1. 모델명 검색 구현 2. repo 재시작 스케줄러

app.py

@@ -1,27 +1,76 @@
 import gradio as gr
 import pandas as pd
 import requests
-
+from datetime import datetime
+from apscheduler.schedulers.background import BackgroundScheduler
 from src.about import (
     INTRODUCTION_TEXT,
     LLM_BENCHMARKS_TEXT,
     TITLE,
 )
 from src.display.css_html_js import custom_css
+from huggingface_hub import HfApi
+repo_id = "qinference/AIM100Leaderboard"
+api = HfApi()
+last_updated =  datetime.now().strftime('Last updated at %Y-%m-%d %H:%m:%S')
+
+
+def restart_leaderboard():
+    api.pause_space(repo_id=repo_id)
+    api.restart_space(repo_id=repo_id)
 
 
 def get_evaluation():
-    response = requests.get("http://aim100.qinference.com/api/leaderboard/list")
-    data_json = response.json()
-    df = pd.DataFrame(data_json)
-    for col in df.columns:
-        df.loc[df[col] == 0, col] = '-'
-    df.insert(0, 'No', df.reset_index().index + 1)
-    ret = df.drop(columns='nodeSeq').rename(columns={'modelName': 'Model'})
-    ret.columns = [x.capitalize() for x in ret.columns]
+    global last_updated
+    try:
+        response = requests.get("http://aim100.qinference.com/api/leaderboard/list")
+        data_json = response.json()
+        df = pd.DataFrame(data_json)
+        for col in df.columns:
+            df.loc[df[col] == 0, col] = '-'
+        df.insert(0, 'No', df.reset_index().index + 1)
+        ret = df.drop(columns='nodeSeq').rename(columns={'modelName': 'Model'})
+        ret.columns = [x.capitalize() for x in ret.columns]
+    except ValueError:
+        ret = default_evaluation()
+
+    last_updated = "<p style='text-align: right; padding-right: 5px;'>" + datetime.now().strftime('Last updated at %Y-%m-%d %H:%m:%S') + "</p>"
     return ret
 
 
+def default_evaluation():
+    global last_updated
+
+    default_data = [{
+        "No": "-",
+        "Model": "-",
+        "Total": "-",
+        "Inference": "-",
+        "Grammar": "-",
+        "Understanding": "-",
+        "Coding": "-",
+        "Math": "-",
+        "Writing": "-",
+        "Etc": "-"
+    }]
+    df = pd.DataFrame(default_data)
+    last_updated = datetime.now().strftime('Last updated at %Y-%m-%d %H:%m:%S')
+    return df
+
+
+# Searching and filtering
+def update_table(
+    hidden_df: pd.DataFrame,
+    query: str,
+):
+    filtered_df = hidden_df[hidden_df["Model"].str.contains(query, case=False)]
+    return filtered_df
+
+
+original_df = get_evaluation()
+leaderboard_df = original_df.copy()
+
+
 leaderboard = gr.Blocks(css=custom_css)
 with leaderboard:
     gr.HTML(TITLE)
@@ -29,15 +78,38 @@ with leaderboard:
 
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
         with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
+            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",
+                )
             leaderboard_table = gr.components.Dataframe(
-                value=get_evaluation(),
+                value=leaderboard_df,
                 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,
+                visible=False,
+            )
+            search_bar.submit(
+                update_table,
+                [
+                    hidden_leaderboard_table_for_search,
+                    search_bar
+                ],
+                leaderboard_table,
+            )
+            scheduler = BackgroundScheduler()
+            scheduler.add_job(restart_leaderboard, "interval", seconds=60)
+            scheduler.start()
+        with gr.Row():
+            gr.HTML(last_updated)
         with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
             gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
 
-
 leaderboard.queue(default_concurrency_limit=40).launch()
+