Update app.py

app.py

@@ -112,50 +112,49 @@ 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
+    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()
 
     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]
-
-    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 = filtered_df[mask]
 
-#     filtered_df[AutoEvalColumn.model.name] = filtered_df[AutoEvalColumn.model.name].apply(lambda x: x.split('>')[-2].split('<')[0] if '<a' in x else x)
+    if italian_only:
+        filtered_df = filtered_df[filtered_df[AutoEvalColumn.author.name] == "🇮🇹"]
 
-#     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
 
-#     return filtered_df
 
 def get_data_totale():
     dataset = pd.read_csv("mmlu_pro_it.csv", sep=',')