fix(app): search/filter bug fix

app.py

@@ -44,12 +44,12 @@ def update_table(
     hidden_df: pd.DataFrame,
     columns: list,
     type_query: list,
-    precision_query: str,
-    size_query: list,
-    show_deleted: bool,
+    # precision_query: str,
+    # size_query: list,
+    # show_deleted: bool,
     query: str,
 ):
-    filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
+    filtered_df = filter_models(hidden_df, type_query)#, size_query, precision_query, show_deleted)
     filtered_df = filter_queries(query, filtered_df)
     df = select_columns(filtered_df, columns)
     return df
@@ -84,7 +84,7 @@ def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
         if len(final_df) > 0:
             filtered_df = pd.concat(final_df)
             filtered_df = filtered_df.drop_duplicates(
-                subset=[utils.AutoEvalColumn.model.name, utils.AutoEvalColumn.precision.name, utils.AutoEvalColumn.revision.name]
+                subset=[utils.AutoEvalColumn.model.name)#, utils.AutoEvalColumn.precision.name, utils.AutoEvalColumn.revision.name]
             )
 
     return filtered_df
@@ -103,12 +103,12 @@ def filter_models(
     
     type_emoji = [t[0] for t in type_query]
     filtered_df = filtered_df.loc[df[utils.AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
-    filtered_df = filtered_df.loc[df[utils.AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
+    # filtered_df = filtered_df.loc[df[utils.AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
 
-    numeric_interval = pd.IntervalIndex(sorted([utils.NUMERIC_INTERVALS[s] for s in size_query]))
-    params_column = pd.to_numeric(df[utils.AutoEvalColumn.params.name], errors="coerce")
-    mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
-    filtered_df = filtered_df.loc[mask]
+    # numeric_interval = pd.IntervalIndex(sorted([utils.NUMERIC_INTERVALS[s] for s in size_query]))
+    # params_column = pd.to_numeric(df[utils.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