add filter: Accuracy Threshold

app.py

@@ -34,6 +34,7 @@ def update_table(
     llm_query: list,
     llm_provider_query: list,
     accuracy_method_query: str,
+    accuracy_threshold_query: str,
     use_case_area_query: list,
     use_case_query: list,
     use_case_type_query: list,
@@ -48,6 +49,7 @@ def update_table(
     filtered_df = filter_llm_func(hidden_df, llm_query)
     filtered_df = filter_llm_provider_func(filtered_df, llm_provider_query)
     filtered_df = filter_accuracy_method_func(filtered_df, accuracy_method_query)
+    filtered_df = filter_accuracy_threshold_func(filtered_df, accuracy_threshold_query)
 
     filtered_df["Use Case Area"] = filtered_df["Use Case Name"].apply(lambda x: x.split(": ")[0])
     filtered_df = filter_use_case_area_func(filtered_df, use_case_area_query)
@@ -63,16 +65,22 @@ def init_leaderboard_df(
     llm_query: list,
     llm_provider_query: list,
     accuracy_method_query: str,
+    accuracy_threshold_query: str,
     use_case_area_query: list,
     use_case_query: list,
     use_case_type_query: list,
 ):
+
+    # Applying the style function
+    # df = leaderboard_df[[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value]
+    # return df.style.apply(highlight_cols, axis=None)
     return update_table(
         leaderboard_df,
         columns,
         llm_query,
         llm_provider_query,
         accuracy_method_query,
+        accuracy_threshold_query,
         use_case_area_query,
         use_case_query,
         use_case_type_query,
@@ -83,6 +91,11 @@ def filter_accuracy_method_func(df: pd.DataFrame, accuracy_method_query: str) ->
     return df[df["Accuracy Method"] == accuracy_method_query]
 
 
+def filter_accuracy_threshold_func(df: pd.DataFrame, accuracy_threshold_query: str) -> pd.DataFrame:
+    accuracy_cols = ["Instruction Following", "Conciseness", "Completeness", "Accuracy"]
+    return df[(df.loc[:, accuracy_cols] >= float(accuracy_threshold_query)).all(axis=1)]
+
+
 def filter_use_case_area_func(df: pd.DataFrame, use_case_area_query: list) -> pd.DataFrame:
     return df[
         df["Use Case Area"].apply(
@@ -108,10 +121,6 @@ def filter_llm_provider_func(df: pd.DataFrame, llm_provider_query: list) -> pd.D
     return df[df["LLM Provider"].isin(llm_provider_query)]
 
 
-# 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.name,
@@ -298,6 +307,7 @@ with demo:
                     filter_llm.value,
                     filter_llm_provider.value,
                     filter_accuracy_method.value,
+                    filter_accuracy_threshold.value,
                     filter_use_case_area.value,
                     filter_use_case.value,
                     filter_use_case_type.value,
@@ -334,6 +344,7 @@ with demo:
                 filter_llm,
                 filter_llm_provider,
                 filter_accuracy_method,
+                filter_accuracy_threshold,
                 filter_use_case_area,
                 filter_use_case,
                 filter_use_case_type,
@@ -350,6 +361,7 @@ with demo:
                         filter_llm,
                         filter_llm_provider,
                         filter_accuracy_method,
+                        filter_accuracy_threshold,
                         filter_use_case_area,
                         filter_use_case,
                         filter_use_case_type,