clean

app.py

@@ -1,11 +1,8 @@
 import gradio as gr
-import pandas as pd
-from src.populate import get_model_info_df, get_merged_df
 
 from src.about import (
     CITATION_BUTTON_LABEL,
     CITATION_BUTTON_TEXT,
-    EVALUATION_QUEUE_TEXT,
     INTRODUCTION_TEXT,
     LLM_BENCHMARKS_TEXT,
     TITLE,
@@ -14,50 +11,23 @@ from src.display.css_html_js import custom_css
 from src.display.utils import (
     BENCHMARK_COLS,
     COLS,
-    EVAL_COLS,
-    EVAL_TYPES,
     AutoEvalColumn,
-    ModelType,
     fields,
-    WeightType,
-    Precision,
 )
-from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
-from src.populate import get_evaluation_queue_df, get_leaderboard_df
-from src.submission.submit import add_new_eval
+from src.envs import (
+    API,
+    EVAL_REQUESTS_PATH,
+    EVAL_RESULTS_PATH,
+    REPO_ID,
+    TOKEN,
+)
+from src.populate import get_leaderboard_df, get_model_info_df, get_merged_df
 
 
 def restart_space():
     API.restart_space(repo_id=REPO_ID, token=TOKEN)
 
 
-### Space initialisation
-# 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()
-
-
 LEADERBOARD_DF = get_leaderboard_df(
     EVAL_RESULTS_PATH + "/leaderboards/BOOM_leaderboard.csv", EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS
 )
@@ -105,15 +75,15 @@ def init_leaderboard(dataframe, model_info_df):
         ]
     )
     merged_df = merged_df[cols]
-    
+
     # Remove hidden columns
     hidden_cols = [c.name for c in fields(AutoEvalColumn) if c.hidden]
-    merged_df = merged_df.drop(columns=[col for col in hidden_cols if col in merged_df.columns], errors='ignore')
-    
+    merged_df = merged_df.drop(columns=[col for col in hidden_cols if col in merged_df.columns], errors="ignore")
+
     # Build datatype list
     col2type_dict = {c.name: c.type for c in fields(AutoEvalColumn)}
     datatype_list = [col2type_dict[col] if col in col2type_dict else "number" for col in merged_df.columns]
-    
+
     # Use native Gradio 6 Dataframe with search and filter
     return gr.Dataframe(
         value=merged_df,