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Running
Clémentine
commited on
Commit
•
ef5b51c
1
Parent(s):
5140860
fix model search
Browse files- app.py +18 -18
- model_info_cache.pkl +2 -2
- model_size_cache.pkl +2 -2
- src/display_models/get_model_metadata.py +1 -1
app.py
CHANGED
@@ -224,7 +224,6 @@ def change_tab(query_param: str):
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# Searching and filtering
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def update_table(
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hidden_df: pd.DataFrame,
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-
current_columns_df: pd.DataFrame,
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columns: list,
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type_query: list,
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precision_query: str,
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@@ -233,16 +232,7 @@ def update_table(
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query: str,
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):
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filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
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-
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if query != "":
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queries = query.split(";")
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for _q in queries:
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if _q != "":
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temp_filtered_df = search_table(filtered_df, _q)
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if len(temp_filtered_df) > 0:
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final_df.append(temp_filtered_df)
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if len(final_df) > 0:
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filtered_df = pd.concat(final_df).drop_duplicates()
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df = select_columns(filtered_df, columns)
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return df
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@@ -250,7 +240,6 @@ def update_table(
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def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
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return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))]
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-
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def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
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always_here_cols = [
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AutoEvalColumn.model_type_symbol.name,
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@@ -274,6 +263,23 @@ NUMERIC_INTERVALS = {
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}
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def filter_models(
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df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
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) -> pd.DataFrame:
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@@ -409,7 +415,6 @@ with demo:
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update_table,
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[
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hidden_leaderboard_table_for_search,
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leaderboard_table,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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@@ -423,7 +428,6 @@ with demo:
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update_table,
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[
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hidden_leaderboard_table_for_search,
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leaderboard_table,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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@@ -438,7 +442,6 @@ with demo:
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update_table,
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[
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hidden_leaderboard_table_for_search,
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leaderboard_table,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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@@ -453,7 +456,6 @@ with demo:
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update_table,
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[
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hidden_leaderboard_table_for_search,
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leaderboard_table,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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@@ -468,7 +470,6 @@ with demo:
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update_table,
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[
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hidden_leaderboard_table_for_search,
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leaderboard_table,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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@@ -483,7 +484,6 @@ with demo:
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update_table,
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[
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hidden_leaderboard_table_for_search,
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leaderboard_table,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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# Searching and filtering
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def update_table(
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hidden_df: pd.DataFrame,
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columns: list,
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type_query: list,
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precision_query: str,
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query: str,
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):
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filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
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+
filtered_df = filter_queries(query, filtered_df)
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df = select_columns(filtered_df, columns)
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return df
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def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
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return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))]
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def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
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always_here_cols = [
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AutoEvalColumn.model_type_symbol.name,
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}
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+
def filter_queries(query: str, filtered_df: pd.DataFrame):
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"""Added by Abishek"""
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final_df = []
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if query != "":
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queries = [q.strip() for q in query.split(";")]
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for _q in queries:
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_q = _q.strip()
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if _q != "":
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temp_filtered_df = search_table(filtered_df, _q)
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if len(temp_filtered_df) > 0:
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final_df.append(temp_filtered_df)
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if len(final_df) > 0:
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filtered_df = pd.concat(final_df)
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filtered_df = filtered_df.drop_duplicates(subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name])
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return filtered_df
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def filter_models(
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df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
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) -> pd.DataFrame:
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update_table,
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[
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hidden_leaderboard_table_for_search,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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update_table,
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[
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hidden_leaderboard_table_for_search,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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update_table,
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[
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hidden_leaderboard_table_for_search,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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update_table,
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[
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hidden_leaderboard_table_for_search,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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update_table,
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[
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hidden_leaderboard_table_for_search,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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update_table,
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[
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hidden_leaderboard_table_for_search,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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model_info_cache.pkl
CHANGED
@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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-
oid sha256:
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-
size
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:94e311e2414e80b8eb5e50844c2e79daa4bd3bb6be516fc2448bd05242d125f9
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+
size 3656702
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model_size_cache.pkl
CHANGED
@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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-
oid sha256:
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-
size
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:4aedc91b51cf257cbe3e26a1fdd99e19250bacfa619a64dd85e67d4ff383130f
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+
size 75455
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src/display_models/get_model_metadata.py
CHANGED
@@ -40,7 +40,7 @@ def get_model_infos_from_hub(leaderboard_data: List[dict]):
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try:
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model_info = api.model_info(model_name)
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model_info_cache[model_name] = model_info
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-
except huggingface_hub.utils._errors.RepositoryNotFoundError:
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print("Repo not found!", model_name)
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model_data[AutoEvalColumn.license.name] = None
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model_data[AutoEvalColumn.likes.name] = None
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try:
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model_info = api.model_info(model_name)
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model_info_cache[model_name] = model_info
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+
except (huggingface_hub.utils._errors.RepositoryNotFoundError, huggingface_hub.utils._errors.HfHubHTTPError):
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print("Repo not found!", model_name)
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model_data[AutoEvalColumn.license.name] = None
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model_data[AutoEvalColumn.likes.name] = None
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