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 import traceback from huggingface_hub import HfApi BOT_HF_TOKEN = os.getenv('BOT_HF_TOKEN') data = get_json_format_data() finished_models = get_datas(data) df = pd.DataFrame(finished_models) source_name = "Open Portuguese LLM Leaderboard" default_pull_request_title = "Adding the Open Portuguese LLM Leaderboard Evaluation Results" desc = """ This is an automated PR created with https://huggingface.co/spaces/eduagarcia-temp/portuguese-leaderboard-results-to-modelcard The purpose of this PR is to add evaluation results from the Open Portuguese LLM Leaderboard to your model card. If you encounter any issues, please report them to https://huggingface.co/spaces/eduagarcia-temp/portuguese-leaderboard-results-to-modelcard/discussions """ def search(df, value): result_df = df[df["Model Name"] == 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/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/{repo}" def get_query_url(repo): return f"https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query={repo}" def get_task_summary(results): return { "ENEM": {"dataset_type":"eduagarcia/enem_challenge", "dataset_name":"ENEM Challenge (No Images)", "metric_type":"acc", "metric_value":results["ENEM"], "dataset_config": None, "dataset_split":"train", "dataset_revision":None, "dataset_args":{"num_few_shot": 3}, "metric_name":"accuracy" }, "BLUEX": {"dataset_type":"eduagarcia-temp/BLUEX_without_images", "dataset_name":"BLUEX (No Images)", "metric_type":"acc", "metric_value":results["BLUEX"], "dataset_config": None, "dataset_split":"train", "dataset_revision":None, "dataset_args":{"num_few_shot": 3}, "metric_name":"accuracy" }, "OAB Exams": {"dataset_type":"eduagarcia/oab_exams", "dataset_name":"OAB Exams", "metric_type":"acc", "metric_value":results["OAB Exams"], "dataset_config": None, "dataset_split":"train", "dataset_revision":None, "dataset_args":{"num_few_shot": 3}, "metric_name":"accuracy" }, "ASSIN2 RTE": {"dataset_type":"assin2", "dataset_name":"Assin2 RTE", "metric_type":"f1_macro", "metric_value":results["ASSIN2 RTE"], "dataset_config": None, "dataset_split":"test", "dataset_revision":None, "dataset_args":{"num_few_shot": 15}, "metric_name":"f1-macro" }, "ASSIN2 STS": {"dataset_type":"eduagarcia/portuguese_benchmark", "dataset_name":"Assin2 STS", "metric_type":"pearson", "metric_value":results["ASSIN2 STS"], "dataset_config": None, "dataset_split":"test", "dataset_revision":None, "dataset_args":{"num_few_shot": 15}, "metric_name":"pearson" }, "FAQUAD NLI": {"dataset_type":"ruanchaves/faquad-nli", "dataset_name":"FaQuAD NLI", "metric_type":"f1_macro", "metric_value":results["FAQUAD NLI"], "dataset_config": None, "dataset_split":"test", "dataset_revision":None, "dataset_args":{"num_few_shot": 15}, "metric_name":"f1-macro" }, "HateBR": {"dataset_type":"ruanchaves/hatebr", "dataset_name":"HateBR Binary", "metric_type":"f1_macro", "metric_value":results["HateBR"], "dataset_config": None, "dataset_split":"test", "dataset_revision":None, "dataset_args":{"num_few_shot": 25}, "metric_name":"f1-macro" }, "PT Hate Speech": {"dataset_type":"hate_speech_portuguese", "dataset_name":"PT Hate Speech Binary", "metric_type":"f1_macro", "metric_value":results["PT Hate Speech"], "dataset_config": None, "dataset_split":"test", "dataset_revision":None, "dataset_args":{"num_few_shot": 25}, "metric_name":"f1-macro" }, "tweetSentBR": {"dataset_type":"eduagarcia/tweetsentbr_fewshot", "dataset_name":"tweetSentBR", "metric_type":"f1_macro", "metric_value":results["tweetSentBR"], "dataset_config": None, "dataset_split":"test", "dataset_revision":None, "dataset_args":{"num_few_shot": 25}, "metric_name":"f1-macro" } } 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 = [["Average", f"**{results['Average ⬆️']}**"]] + [[v["dataset_name"], v["metric_value"]] for v in task_summary.values()] text = f""" # Open Portuguese LLM Leaderboard Evaluation Results Detailed results can be found [here]({get_details_url(repo)}) and on the [🚀 Open Portuguese LLM Leaderboard](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard) {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": source_name, "source_url": get_query_url(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 pr_already_exists(repo, token: str | None = None): card = ModelCard.load(repo, token=token) if 'eval_results' in card.data and card.data['eval_results']: for x in card.data['eval_results']: if x.source_name == source_name: return True if 'Open Portuguese LLM Leaderboard' in card.content: return True if 'Open PT LLM Leaderboard' in card.content: return True api = HfApi(token=token) for x in api.get_repo_discussions(repo): if x.title == default_pull_request_title: return True if x.author == "leaderboard-pt-pr-bot": return True if x.author == "eduagarcia" and x.is_pull_request: return True return False def commit(repo, pr_number=None, message=default_pull_request_title, oauth_token: gr.OAuthToken | None = None, check_if_pr_exists=False): # specify pr number if you want to edit it, don't if you don't want if oauth_token is None: gr.Warning("You are not logged in; therefore, the leaderboard-pr-bot will open the pull request instead of you. Click on 'Sign in with Huggingface' to log in.") token = BOT_HF_TOKEN elif oauth_token.expires_at < time.time(): raise gr.Error("Token expired. Logout and try again.") else: token = oauth_token.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)}") if check_if_pr_exists or token == BOT_HF_TOKEN: if pr_already_exists(repo, token): return "PR already exists, Login to make a duplicate PR" 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) + '\n' + 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: traceback.print_exc() 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 if __name__ == "__main__": print(get_eval_results("Qwen/Qwen1.5-72B-Chat"))