from dataclasses import dataclass
from enum import Enum

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


# Select your tasks here
# ---------------------------------------------------
class Tasks(Enum):
    # task_key in the json file, metric_key in the json file, name to display in the leaderboard 
    task0 = Task("anli_r1", "acc", "ANLI")
    task1 = Task("logiqa", "acc_norm", "LogiQA")

NUM_FEWSHOT = 0 # Change with your few shot
# ---------------------------------------------------



# Your leaderboard name
TITLE = """<h1 align="center" id="space-title">PROBE</h1>"""

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """
# (Protein RepresentatiOn BEnchmark): Function-Centric Evaluation of Protein Representation Method
"""

# Which evaluations are you running? how can people reproduce what you have?
LLM_BENCHMARKS_TEXT = f"""
# PROBE (Protein RepresentatiOn BEnchmark): Function-Centric Evaluation of Protein Representation Methods

- PROBE runs benchmark analyses on protein representation/feature vectors of any representation learning method in order to evaluate its predictive performance on protein function related predictive tasks, and to and compare it other methods from literature.

- Aiming to evaluate how much each representation model captures different facets of functional information, we constructed and applied 4 independent benchmark tests based on;
  - inferring semantic similarities between proteins,
  - predicting ontology-based protein functions, 
  - classifying drug target proteins according to their families, and
  - estimating protein-protein binding affinities.

- PROBE is part of the the study entitled [Learning functional properties of proteins with language models](https://rdcu.be/cJAKN) which is schematically summarized in the figure below:<br/>

![Summary of The Study](https://raw.githubusercontent.com/serbulent/TrainableRepresentationAnalysis/refs/heads/master/evalprotrep_summary_figure.jpg)

If you find PROBE useful please consider citing:

Unsal, S., Atas, H., Albayrak, M., Turhan, K., Acar, A. C., & Doğan, T. (2022). Learning functional properties of proteins with language models. *Nature Machine Intelligence, 4*(3), 227-245.
"""

EVALUATION_QUEUE_TEXT = """
# **Benchmarking your own representation model**
## To run the benchmarks, the following representation vectors need to be generated:
    - For benchmarks 1, 2, and 3 (similarity, function, and family), you will need to generate representation vectors for all human proteins. The amino acid sequences for canonical isoforms of human proteins can be found [here](https://drive.google.com/file/d/1wXF2lmj4ZTahMrl66QpYM2TvHmbcIL6b/view?usp=sharing).
    - For benchmark 4 (affinity), representation vectors will need to be generated for the samples in the SKEMPI dataset, which can be accessed [here](https://drive.google.com/file/d/1m5jssC0RMsiFT_w-Ykh629Pw_An3PInI/view?usp=sharing).

## Format of the both protein representation files:
    1. Each row corresponds to the representation vector of a particular protein.
    2. Rows & columns: first column's header one should be "Entry", and the rest of the column headers should contain the index number that correspond to dimensions of the vector. After the column header, the rows of the first column should contain the UniProt protein accessions of respective proteins (i.e., each row in this file corresponds to a different protein), rows of other columns should contain representation vector values for the corresponding proteins (i.e. each column in this file corresponds to a dimension of representation vectors).
    3. All representation vectors in a file should have the same size (i.e., fixed sized vectors).
    4. Representation vectors of the whole dataset should be saved in a comma separated (csv) text file.
"""

CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""
"""

TASK_INFO = ["similarity", "function", "family", "affinity"]

CSV_RESULT_PATH = "./src/data/leaderboard_result.csv"

LEADERBOARD_INTRODUCTION = """
<h1 align="center">PROBE Leaderboard: Protein Representation Model Evaluation</h1>

Welcome to the PROBE (Protein RepresentatiOn BEnchmark) leaderboard! This platform evaluates protein representation models based on their ability to capture functional properties of proteins through four key benchmarks:

- **Protein Similarity**: Inferring semantic similarities.
- **Protein Function**: Predicting ontology-based functions.
- **Protein Family**: Classifying drug target families.
- **Protein Affinity**: Estimating binding affinities.

Submit your own representation models and compare their performance across these tasks. For more details on how to participate, see the submission guidelines.

If you find PROBE useful, please consider citing our work."""

similarity_tasks_options = ["Sparse", "200", "500"]
function_prediction_aspect_options = ["MF", "BP", "CC", "All_Aspects"]
function_prediction_dataset_options = ["High", "Middle", "Low", "All_Data_Sets"]
family_prediction_dataset_options = ["nc", "uc50", "uc30", "mm15"]