from dataclasses import dataclass
from enum import Enum

@dataclass
class Task:
    phenotype: str
    metric: str


# Select your tasks here
# ---------------------------------------------------
class Tasks(Enum):
    task0 = Task("Asthma", "auroc")
    task1 = Task("Cataract", "auroc")
    task2 = Task("Diabetes", "auroc")
    task3 = Task("GERD", "auroc")
    task4 = Task("Hay-fever & Eczema", "auroc")
    task5 = Task("Major depression", "auroc")
    task6 = Task("Myocardial infarction", "auroc")
    task7 = Task("Osteoarthritis", "auroc")
    task8 = Task("Pneumonia", "auroc")
    task9 = Task("Stroke", "auroc")
    task10 = Task("Asthma", "auprc")
    task11 = Task("Cataract", "auprc")
    task12 = Task("Diabetes", "auprc")
    task13 = Task("GERD", "auprc")
    task14 = Task("Hay-fever & Eczema", "auprc")
    task15 = Task("Major depression", "auprc")
    task16 = Task("Myocardial infarction", "auprc")
    task17 = Task("Osteoarthritis", "auprc")
    task18 = Task("Pneumonia", "auprc")
    task19 = Task("Stroke", "auprc")
# ---------------------------------------------------


# Your leaderboard name
TITLE = """<h1 align="center" id="space-title">LLMs Disease Risk Prediction Leaderboard</h1>"""

# What does your leaderboard evaluate?
# INTRODUCTION_TEXT = """
# TODO:

#     - Add a description of the leaderboard    
#     - Add class distribution for each phenotype
#     - Potentially a warning when we should not rely on AUROC
#     - Plot of AUROC and AUPRC for each phenotype
#     - Edit about section
#     - Edit submit section (AutoModelForCausalLM)
# """
INTRODUCTION_TEXT = """
"""

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

## Reproducibility
To reproduce our results, here is the commands you can run:

"""

EVALUATION_QUEUE_TEXT = """
## Some good practices before submitting a model

### 1) Make sure you can load your model and tokenizer using AutoClasses:
```python
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
config = AutoConfig.from_pretrained("your model name", revision=revision)
model = AutoModelForCausalLM.from_pretrained("your model name", revision=revision)
tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
```
If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded.

Note: make sure your model is public!
Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted!

### 2) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index)
It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`!

### 3) Make sure your model has an open license!
We'd love for as many people as possible to know they can use your model 🤗

### 4) Fill up your model card
When we add extra information about models to the leaderboard, it will be automatically taken from the model card

## In case of model failure
If your model is displayed in the `FAILED` category, its execution stopped.
Make sure you have followed the above steps first.
If everything is done, feel free to open a new discussion on the Hugging Face space.
"""

CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""
@misc{
    TemryL/LLM-Disease-Risk-Leaderboard,
    author = {Tom Mery, Chirag Patel},
    title = {TemryL/LLM-Disease-Risk-Leaderboard},
    year = {2024},
    publisher = {Hugging Face},
    howpublished = "\url{https://huggingface.co/spaces/TemryL/LLM-Disease-Risk-Leaderboard}"
}
"""