src/display/utils.py

from dataclasses import dataclass, make_dataclass
from enum import Enum

import pandas as pd

from src.about import Tasks, TasksMib_Subgraph, TasksMib_Causalgraph

def fields(raw_class):
    return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]


# These classes are for user facing column names,
# to avoid having to change them all around the code
# when a modif is needed
@dataclass
class ColumnContent:
    name: str
    type: str
    displayed_by_default: bool
    hidden: bool = False
    never_hidden: bool = False

## Leaderboard columns
auto_eval_column_dict = []
auto_eval_column_dict_multimodal = []

auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
auto_eval_column_dict.append(["hf_repo", ColumnContent, ColumnContent("HF Repo", "str", False)])
auto_eval_column_dict.append(["track", ColumnContent, ColumnContent("Track", "markdown", False)])
#Scores
# for task in Tasks:
#     auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
# Model information
auto_eval_column_dict.append(["text_average", ColumnContent, ColumnContent("Text Average", "number", True)])
auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])

auto_eval_column_dict_multimodal.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
auto_eval_column_dict_multimodal.append(["hf_repo", ColumnContent, ColumnContent("HF Repo", "str", False)])
auto_eval_column_dict_multimodal.append(["track", ColumnContent, ColumnContent("Track", "markdown", False)])
# for task in TasksMultimodal:
#     auto_eval_column_dict_multimodal.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
#     if task.value.col_name in ("ewok", "EWoK"):   # make sure this appears in the right order
#         auto_eval_column_dict_multimodal.append(["text_average", ColumnContent, ColumnContent("Text Average", "number", True)])
auto_eval_column_dict_multimodal.append(["vision_average", ColumnContent, ColumnContent("Vision Average", "number", True)])
auto_eval_column_dict_multimodal.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
auto_eval_column_dict_multimodal.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])



AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
AutoEvalColumnMultimodal = make_dataclass("AutoEvalColumnMultimodal", auto_eval_column_dict_multimodal, frozen=True)



##############################################################################################################
# Version 3
auto_eval_column_dict_mib_subgraph = []

# Method name column (always present)
auto_eval_column_dict_mib_subgraph.append(
    ["method", ColumnContent, ColumnContent("Method", "markdown", True, never_hidden=True)]
)

# Description support
auto_eval_column_dict_mib_subgraph.append(
    ["description", ColumnContent, ColumnContent("Description", "markdown", False)]
)

# Add columns for each task-model combination
for task in TasksMib_Subgraph:
    for model in task.value.models:
        field_name = f"{task.value.benchmark}_{model}"
        display_name = f"{task.value.benchmark}({model})"
        
        print(f"Creating column - Field name: {field_name}, Display name: {display_name}")
        
        column_content = ColumnContent(display_name, "number", True)
        print(f"Column content name property: {column_content.name}")
        
        auto_eval_column_dict_mib_subgraph.append([
            field_name,
            ColumnContent,
            column_content
        ])

# Add the Average column
auto_eval_column_dict_mib_subgraph.append(
    ["average", ColumnContent, ColumnContent("Average", "number", True)]
)

print("\nFinal column configurations:")
for field in auto_eval_column_dict_mib_subgraph:
    print(f"Field name: {field[0]}, Display name: {field[2].name}")


# Create the dataclass for MIB columns
AutoEvalColumn_mib_subgraph = make_dataclass("AutoEvalColumn_mib_subgraph", auto_eval_column_dict_mib_subgraph, frozen=True)

# Column selection for display
COLS_MIB_SUBGRAPH = [c.name for c in fields(AutoEvalColumn_mib_subgraph) if not c.hidden]


BENCHMARK_COLS_MIB_SUBGRAPH = []
for task in TasksMib_Subgraph:
    for model in task.value.models:
        col_name = f"{task.value.col_name}_{model.replace('-', '_')}"
        BENCHMARK_COLS_MIB_SUBGRAPH.append(col_name)

# Implement the same for causal graph, auto_eval_column_dict_mib_causalgraph, AutoEvalColumn_mib_causalgraph
AutoEvalColumn_mib_causalgraph = []
COLS_MIB_CAUSALGRAPH = []
BENCHMARK_COLS_MIB_CAUSALGRAPH = []


auto_eval_column_dict_mib_causalgraph = []

# Only include Method column as required
auto_eval_column_dict_mib_causalgraph.append(["method", ColumnContent, ColumnContent("Method", "markdown", True, never_hidden=True)])

auto_eval_column_dict_mib_causalgraph.append(["description", ColumnContent, ColumnContent("Description", "markdown", False)])

# For each model-task-intervention-counterfactual combination
for task in TasksMib_Causalgraph:
    for model in task.value.models:  # model will be lowercase
        col_name = f"{task.value.benchmark}_{model}"
        auto_eval_column_dict_mib_causalgraph.append([
            col_name, 
            ColumnContent, 
            ColumnContent(col_name, "number", True)
        ])

# Add the Average column
auto_eval_column_dict_mib_causalgraph.append(
    ["average_score", ColumnContent, ColumnContent("Average", "number", True)]
)

# Create the dataclass
AutoEvalColumn_mib_causalgraph = make_dataclass(
    "AutoEvalColumn_mib_causalgraph", 
    auto_eval_column_dict_mib_causalgraph,
    frozen=True
)


## For the queue columns in the submission tab
@dataclass(frozen=True)
class EvalQueueColumn:  # Queue column
    track_name = ColumnContent("track", "str", True)
    method_name = ColumnContent("method_name", "str", True)
    repo_id = ColumnContent("hf_repo", "markdown", True)
    revision = ColumnContent("revision", "str", True)
    status = ColumnContent("status", "str", True)

## All the model information that we might need
@dataclass
class ModelDetails:
    name: str
    display_name: str = ""
    symbol: str = "" # emoji

# Column selection

COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
COLS_MULTIMODAL = [c.name for c in fields(AutoEvalColumnMultimodal) if not c.hidden]

EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]

BENCHMARK_COLS = [t.value.col_name for t in Tasks]