opponent Agent"
    ]
    desired_col = [i for i in list(df.columns) if i not in always_here_cols]
    newdf = df[desired_col].mean(axis=1).round(3)
    return newdf


gtbench_raw_data = dummydf()
gtbench_raw_data["Average"] = add_average_col(gtbench_raw_data)

column_to_move = "Average"
# Move the column to the desired index
gtbench_raw_data.insert(
    4, column_to_move, gtbench_raw_data.pop(column_to_move))

models = list(set(gtbench_raw_data['Model']))

opponent_models = list(set(gtbench_raw_data['Opponent Model']))


agents = list(set(gtbench_raw_data['Agent']))


opponent_agents = list(set(gtbench_raw_data['Opponent Agent']))

# Searching and filtering


def update_table(
    hidden_df: pd.DataFrame,
    columns: list,
    model1: list,
    model2: list,
    agent1: list,
    agent2: list
):

    filtered_df = select_columns(hidden_df, columns)

    filtered_df = filter_model1(filtered_df, model1)
    filtered_df = filter_model2(filtered_df, model2)
    filtered_df = filter_agent1(filtered_df, agent1)
    filtered_df = filter_agent2(filtered_df, agent2)

    return filtered_df

# triggered only once at startup => read query parameter if it exists


def load_query(request: gr.Request):
    query = request.query_params.get("query") or ""
    return query, query  # return one for the "search_bar", one for a hidden component that triggers a reload only if value has changed


def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
    return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))]


def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
    always_here_cols = [
        "Model", "Agent", "Opponent Model", "Opponent Agent"
    ]
    # We use COLS to maintain sorting
    all_columns = games

    if len(columns) == 0:
        filtered_df = df[
            always_here_cols +
            [c for c in all_columns if c in df.columns]
        ]
        filtered_df["Average"] = add_average_col(filtered_df)
        column_to_move = "Average"
        current_index = filtered_df.columns.get_loc(column_to_move)

        # Move the column to the desired index
        filtered_df.insert(4, column_to_move, filtered_df.pop(column_to_move))
        return filtered_df

    filtered_df = df[
        always_here_cols +
        [c for c in all_columns if c in df.columns and c in columns]
    ]
    if "Average" in columns:
        filtered_df["Average"] = add_average_col(filtered_df)
        # Get the current index of the column
        column_to_move = "Average"
        current_index = filtered_df.columns.get_loc(column_to_move)

        # Move the column to the desired index
        filtered_df.insert(4, column_to_move, filtered_df.pop(column_to_move))
    else:
        if "Average" in filtered_df.columns:
            # Remove the column
            filtered_df = filtered_df.drop(columns=["Average"])

    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["Model"].isin(
        model_query)]
    return filtered_df


def filter_model2(
    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["Opponent Model"].isin(
        model_query)]
    return filtered_df


def filter_agent1(
    df: pd.DataFrame, agent_query: list
) -> pd.DataFrame:
    # Show all models
    if len(agent_query) == 0:
        return df
    filtered_df = df

    filtered_df = filtered_df[filtered_df["Agent"].isin(
        agent_query)]
    return filtered_df


def filter_agent2(
    df: pd.DataFrame, agent_query: list
) -> pd.DataFrame:
    # Show all models
    if len(agent_query) == 0:
        return df
    filtered_df = df

    filtered_df = filtered_df[filtered_df["Opponent Agent"].isin(
        agent_query)]
    return filtered_df


# leaderboard_df = filter_models(leaderboard_df, [t.to_str(" : ") for t in ModelType], list(NUMERIC_INTERVALS.keys()), [i.value.name for i in Precision], False, False)


class LLM_Model:
    def __init__(self, t_value, model_value, average_value, arc_value, hellaSwag_value, mmlu_value) -> None:
        self.t = t_value
        self.model = model_value
        self.average = average_value
        self.arc = arc_value
        self.hellaSwag = hellaSwag_value
        self.mmlu = mmlu_value


games = ["Breakthrough", "Connect Four", "Blind Auction", "Kuhn Poker",
         "Liar's Dice", "Negotiation", "Nim", "Pig", "Iterated Prisoner's Dilemma", "Tic-Tac-Toe"]

# models = ["gpt-35-turbo-1106", "gpt-4", "Llama-2-70b-chat-hf", "CodeLlama-34b-Instruct-hf",
#           "CodeLlama-70b-Instruct-hf", "Mistral-7B-Instruct-v01", "Mistral-7B-OpenOrca"]

# agents = ["Prompt Agent", "CoT Agent", "SC-CoT Agent",
#           "ToT Agent", "MCTS", "Random", "TitforTat"]

demo = gr.Blocks(css=custom_css)


def load_image(image_path):
    image = Image.open(image_path)
    return image


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():

                        shown_columns = gr.CheckboxGroup(
                            choices=[
                                'Average'
                            ]+games,
                            label="Select columns to show",
                            elem_id="column-select",
                            interactive=True,
                        )
                with gr.Column(min_width=320):
                    # with gr.Box(elem_id="box-filter"):
                    model1_column = gr.CheckboxGroup(
                        label="Model",
                        choices=models,
                        interactive=True,
                        elem_id="filter-columns-type",
                    )

                    agent1_column = gr.CheckboxGroup(
                        label="Agents",
                        choices=agents,
                        interactive=True,
                        elem_id="filter-columns-precision",
                    )

                    model2_column = gr.CheckboxGroup(
                        label="Opponent Model",
                        choices=opponent_models,
                        interactive=True,
                        elem_id="filter-columns-type",
                    )
                    agent2_column = gr.CheckboxGroup(
                        label="Opponent Agents",
                        choices=opponent_agents,
                        interactive=True,
                        elem_id="filter-columns-precision",
                    )
                    # filter_columns_size = gr.CheckboxGroup(
                    #     label="Model sizes (in billions of parameters)",
                    #     choices=[f'NUMERIC_INTERVALS{i}' for i in range(0, 5)],
                    #     value=[f'NUMERIC_INTERVALS{i}' for i in range(0, 5)],
                    #     interactive=True,
                    #     elem_id="filter-columns-size",
                    # )

            leaderboard_table = gr.components.Dataframe(
                value=gtbench_raw_data,
                elem_id="leaderboard-table",
                interactive=False,
                visible=True,
                # column_widths=["2%", "33%"]
            )

            game_bench_df_for_search = gr.components.Dataframe(
                value=gtbench_raw_data,
                elem_id="leaderboard-table",
                interactive=False,
                visible=False,
                # column_widths=["2%", "33%"]
            )

            # Dummy leaderboard for handling the case when the user uses backspace key
            # hidden_leaderboard_table_for_search = gr.components.Dataframe(
            #     value=[],
            #     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,
            #         # flagged_models_visibility,
            #         # search_bar,
            #     ],
            #     leaderboard_table,
            # )

            # # Define a hidden component that will trigger a reload only if a query parameter has be set
            # hidden_search_bar = gr.Textbox(value="", visible=False)
            # hidden_search_bar.change(
            #     update_table,
            #     [
            #         hidden_leaderboard_table_for_search,
            #         shown_columns,
            #         filter_columns_type,
            #         filter_columns_precision,
            #         filter_columns_size,
            #         deleted_models_visibility,
            #         flagged_models_visibility,
            #         search_bar,
            #     ],
            #     leaderboard_table,
            # )
            # # Check query parameter once at startup and update search bar + hidden component
            # demo.load(load_query, inputs=[], outputs=[search_bar, hidden_search_bar])

            for selector in [shown_columns, model1_column, model2_column, agent1_column, agent2_column]:
                selector.change(
                    update_table,
                    [
                        game_bench_df_for_search,
                        shown_columns,
                        model1_column,
                        model2_column,
                        agent1_column,
                        agent2_column
                        # filter_columns_precision,
                        # None,  # filter_columns_size,
                        # None,  # deleted_models_visibility,
                        # None,  # flagged_models_visibility,
                        # None,  # search_bar,
                    ],
                    leaderboard_table,
                    queue=True,
                )

        # with gr.TabItem("📈 Metrics through time", elem_id="llm-benchmark-tab-table", id=4):
        #     with gr.Row():
        #         with gr.Column():
        #             chart = create_metric_plot_obj_1(
        #                 dummy_data_for_plot(
        #                     ["Metric1", "Metric2", 'Metric3']),
        #                 ["Metric1", "Metric2", "Metric3"],
        #                 title="Average of Top Scores and Human Baseline Over Time (from last update)",
        #             )
        #             gr.Plot(value=chart, min_width=500)

        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.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 ({9})",
                        open=False,
                    ):
                        with gr.Row():
                            finished_eval_table = gr.components.Dataframe(
                                value=None,
                                headers=EVAL_COLS,
                                datatype=EVAL_TYPES,
                                row_count=5,
                            )
                    with gr.Accordion(
                        f"🔄 Running Evaluation Queue ({5})",
                        open=False,
                    ):
                        with gr.Row():
                            running_eval_table = gr.components.Dataframe(
                                value=None,
                                headers=EVAL_COLS,
                                datatype=EVAL_TYPES,
                                row_count=5,
                            )

                    with gr.Accordion(
                        f"⏳ Pending Evaluation Queue ({7})",
                        open=False,
                    ):
                        with gr.Row():
                            pending_eval_table = gr.components.Dataframe(
                                value=None,
                                headers=EVAL_COLS,
                                datatype=EVAL_TYPES,
                                row_count=5,
                            )
            with gr.Row():
                gr.Markdown("# ✉️✨ Submit your Agent here!",
                            elem_classes="markdown-text")

            with gr.Row():
                with gr.Column():
                    model_name_textbox = gr.Textbox(label="Agent name")
                    # revision_name_textbox = gr.Textbox(
                    #     label="Revision commit", placeholder="main")
                    # private = gr.Checkbox(
                    #     False, label="Private", visible=not IS_PUBLIC)
                    model_type = gr.Dropdown(
                        choices=[t.to_str(" : ")
                                 for t in ModelType if t != ModelType.Unknown],
                        label="Agent type",
                        multiselect=False,
                        value=ModelType.FT.to_str(" : "),
                        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,
    #                 private,
    #                 weight_type,
    #                 model_type,
    #             ],
    #             submission_result,
    #         )

    '''
    with gr.Row():
        with gr.Accordion("📙 Citation", open=False):
            citation_button = gr.Textbox(
                value=CITATION_BUTTON_TEXT,
                label=CITATION_BUTTON_LABEL,
                lines=20,
                elem_id="citation-button",
                show_copy_button=True,
            )

# scheduler = BackgroundScheduler()
# scheduler.add_job(restart_space, "interval", seconds=1800)
# scheduler.start()
demo.launch()
# Both launches the space and its CI
# configure_space_ci(
#     demo.queue(default_concurrency_limit=40),
#     trusted_authors=[],  # add manually trusted authors
#     private="True",  # ephemeral spaces will have same visibility as the main space. Otherwise, set to `True` or `False` explicitly.
#     variables={},  # We overwrite HF_HOME as tmp CI spaces will have no cache
#     secrets=["HF_TOKEN", "H4_TOKEN"],  # which secret do I want to copy from the main space? Can be a `List[str]`."HF_TOKEN", "H4_TOKEN"
#     hardware=None,  # "cpu-basic" by default. Otherwise set to "auto" to have same hardware as the main space or any valid string value.
#     storage=None,  # no storage by default. Otherwise set to "auto" to have same storage as the main space or any valid string value.
# ).launch()


# notes: opponent model , opponent agent
# column is games