Update app.py

app.py

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