from dataclasses import dataclass, make_dataclass, field from enum import Enum import pandas as pd from src.about import Tasks def fields(raw_class): return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"] # Define ColumnContent class @dataclass class ColumnContent: name: str type: str displayed_by_default: bool hidden: bool = False never_hidden: bool = False # Define auto_eval_column_dict with correct structure auto_eval_column_dict = [ ("model", ColumnContent, field(default_factory=lambda: ColumnContent("Model", "markdown", True, never_hidden=True))), ("org", ColumnContent, field(default_factory=lambda: ColumnContent("Organization", "str", True))), ("average", ColumnContent, field(default_factory=lambda: ColumnContent("Aiera Score ⬆️", "number", True))), ] # Add task-specific columns for task in Tasks: auto_eval_column_dict.append( (task.value.benchmark, ColumnContent, field(default_factory=lambda task=task: ColumnContent(task.value.col_name, "number", True))) ) # Add remaining columns auto_eval_column_dict.extend([ ("params", ColumnContent, field(default_factory=lambda: ColumnContent("#Params (B)", "number", False))), ("still_on_hub", ColumnContent, field(default_factory=lambda: ColumnContent("Available on the hub", "bool", False))), ("license", ColumnContent, field(default_factory=lambda: ColumnContent("License", "str", False))), ]) # Dynamically create the AutoEvalColumn dataclass AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True) ## For the queue columns in the submission tab @dataclass(frozen=True) class EvalQueueColumn: # Queue column model = ColumnContent("model", "markdown", True) private = ColumnContent("private", "bool", False) status = ColumnContent("status", "str", True) @dataclass(frozen=True) class FailedEvalQueueColumn: # Queue column model = ColumnContent("model", "markdown", True) private = ColumnContent("private", "bool", False) status = ColumnContent("status", "str", True) reason = ColumnContent("reason", "str", True) ## All the model information that we might need @dataclass class ModelDetails: name: str display_name: str = "" symbol: str = "" # emoji # Column selection eval_col_instance = AutoEvalColumn() COLS = [c.name for c in fields(eval_col_instance) if not c.hidden] EVAL_COLS = [c.name for c in fields(EvalQueueColumn)] EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)] FAILED_EVAL_COLS = [c.name for c in fields(FailedEvalQueueColumn)] FAILED_EVAL_TYPES = [c.type for c in fields(FailedEvalQueueColumn)] BENCHMARK_COLS = [t.value.col_name for t in Tasks]