app.py

import subprocess
import gradio as gr
import zipfile
import os
import shutil
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download, Repository, HfFolder

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, EVAL_REQUESTS_PATH_BACKEND, EVAL_RESULTS_PATH_BACKEND
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval
from src.submission.evaluate import calculate_metrics

def handle_new_eval_submission(model_name, model_zip, model_link=None):
    
    if not model_name:
        return "Please enter a model name."
    
    # check if the model name is already in the leaderboard
    if model_name in leaderboard_df[AutoEvalColumn.model.name].values:
        return "Model name already exists in the leaderboard. Please choose a different name."
    
    # check if the zip file is actually a zip file
    if model_zip is None or not zipfile.is_zipfile(model_zip):
        return "Please upload a valid zip file."
    
    # check if the model name is only one word
    if len(model_name.split()) > 1:
        return "Model name should be a single word with hyphens."
    
    extraction_path = EVAL_RESULTS_PATH_BACKEND

    if not os.path.exists(extraction_path):
        os.makedirs(extraction_path)
        
    # define path for the zip file to be extracted to
    extraction_path = os.path.join(extraction_path, model_name)

    if model_zip is not None:
        with zipfile.ZipFile(model_zip, 'r') as zip_ref:
            zip_ref.extractall(extraction_path)
    print("File unzipped successfully to:", extraction_path)
        
    # Evaluate the model's performance
    calculate_metrics(extraction_path, model_name)
    
    # upload to results repo
    API.upload_file(
        path_or_fileobj=os.path.join(os.getcwd(), EVAL_RESULTS_PATH, '3d-pope', model_name, 'results.json'),
        path_in_repo=os.path.join('3d-pope', model_name, 'results.json'),
        repo_id=RESULTS_REPO,
        repo_type="dataset",
        )
    
    restart_space()
    
    return "Submission received and results are being processed. Please check the leaderboard for updates."



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()

def custom_format(x):
    if pd.isna(x):
        return x  # Return as is if NaN
    try:
        float_x = float(x)
        if float_x.is_integer():
            return f"{int(float_x)}"
        else:
            return f"{float_x:.2f}".rstrip('0').rstrip('.')
    except ValueError:
        return x  # Return as is if conversion to float fails

numeric_cols = [col for col in leaderboard_df.columns if leaderboard_df[col].dtype in ['float64', 'float32']]
leaderboard_df[numeric_cols] = leaderboard_df[numeric_cols].applymap(custom_format)

(
    finished_eval_queue_df,
    running_eval_queue_df,
    pending_eval_queue_df,
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)


# Searching and filtering
def update_table(
    hidden_df: pd.DataFrame,
    columns: list,
    # type_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, hidden_df)
    df = select_columns(filtered_df, columns)
    return df


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


def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
    always_here_cols = [
    #     AutoEvalColumn.model_type_symbol.name,
        AutoEvalColumn.model.name,
    ]
    # We use COLS to maintain sorting
    filtered_df = df[
        always_here_cols + [c for c in COLS if c in df.columns and c in columns] 
    ]
    return filtered_df


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)
            existing_columns = [col for col in [AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name] if col in filtered_df.columns]
            filtered_df = filtered_df.drop_duplicates(subset=existing_columns)

    return filtered_df



def filter_models(
    df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
) -> pd.DataFrame:
    # Show all models
    # if show_deleted:
    #     filtered_df = df
    # else:  # Show only still on the hub models
    #     filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
    
    filtered_df = df

    type_emoji = [t[0] for t in type_query]
    # filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
    # filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]

    numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
    params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
    mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
    filtered_df = filtered_df.loc[mask]

    return filtered_df


demo = gr.Blocks(css=custom_css)
with demo:
    gr.HTML(TITLE)
    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("🏅 3D-POPE Benchmark", elem_id="llm-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():
                        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"):

            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,
                    # deleted_models_visibility,
                    search_bar,
                ],
                leaderboard_table,
            )

            for selector in [shown_columns]:
                selector.change(
                    update_table,
                    [
                        hidden_leaderboard_table_for_search,
                        shown_columns,
                        # 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.Row():
                gr.Markdown("# 📋 Submit your results here!", elem_classes="markdown-text")

            with gr.Row():
                    model_name_textbox = gr.Textbox(label="Model name")
                    model_zip_file = gr.File(label="Upload model prediction result ZIP file")
                    # model_link_textbox = gr.Textbox(label="Link to model page")
            with gr.Row():
                gr.Column()
                with gr.Column(scale=2):
                    submit_button = gr.Button("Submit Model")
                    submission_result = gr.Markdown()

                    submit_button.click(
                        handle_new_eval_submission,
                        [model_name_textbox, model_zip_file],
                        submission_result
                    )
                gr.Column()

    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.queue(default_concurrency_limit=40).launch()