edits to make_dataclass

src/display/utils.py

@@ -1,9 +1,8 @@
-from dataclasses import dataclass, make_dataclass
+from dataclasses import dataclass
 
 """ adapted from original template, where unnecessary code was removed
 util.py is used for defining our fixed columns, which will be referenced to from app.py
-dataclass was used to ensure our changes made are reflected across all instances
-our metric columns are added dynamically, as not all json files have same eval methods used"""
+ColumnContent dataclass used to define column properties"""
 
 def fields(raw_class):
     return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
@@ -17,14 +16,11 @@ class ColumnContent:
     never_hidden: bool = False
 
 ## Leaderboard columns
-auto_eval_column_dict = []
-# Init the fixed columns, eval methods and their results is added dynamically, different runs have different num of results
-auto_eval_column_dict.append(["run_id", ColumnContent, ColumnContent("run_id", "str", True, never_hidden=True)])
-auto_eval_column_dict.append(["technique", ColumnContent, ColumnContent("technique", "str", True, never_hidden=True)])
-
-# list is converted to dataclass
-AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
-# the list is converted into a class, each element in that list, become a class attribute
+# manually create class instead of using make_dataclass as our columns are not built dynamically, 
+# the given and known cols are the run id and technique, based on our results json file
+class AutoEvalColumn:
+    run_id = ColumnContent("run_id", "str", True, never_hidden= True)
+    technique = ColumnContent("technique", "str", True, never_hidden= True)
 
 # Column selection
 COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]