import os
from huggingface_hub import HfApi

ASSAY_LIST = ["AC-SINS_pH7.4", "PR_CHO", "HIC", "Tm2", "Titer"]
ASSAY_RENAME = {
    "AC-SINS_pH7.4": "Self-association",
    "PR_CHO": "Polyreactivity",
    "HIC": "Hydrophobicity",
    "Tm2": "Thermostability",
    "Titer": "Titer",
}
ASSAY_EMOJIS = {
    "AC-SINS_pH7.4": "🧲",
    "PR_CHO": "🎯",
    "HIC": "💧",
    "Tm2": "🌡️",
    "Titer": "🧪",
}
TOKEN = os.environ.get("HF_TOKEN")
CACHE_PATH=os.getenv("HF_HOME", ".")
API = HfApi(token=TOKEN)
organization="ginkgo-datapoints"
submissions_repo = f'{organization}/abdev-bench-submissions'
results_repo = f'{organization}/abdev-bench-results'

ABOUT_TEXT = """
## About this challenge

We're inviting the ML/bio community to predict developability properties for 244 antibodies from the [GDPa1 dataset](https://huggingface.co/datasets/ginkgo-datapoints/GDPa1).

**What is antibody developability?**

Antibodies have to be manufacturable, stable in high concentrations, and have low off-target effects. 
Properties such as these can often hinder the progression of an antibody to the clinic, and are collectively referred to as 'developability'.
Here we show 5 of these properties and invite the community to submit and develop better predictors, which will be tested out on a heldout private set to assess model generalization.

**How to submit?**

TODO

**How to evaluate?**

TODO

FAQs: A list of frequently asked questions.
"""
FAQS = {
    "Example FAQ with dropdown": """Full answer to this question""",
}