Update app.py

app.py

@@ -112,53 +112,51 @@ def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
     return filtered_df
 
 
+def filter_models(
+    df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
+) -> pd.DataFrame:
+    # Show all models
+    if show_deleted:
+        filtered_df = df
+    else:  # Show only still on the hub models
+        filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
+
+    type_emoji = [t[0] for t in type_query]
+    filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
+    filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
+
+    numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
+    params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
+    mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
+    filtered_df = filtered_df.loc[mask]
+
+    return filtered_df
+
 # def filter_models(
 #     df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool, italian_only: bool
 # ) -> pd.DataFrame:
 #     # Show all models
 #     if show_deleted:
-#         filtered_df = df
+#         filtered_df = df.copy()
 #     else:  # Show only still on the hub models
-#         filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
+#         filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True].copy()
+
+#     filtered_df[AutoEvalColumn.model.name] = filtered_df[AutoEvalColumn.model.name].apply(lambda x: x.split('>')[-2].split('<')[0] if '<a' in x else x)
 
 #     type_emoji = [t[0] for t in type_query]
-#     filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
-#     filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
+#     filtered_df = filtered_df[filtered_df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
+#     filtered_df = filtered_df[filtered_df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
 
 #     numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
-#     params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
+#     params_column = pd.to_numeric(filtered_df[AutoEvalColumn.params.name], errors="coerce")
 #     mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
-#     filtered_df = filtered_df.loc[mask]
+#     filtered_df = filtered_df[mask]
+
 #     if italian_only:
 #         filtered_df = filtered_df[filtered_df[AutoEvalColumn.author.name] == "🇮🇹"]
 
 #     return filtered_df
 
-def filter_models(
-    df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool, italian_only: bool
-) -> pd.DataFrame:
-    # Show all models
-    if show_deleted:
-        filtered_df = df.copy()
-    else:  # Show only still on the hub models
-        filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True].copy()
-
-    filtered_df[AutoEvalColumn.model.name] = filtered_df[AutoEvalColumn.model.name].apply(lambda x: x.split('>')[-2].split('<')[0] if '<a' in x else x)
-
-    type_emoji = [t[0] for t in type_query]
-    filtered_df = filtered_df[filtered_df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
-    filtered_df = filtered_df[filtered_df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
-
-    numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
-    params_column = pd.to_numeric(filtered_df[AutoEvalColumn.params.name], errors="coerce")
-    mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
-    filtered_df = filtered_df[mask]
-
-    if italian_only:
-        filtered_df = filtered_df[filtered_df[AutoEvalColumn.author.name] == "🇮🇹"]
-
-    return filtered_df
-
 def get_data_totale():
     dataset = pd.read_csv("mmlu_pro_it.csv", sep=',')
     if 'model ' in dataset.columns: