# src/display/utils.py

from dataclasses import dataclass
from enum import Enum
from typing import Any, List

from src.about import Tasks

@dataclass
class ColumnContent:
    name: str
    type: Any
    label: str
    description: str
    hidden: bool = False
    displayed_by_default: bool = True  # All columns displayed by default
    never_hidden: bool = False

# Initialize the list of columns for the leaderboard
COLUMNS: List[ColumnContent] = []

# Essential columns
COLUMNS.append(
    ColumnContent(
        name="model",
        type=str,
        label="Model",
        description="Model name",
        never_hidden=True,
    )
)
COLUMNS.append(
    ColumnContent(
        name="average",
        type=float,
        label="Average Accuracy (%)",
        description="Average accuracy across all subjects",
    )
)

# Include per-subject accuracy columns based on your subjects
for task in Tasks:
    COLUMNS.append(
        ColumnContent(
            name=task.value.benchmark,
            type=float,
            label=f"{task.value.col_name} (%)",
            description=f"Accuracy on {task.value.col_name}",
            displayed_by_default=True,
        )
    )

# Additional columns
COLUMNS.extend([
    ColumnContent(
        name="model_type",
        type=str,
        label="Model Type",
        description="Type of the model (e.g., Transformer, RNN, etc.)",
        displayed_by_default=True,
    ),
    ColumnContent(
        name="weight_type",
        type=str,
        label="Weight Type",
        description="Type of model weights (e.g., Original, Delta, Adapter)",
        displayed_by_default=True,
    ),
    ColumnContent(
        name="precision",
        type=str,
        label="Precision",
        description="Precision of the model weights (e.g., float16)",
        displayed_by_default=True,
    ),
    ColumnContent(
        name="license",
        type=str,
        label="License",
        description="License of the model",
        displayed_by_default=True,
    ),
    ColumnContent(
        name="likes",
        type=int,
        label="Likes",
        description="Number of likes on the Hugging Face Hub",
        displayed_by_default=True,
    ),
    ColumnContent(
        name="still_on_hub",
        type=bool,
        label="Available on the Hub",
        description="Whether the model is still available on the Hugging Face Hub",
        displayed_by_default=True,
    ),
])

# Now we can create lists of column names for use in the application
COLS = [col.name for col in COLUMNS]
BENCHMARK_COLS = [col.name for col in COLUMNS if col.name not in [
    "model", "average", "model_type", "weight_type", "precision", "license", "likes", "still_on_hub"
]]