[Update]Add line 69-112

app.py

@@ -66,76 +66,50 @@ raw_data = dummydf()
 methods = list(set(raw_data['Method']))
 metrics = ["Chruch","Parachute","Tench","Garbage Turch","Van Gogh","Violence","Illegal Activity","Nudity"]
 
-# Searching and filtering
-# def update_table(
-#     hidden_df: pd.DataFrame,
-#     columns: list,
-#     type_query: list,
-#     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_queries(query, filtered_df)
-#     df = select_columns(filtered_df, columns)
-#     return df
-
-
-# def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
-#     return df[(df[AutoEvalColumn.model.name].str.contains(query, case=False))]
-
-
-# def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
-#     always_here_cols = [
-#         AutoEvalColumn.model_type_symbol.name,
-#         AutoEvalColumn.model.name,
-#     ]
-#     # We use COLS to maintain sorting
-#     filtered_df = df[
-#         always_here_cols + [c for c in COLS if c in df.columns and c in columns] 
-#     ]
-#     return filtered_df
-
-
-# def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
-#     final_df = []
-#     if query != "":
-#         queries = [q.strip() for q in query.split(";")]
-#         for _q in queries:
-#             _q = _q.strip()
-#             if _q != "":
-#                 temp_filtered_df = search_table(filtered_df, _q)
-#                 if len(temp_filtered_df) > 0:
-#                     final_df.append(temp_filtered_df)
-#         if len(final_df) > 0:
-#             filtered_df = pd.concat(final_df)
-#             filtered_df = filtered_df.drop_duplicates(
-#                 subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name]
-#             )
-
-#     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 update_table(
+    hidden_df: pd.DataFrame,
+    columns_1: list,
+    columns_2: list,
+    columns_3: list,
+    model1: list,
+):
+
+    filtered_df = select_columns(hidden_df, columns_1, columns_2, columns_3)
+
+    filtered_df = filter_model1(filtered_df, model1)
+
+    return filtered_df
+
+
+def select_columns(df: pd.DataFrame, columns_1: list, columns_2: list,  columns_3: list) -> pd.DataFrame:
+    always_here_cols = ["Method"]
+    
+    # We use COLS to maintain sorting
+    all_columns = metrics
+
+    if (len(columns_1)+len(columns_2) + len(columns_3)) == 0:
+        filtered_df = df[
+            always_here_cols +
+            [c for c in all_columns if c in df.columns]
+        ]
+
+    else:
+        filtered_df = df[
+            always_here_cols +
+            [c for c in all_columns if c in df.columns and (c in columns_1 or c in columns_2 or c in columns_3 ) ]
+        ]
+
+    return filtered_df
+
+
+def filter_model1(df: pd.DataFrame, model_query: list) -> pd.DataFrame:
+    # Show all models
+    if len(model_query) == 0:
+        return df
+    
+    filtered_df = df
+    filtered_df = filtered_df[filtered_df["Method"].isin(model_query)]
+    return filtered_df
 
 
 demo = gr.Blocks(css=custom_css)