from enum import Enum
from dataclasses import dataclass

# Define TaskInfo and Tasks as before
@dataclass
class TaskInfo:
    benchmark: str
    col_name: str
    metric: str

# Replace these with actual subjects from your dataset
class Tasks(Enum):
    History = TaskInfo(benchmark='History', col_name='History', metric='accuracy')
    Mathematics = TaskInfo(benchmark='Mathematics', col_name='Mathematics', metric='accuracy')
    Science = TaskInfo(benchmark='Science', col_name='Science', metric='accuracy')
    Geography = TaskInfo(benchmark='Geography', col_name='Geography', metric='accuracy')
    Literature = TaskInfo(benchmark='Literature', col_name='Literature', metric='accuracy')
    Art = TaskInfo(benchmark='Art', col_name='Art', metric='accuracy')
    Physics = TaskInfo(benchmark='Physics', col_name='Physics', metric='accuracy')
    Chemistry = TaskInfo(benchmark='Chemistry', col_name='Chemistry', metric='accuracy')
    Biology = TaskInfo(benchmark='Biology', col_name='Biology', metric='accuracy')
    ComputerScience = TaskInfo(benchmark='Computer Science', col_name='Computer Science', metric='accuracy')

# Now include the variables expected by app.py

TITLE = """
<h1 align="center">🌐 LLM Leaderboard for MMMLU Evaluation 🌐</h1>
"""

INTRODUCTION_TEXT = """
Welcome to the LLM Leaderboard for the MMMLU dataset evaluation. This leaderboard displays the performance of various language models on the MMMLU dataset across different subjects.
"""

LLM_BENCHMARKS_TEXT = """
## About the MMMLU Benchmark

The Massive Multitask Multilingual Language Understanding (MMMLU) benchmark is designed to evaluate models on a wide range of subjects.

## How to Interpret the Leaderboard

- **Model**: The name of the model evaluated.
- **Average ⬆️**: The average accuracy across all subjects.
- **Subject Columns**: The accuracy (%) for each individual subject.

## How to Submit Your Model

Go to the **Submit here!** tab and provide your model details to have it evaluated and appear on the leaderboard.
"""

EVALUATION_QUEUE_TEXT = """
Below are the lists of models that have been evaluated, are currently being evaluated, or are pending evaluation.
"""

CITATION_BUTTON_LABEL = "Citation"
CITATION_BUTTON_TEXT = """
If you use this leaderboard or the MMMLU dataset in your research, please cite:
@article{your_citation_here, title={Your Title}, author={Your Name}, journal={Your Journal}, year={2024} }
"""