Clean up

app.py

@@ -301,15 +301,15 @@ def toggle_all_categories(action: str) -> list[gr.CheckboxGroup]:
     return results
 
 
-def plot_size_vs_score(df: pd.DataFrame, hidden_df: pd.DataFrame) -> go.Figure:
-    df2 = hidden_df[hidden_df[AutoEvalColumn.row_id.name].isin(df[AutoEvalColumn.row_id.name])]
-    df2 = df2[df2["#Params (B)"] > 0]
-    df2 = df2[["model_name_for_query", "#Params (B)", "AVG", "Few-shot"]]
-    df2["AVG"] = df2["AVG"].astype(float)
-    df2 = df2.rename(columns={"model_name_for_query": "Model", "Few-shot": "n-shot"})
-    df2["model_name_without_org_name"] = df2["Model"].str.split("/").str[-1] + " (" + df2["n-shot"] + "-shot)"
+def plot_size_vs_score(df_filtered: pd.DataFrame, df_original: pd.DataFrame) -> go.Figure:
+    df = df_original[df_original[AutoEvalColumn.row_id.name].isin(df_filtered[AutoEvalColumn.row_id.name])]
+    df = df[df["#Params (B)"] > 0]
+    df = df[["model_name_for_query", "#Params (B)", "AVG", "Few-shot"]]
+    df["AVG"] = df["AVG"].astype(float)
+    df = df.rename(columns={"model_name_for_query": "Model", "Few-shot": "n-shot"})
+    df["model_name_without_org_name"] = df["Model"].str.split("/").str[-1] + " (" + df["n-shot"] + "-shot)"
     fig = px.scatter(
-        df2,
+        df,
         x="#Params (B)",
         y="AVG",
         text="model_name_without_org_name",
@@ -328,16 +328,16 @@ TASK_AVG_NAME_MAP = {
 }
 
 
-def plot_average_scores(df: pd.DataFrame, hidden_df: pd.DataFrame) -> go.Figure:
-    df2 = hidden_df[hidden_df[AutoEvalColumn.row_id.name].isin(df[AutoEvalColumn.row_id.name])]
-    df2 = df2[["model_name_for_query", "Few-shot"] + list(TASK_AVG_NAME_MAP.keys())]
-    df2 = df2.rename(columns={"model_name_for_query": "Model", "Few-shot": "n-shot"})
-    df2 = df2.rename(columns=TASK_AVG_NAME_MAP)
-    df2["n-shot"] = df2["n-shot"].astype(int)
-    df2 = df2.set_index(["Model", "n-shot"]).astype(float)
+def plot_average_scores(df_filtered: pd.DataFrame, df_original: pd.DataFrame) -> go.Figure:
+    df = df_original[df_original[AutoEvalColumn.row_id.name].isin(df_filtered[AutoEvalColumn.row_id.name])]
+    df = df[["model_name_for_query", "Few-shot"] + list(TASK_AVG_NAME_MAP.keys())]
+    df = df.rename(columns={"model_name_for_query": "Model", "Few-shot": "n-shot"})
+    df = df.rename(columns=TASK_AVG_NAME_MAP)
+    df["n-shot"] = df["n-shot"].astype(int)
+    df = df.set_index(["Model", "n-shot"]).astype(float)
 
     fig = go.Figure()
-    for i, ((name, n_shot), row) in enumerate(df2.iterrows()):
+    for i, ((name, n_shot), row) in enumerate(df.iterrows()):
         visible = True if i < 3 else "legendonly"  # Display only the first 3 models
         fig.add_trace(
             go.Scatterpolar(