[Update]Comment line 70-138

app.py

@@ -67,75 +67,75 @@ 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: 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
 
 
 demo = gr.Blocks(css=custom_css)