app.py

import json
import gzip
import gradio as gr
from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
from io import StringIO

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,
    BENCHMARK_COLS_MULTIMODAL,
    BENCHMARK_COLS_MIB,
    COLS,
    COLS_MIB,
    COLS_MULTIMODAL,
    EVAL_COLS,
    EVAL_TYPES,
    AutoEvalColumn,
    AutoEvalColumn_mib,
    fields,
)
from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN, RESULTS_REPO_MIB_SUBGRAPH, EVAL_RESULTS_MIB_SUBGRAPH_PATH
from src.populate import get_evaluation_queue_df, get_leaderboard_df, get_leaderboard_df_mib
from src.submission.submit import add_new_eval

print("restart_space ")

def restart_space():
    API.restart_space(repo_id=REPO_ID)
    
print("end restart_space")


print("Space initialisation ")
### Space initialisation
print("EVAL_REQUESTS_PATH")
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()

print("EVAL_RESULTS_PATH")
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()

print("RESULTS_REPO_MIB_SUBGRAPH")
try:
    print(RESULTS_REPO_MIB_SUBGRAPH)
    snapshot_download(
        repo_id=RESULTS_REPO_MIB_SUBGRAPH, local_dir=EVAL_RESULTS_MIB_SUBGRAPH_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
    )
except Exception:
    restart_space()

print("End Space initialisation ")


LEADERBOARD_DF_MIB_SUBGRAPH = get_leaderboard_df_mib(EVAL_RESULTS_MIB_SUBGRAPH_PATH, EVAL_REQUESTS_PATH, COLS_MIB, BENCHMARK_COLS_MIB)

# LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
# LEADERBOARD_DF_MULTIMODAL = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS_MULTIMODAL, BENCHMARK_COLS_MULTIMODAL)

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


def init_leaderboard_mib(dataframe, track):
    print(f"init_leaderboard_mib: dataframe head before loc is {dataframe.head()}\n")
    
    if dataframe is None or dataframe.empty:
        raise ValueError("Leaderboard DataFrame is empty or None.")

    # filter for correct track
    # dataframe = dataframe.loc[dataframe["Track"] == track]

    print(f"init_leaderboard_mib: dataframe head after loc is {dataframe.head()}\n")
    
    return Leaderboard(
        value=dataframe,
        datatype=[c.type for c in fields(AutoEvalColumn_mib)],
        select_columns=SelectColumns(
            default_selection=[c.name for c in fields(AutoEvalColumn_mib) if c.displayed_by_default],
            cant_deselect=[c.name for c in fields(AutoEvalColumn_mib) if c.never_hidden],
            label="Select Columns to Display:",
        ),
        search_columns=["Method"],  # Changed from AutoEvalColumn_mib.model.name to "Method"
        hide_columns=[c.name for c in fields(AutoEvalColumn_mib) if c.hidden],
        bool_checkboxgroup_label="Hide models",
        interactive=False,
    )

def init_leaderboard(dataframe, track):
    if dataframe is None or dataframe.empty:
        raise ValueError("Leaderboard DataFrame is empty or None.")
    # filter for correct track
    dataframe = dataframe.loc[dataframe["Track"] == track]

    # print(f"\n\n\n dataframe is {dataframe}\n\n\n")
    
    return Leaderboard(
        value=dataframe,
        datatype=[c.type for c in fields(AutoEvalColumn)],
        select_columns=SelectColumns(
            default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
            cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
            label="Select Columns to Display:",
        ),
        search_columns=[AutoEvalColumn.model.name],
        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
        bool_checkboxgroup_label="Hide models",
        interactive=False,
    )

def process_json(temp_file):
    if temp_file is None:
        return {}

    # Handle file upload
    try:
        file_path = temp_file.name
        if file_path.endswith('.gz'):
            with gzip.open(file_path, 'rt') as f:
                data = json.load(f)
        else:
            with open(file_path, 'r') as f:
                data = json.load(f)
    except Exception as e:
        raise gr.Error(f"Error processing file: {str(e)}")

    gr.Markdown("Upload successful!")
    return data


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("Strict", elem_id="strict-benchmark-tab-table", id=0):
        #     leaderboard = init_leaderboard(LEADERBOARD_DF, "strict")
        # with gr.TabItem("Strict-small", elem_id="strict-small-benchmark-tab-table", id=1):
        #     leaderboard = init_leaderboard(LEADERBOARD_DF, "strict-small")
        # with gr.TabItem("Multimodal", elem_id="multimodal-benchmark-tab-table", id=2):
        #     leaderboard = init_leaderboard(LEADERBOARD_DF_MULTIMODAL, "multimodal")

        # with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=4):
        #     gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
 
        # with gr.TabItem("👶 Submit", elem_id="llm-benchmark-tab-table", id=5):
        #     with gr.Column():
        #         with gr.Row():                                                                                                           
        #             gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
        
        with gr.TabItem("Subgraph", elem_id="subgraph", id=0):
            leaderboard = init_leaderboard_mib(LEADERBOARD_DF_MIB_SUBGRAPH, "Subgraph")
            # leaderboard = init_leaderboard_mib(LEADERBOARD_DF, "mib")
        
        with gr.TabItem("Causal Graph", elem_id="causalgraph", id=1):
            leaderboard = init_leaderboard_mib(LEADERBOARD_DF_MIB_SUBGRAPH, "Causal Graph")
        
    # 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(share=True, ssr_mode=False)