Delete app.py

app.py

@@ -1,196 +0,0 @@
-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)