udpate the results format

app.py

@@ -14,6 +14,7 @@ from src.display.about import (
 )
 from src.display.css_html_js import custom_css
 from src.envs import API
+from src.leaderboard.load_results import load_data
 
 # clone / pull the lmeh eval data
 TOKEN = os.environ.get("TOKEN", None)
@@ -29,62 +30,16 @@ snapshot_download(
 def restart_space():
     API.restart_space(repo_id="SeaLLMs/SeaExam_leaderboard", token=TOKEN)
 
-# Load the CSV file
-# def load_csv(file_path):
-#     data = pd.read_csv(file_path)
-#     return data
-
-def load_data(data_path):
-    df = pd.read_csv(data_path, skiprows=1, header=0).dropna()
-
-    columns = ['Model', 'type', 'open?', 'shot', 'en', 'zh', 'id', 'th', 'vi', 'avg', 'avg_sea']
-    
-
-    # Splitting into three separate DataFrames based on the groups M3Exam and MMLU and average
-    df_m3exam = df.iloc[:, :11]  # M3Exam columns
-    df_mmlu = df.iloc[:, [0, 1, 2, 3, 11, 12, 13, 14, 15, 16, 17]]  # MMLU columns
-    df_avg = df.iloc[:, [0, 1, 2, 3, 18, 19, 20, 21, 22, 23, 24]]  # Average columns
-    df_mmlu.columns = columns
-    df_avg.columns = columns
-
-    # # multiply the values in the ['en', 'zh', 'id', 'th', 'vi', 'avg', 'avg_sea'] by 100 and display as 1 decimal
-    for df_tmp in [df_m3exam, df_mmlu, df_avg]:
-        df_tmp[['en', 'zh', 'id', 'th', 'vi', 'avg', 'avg_sea']] *= 100
-        df_tmp[['en', 'zh', 'id', 'th', 'vi', 'avg', 'avg_sea']] = df_tmp[['en', 'zh', 'id', 'th', 'vi', 'avg', 'avg_sea']].round(2)
-        df_tmp['rank'] = df_tmp['avg'].rank(ascending=False).astype(int)
-    # change the order of the columns to ['Model', 'type', 'open?', 'shot', 'avg', 'avg_sea', 'en', 'zh', 'id', 'th', 'vi'] 
-    # and sort the columns by 'avg' in descending order
-    columns_sorted = ['rank','Model', 'type', 'open?', 'shot', 'avg', 'avg_sea', 'en', 'zh', 'id', 'th', 'vi']
-    df_m3exam = df_m3exam[columns_sorted].sort_values(by='avg', ascending=False)
-    df_mmlu = df_mmlu[columns_sorted].sort_values(by='avg', ascending=False)
-    df_avg = df_avg[columns_sorted].sort_values(by='avg', ascending=False)
-
-    # change the column name from 'avg' to 'avg⬆️'
-    df_m3exam = df_m3exam.rename(columns={'avg': 'avg⬆️'})
-    df_mmlu = df_mmlu.rename(columns={'avg': 'avg⬆️'})
-    df_avg = df_avg.rename(columns={'avg': 'avg⬆️'})
-    
-    return df_m3exam, df_mmlu, df_avg
-
-# Example path to your CSV file
+# Load the data from the csv file
 csv_path = f'{EVAL_RESULTS_PATH}/SeaExam_results.csv'
-# data = load_csv(csv_path)
-
 df_m3exam, df_mmlu, df_avg = load_data(csv_path)
 
-# def show_data():
-#     return data
-
-# iface = gr.Interface(fn=show_data, inputs = None, outputs="dataframe", title="SeaExam Leaderboard", 
-#                      description="Leaderboard for the SeaExam competition.")
-# iface.launch()
-
 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("🏅 Summary", elem_id="llm-benchmark-Sum", id=0):
+        with gr.TabItem("🏅 Overall", elem_id="llm-benchmark-Sum", id=0):
             leaderboard_table = gr.components.Dataframe(
                 value=df_avg,
                 # value=leaderboard_df[

src/leaderboard/load_results.py

@@ -0,0 +1,38 @@
+import pandas as pd
+
+def load_data(data_path):
+    df = pd.read_csv(data_path, skiprows=1, header=0).dropna()
+
+    columns = ['Model', 'type', 'open?', 'shot', 'en', 'zh', 'id', 'th', 'vi', 'avg', 'avg_sea']
+    
+    # Splitting into three separate DataFrames based on the groups M3Exam and MMLU and average
+    df_m3exam = df.iloc[:, :11]  # M3Exam columns
+    df_mmlu = df.iloc[:, [0, 1, 2, 3, 11, 12, 13, 14, 15, 16, 17]]  # MMLU columns
+    df_avg = df.iloc[:, [0, 1, 2, 3, 18, 19, 20, 21, 22, 23, 24]]  # Average columns
+    df_mmlu.columns = columns
+    df_avg.columns = columns
+
+    # # multiply the values in the ['en', 'zh', 'id', 'th', 'vi', 'avg', 'avg_sea'] by 100 and display as 1 decimal
+    for df_tmp in [df_m3exam, df_mmlu, df_avg]:
+        df_tmp[['en', 'zh', 'id', 'th', 'vi', 'avg', 'avg_sea']] *= 100
+        df_tmp[['en', 'zh', 'id', 'th', 'vi', 'avg', 'avg_sea']] = df_tmp[['en', 'zh', 'id', 'th', 'vi', 'avg', 'avg_sea']].round(2)
+        df_tmp['rank'] = df_tmp['avg'].rank(ascending=False).astype(int)
+        
+    # change the order of the columns to ['Model', 'type', 'open?', 'shot', 'avg', 'avg_sea', 'en', 'zh', 'id', 'th', 'vi'] 
+    # and sort the columns by 'avg' in descending order
+    columns_sorted = ['rank','type', 'Model', 'open?', 'shot', 'avg', 'avg_sea', 'en', 'zh', 'id', 'th', 'vi']
+    df_m3exam = df_m3exam[columns_sorted].sort_values(by='avg', ascending=False)
+    df_mmlu = df_mmlu[columns_sorted].sort_values(by='avg', ascending=False)
+    df_avg = df_avg[columns_sorted].sort_values(by='avg', ascending=False)
+
+    # change the column name from 'avg' to 'avg⬆️'
+    df_m3exam = df_m3exam.rename(columns={'avg': 'avg⬆️'})
+    df_mmlu = df_mmlu.rename(columns={'avg': 'avg⬆️'})
+    df_avg = df_avg.rename(columns={'avg': 'avg⬆️'})
+
+    # map the values in the 'type' column to the following values: {'base': 'Base', 'chat': 'Chat'}
+    df_m3exam['type'] = df_m3exam['type'].map({'base': '🟢', 'chat': '🔶'})
+    df_mmlu['type'] = df_mmlu['type'].map({'base': '🟢', 'chat': '🔶'})
+    df_avg['type'] = df_avg['type'].map({'base': '🟢', 'chat': '🔶'})
+    
+    return df_m3exam, df_mmlu, df_avg