Update app.py

app.py

@@ -26,35 +26,25 @@ def get_leaderboard_df(score_path):
                     if sub_key != "20":
                         continue
                     #row[f"{key}_{sub_key}"] = sub_value
-                    row[f"{key}"] = sub_value
+                    row[f"{key.replace('_', ' ')}"] = sub_value
             else:
                 row[key] = value
         rows.append(row)
 
     df = pd.DataFrame(rows)
-    return df
-
-
-leaderboard_df = get_leaderboard_df("score.json")
+    df.iloc[:, 1:] = df.iloc[:, 1:].applymap(lambda x: f"{x * 100:.2f}" if isinstance(x, (int, float)) else x)
 
+    return df
 
-def agg_df(df, agg: str = "max"):
-    df = df.copy()
-    # Drop date and aggregate results by model name
-    df = df.drop("Date", axis=1).groupby("Model").agg(agg).reset_index()
-
-    df.insert(loc=1, column="Average", value=df.mean(axis=1, numeric_only=True))
 
-    # Convert all values to percentage
-    df[df.select_dtypes(include=["number"]).columns] *= 100.0
-    df = df.sort_values(by=["Average"], ascending=False)
-    return df
+leaderboard_df = get_leaderboard_df("output/score.json")
 
 
 # Function to update the table based on search query
 def filter_and_search(cols: list[str], search_query: str, agg: str):
+    print("filter")
     df = leaderboard_df
-    df = agg_df(df, agg)
+    search_terms = "Model"
     if len(search_query) > 0:
         search_terms = search_query.split(";")
         search_terms = [term.strip().lower() for term in search_terms]
@@ -62,12 +52,16 @@ def filter_and_search(cols: list[str], search_query: str, agg: str):
         df = df[df["Model"].str.lower().str.contains(pattern, regex=True)]
         # Drop any columns which are all NaN
         df = df.dropna(how="all", axis=1)
+
     if len(cols) > 0:
         index_cols = list(leaderboard_df.columns[:1])
         new_cols = index_cols + cols
         df = df.copy()[new_cols]
         df = df.copy().dropna(how="all", axis=0, subset=[c for c in df.columns if c in cols])
-        df.insert(loc=1, column="Average", value=df.mean(axis=1, numeric_only=True))
+
+        df[cols] = df[cols].apply(pd.to_numeric, errors='coerce')
+        df = df.sort_values(by=cols, ascending=False, na_position='last')
+        df[cols] = df[cols].astype(str)
     return df
 
 
@@ -79,21 +73,6 @@ with demo:
         gr.Markdown(DESCRIPTION, elem_classes="markdown-text")
         with gr.Row():
             search_bar = gr.Textbox(placeholder="Search for your model...", show_label=False)
-            agg = gr.Radio(
-                ["min", "max", "mean"],
-                value="max",
-                label="Aggregation",
-                info="How to aggregate results for each model",
-            )
-
-        # with gr.Row():
-        #     agg = gr.Radio(
-        #         ["20", "50", "100", "200"],
-        #         value="20",
-        #         label="Threshold",
-        #         info="The threshold of gui",
-        #     )
-
 
         with gr.Row():
             cols_bar = gr.CheckboxGroup(
@@ -109,7 +88,7 @@ with demo:
             )
 
         threshold_text = gr.HTML("Threshold corresponding to the values of gui and embodied: 20")
-    cols_bar.change(filter_and_search, inputs=[cols_bar, search_bar, agg], outputs=[leaderboard_table])
-    agg.change(filter_and_search, inputs=[cols_bar, search_bar, agg], outputs=[leaderboard_table])
-    search_bar.submit(filter_and_search, inputs=[cols_bar, search_bar, agg], outputs=[leaderboard_table])
+    cols_bar.change(filter_and_search, inputs=[cols_bar, search_bar], outputs=[leaderboard_table])
+    search_bar.submit(filter_and_search, inputs=[cols_bar, search_bar], outputs=[leaderboard_table])
+
 demo.launch()