functions.py

import os
from huggingface_hub import CommitOperationAdd, create_commit, RepoUrl
from huggingface_hub import EvalResult, ModelCard
from huggingface_hub.repocard_data import eval_results_to_model_index
import time
from pytablewriter import MarkdownTableWriter
import gradio as gr
from openllm import get_json_format_data, get_datas
import pandas as pd

BOT_HF_TOKEN = os.getenv('BOT_HF_TOKEN')

data = get_json_format_data()
finished_models = get_datas(data)
df = pd.DataFrame(finished_models)

desc = """
This is an automated PR created with https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr

The purpose of this PR is to add evaluation results from the Open LLM Leaderboard to your model card.

If you encounter any issues, please report them to https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr/discussions
"""

def search(df, value):
    result_df = df[df["Model"] == value]
    return result_df.iloc[0].to_dict() if not result_df.empty else None


def get_details_url(repo):
   author, model = repo.split("/")
   return f"https://huggingface.co/datasets/open-llm-leaderboard/details_{author}__{model}"


def get_query_url(repo):
  return f"https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query={repo}"


def get_task_summary(results):
  return {
      "IFEval":
          {"dataset_type":"HuggingFaceH4/ifeval",
          "dataset_name":"IFEval (0-Shot)",
          "metric_type": "inst_level_strict_acc and prompt_level_strict_acc",
          "metric_value":results["IFEval"],
          "dataset_config": None, # don't know
          "dataset_split": None, # don't know
          "dataset_revision":None,
          "dataset_args":{"num_few_shot": 0},
          "metric_name":"strict accuracy"
          },
      "BBH":
          {"dataset_type":"BBH",
          "dataset_name":"BBH (3-Shot)",
          "metric_type":"acc_norm",
          "metric_value":results["BBH"],
          "dataset_config": None, # don't know
          "dataset_split": None, # don't know
          "dataset_revision":None,
          "dataset_args":{"num_few_shot": 3},
          "metric_name":"normalized accuracy"
          },
      "MATH Lvl 5":
      {
          "dataset_type":"hendrycks/competition_math",
          "dataset_name":"MATH Lvl 5 (4-Shot)",
          "metric_type":"exact_match",
          "metric_value":results["MATH Lvl 5"],
          "dataset_config": None, # don't know
          "dataset_split": None, # don't know
          "dataset_revision":None,
          "dataset_args":{"num_few_shot": 4},
          "metric_name":"exact match"
      },
      "GPQA":
      {
          "dataset_type":"Idavidrein/gpqa",
          "dataset_name":"GPQA (0-shot)",
          "metric_type":"acc_norm",
          "metric_value":results["GPQA"],
          "dataset_config": None, # don't know
          "dataset_split": None, # don't know
          "dataset_revision":None,
          "dataset_args":{"num_few_shot": 0},
          "metric_name":"acc_norm"
      },
      "MuSR":
      {
          "dataset_type":"TAUR-Lab/MuSR",
          "dataset_name":"MuSR (0-shot)",
          "metric_type":"acc_norm",
          "metric_value":results["MUSR"],
          "dataset_config": None, # don't know
          "dataset_split": None, # don't know
          "dataset_args":{"num_few_shot": 0},
          "metric_name":"acc_norm"
      },
      "MMLU-PRO":
      {
          "dataset_type":"TIGER-Lab/MMLU-Pro",
          "dataset_name":"MMLU-PRO (5-shot)",
          "metric_type":"acc",
          "metric_value":results["MMLU-PRO"],
          "dataset_config":"main",
          "dataset_split":"test",
          "dataset_args":{"num_few_shot": 5},
          "metric_name":"accuracy"
      }
  }



def get_eval_results(repo):
  results = search(df, repo)
  task_summary = get_task_summary(results)
  md_writer = MarkdownTableWriter()
  md_writer.headers = ["Metric", "Value"]
  md_writer.value_matrix = [["Avg.", results['Average ⬆️']]] + [[v["dataset_name"], v["metric_value"]] for v in task_summary.values()]

  text = f"""
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here]({get_details_url(repo)})

{md_writer.dumps()}
"""
  return text


def get_edited_yaml_readme(repo, token: str | None):
  card = ModelCard.load(repo, token=token)
  results = search(df, repo)

  common = {"task_type": 'text-generation', "task_name": 'Text Generation', "source_name": "Open LLM Leaderboard", "source_url": f"https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query={repo}"}

  tasks_results = get_task_summary(results)

  if not card.data['eval_results']: # No results reported yet, we initialize the metadata
    card.data["model-index"] = eval_results_to_model_index(repo.split('/')[1], [EvalResult(**task, **common) for task in tasks_results.values()])
  else: # We add the new evaluations
    for task in tasks_results.values():
      cur_result = EvalResult(**task, **common)
      if any(result.is_equal_except_value(cur_result) for result in card.data['eval_results']):
        continue
      card.data['eval_results'].append(cur_result)

  return str(card)
    

def commit(repo, pr_number=None, message="Adding Evaluation Results", oauth_token: gr.OAuthToken | None = None): # specify pr number if you want to edit it, don't if you don't want
  global df
  data = get_json_format_data()
  finished_models = get_datas(data)
  df = pd.DataFrame(finished_models)
    
  if not oauth_token:
    raise gr.Warning("You are not logged in. Click on 'Sign in with Huggingface' to log in.")
  else:
    token = oauth_token

  if repo.startswith("https://huggingface.co/"):
      try:
        repo = RepoUrl(repo).repo_id
      except Exception:
        raise gr.Error(f"Not a valid repo id: {str(repo)}")
    
  edited = {"revision": f"refs/pr/{pr_number}"} if pr_number else {"create_pr": True}

  try:
    try: # check if there is a readme already
      readme_text = get_edited_yaml_readme(repo, token=token) + get_eval_results(repo)
    except Exception as e:
      if "Repo card metadata block was not found." in str(e): # There is no readme
        readme_text = get_edited_yaml_readme(repo, token=token)
      else:
        print(f"Something went wrong: {e}")

    liste = [CommitOperationAdd(path_in_repo="README.md", path_or_fileobj=readme_text.encode())]
    commit = (create_commit(repo_id=repo, token=token, operations=liste, commit_message=message, commit_description=desc, repo_type="model", **edited).pr_url)

    return commit

  except Exception as e:

    if "Discussions are disabled for this repo" in str(e):
      return "Discussions disabled"
    elif "Cannot access gated repo" in str(e):
      return "Gated repo"
    elif "Repository Not Found" in str(e):
      return "Repository Not Found"
    else:
      return e