from dataclasses import dataclass
from enum import Enum

@dataclass
class Task:
    benchmark: str
    metric: str
    col_name: str


# Init: to update with your specific keys
class Tasks(Enum):
    # task_key in the json file, metric_key in the json file, name to display in the leaderboard 
    task0 = Task("task_name1", "metric_name", "First task")
    task1 = Task("task_name2", "metric_name", "Second task")


# Your leaderboard name
TITLE = """<h1 align="center" id="space-title">🏆 Auto Arena of LLMs</h1>"""

# subtitle
SUB_TITLE = """<h2 align="center" id="space-title">Automating LLM Evaluations with Agent Peer-battles and Committee Discussions</h1>"""

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """
This leaderboard is from a completely automated large language model (LLM) evaluation framework by employing various LLM agents in peer-battles and committee discussions.
You can find more details from the [project page](https://auto-arena.github.io/) and our [paper]().
"""

# For additional details such as datasets, evaluation criteria, and reproducibility, please refer to the "📝 About" tab.

# Stay tuned for the *SeaBench leaderboard* - focusing on evaluating the model's ability to respond to general human instructions in real-world multi-turn settings.
# """

# Which evaluations are you running? how can people reproduce what you have?
LLM_BENCHMARKS_TEXT = f"""

```

"""

# You can find the detailed numerical results in the results Hugging Face dataset: https://huggingface.co/datasets/SeaLLMs/SeaExam-results

EVALUATION_QUEUE_TEXT = """
"""

CITATION_BUTTON_LABEL = ""
CITATION_BUTTON_TEXT = r"""
"""

CONTACT_TEXT = f"""
## Contact
"""