import json from tempfile import mktemp import argilla as rg from huggingface_hub import HfApi from defaults import REMOTE_CODE_PATHS, SEED_DATA_PATH hf_api = HfApi() with open("DATASET_README_BASE.md") as f: DATASET_README_BASE = f.read() def create_readme(domain_seed_data, project_name, domain): # create a readme for the project that shows the domain and project name readme = DATASET_README_BASE readme += f"# {project_name}\n\n## Domain: {domain}" perspectives = domain_seed_data.get("perspectives") topics = domain_seed_data.get("topics") examples = domain_seed_data.get("examples") if perspectives: readme += "\n\n## Perspectives\n\n" for p in perspectives: readme += f"- {p}\n" if topics: readme += "\n\n## Topics\n\n" for t in topics: readme += f"- {t}\n" if examples: readme += "\n\n## Examples\n\n" for example in examples: readme += f"### {example['question']}\n\n{example['answer']}\n\n" temp_file = mktemp() with open(temp_file, "w") as f: f.write(readme) return temp_file def setup_dataset_on_hub(repo_id, hub_token): # create an empty dataset repo on the hub hf_api.create_repo( repo_id=repo_id, token=hub_token, repo_type="dataset", exist_ok=True, ) def push_dataset_to_hub( domain_seed_data_path, project_name, domain, pipeline_path, hub_username, hub_token: str, ): repo_id = f"{hub_username}/{project_name}" setup_dataset_on_hub(repo_id=repo_id, hub_token=hub_token) # upload the seed data and readme to the hub hf_api.upload_file( path_or_fileobj=domain_seed_data_path, path_in_repo="seed_data.json", token=hub_token, repo_id=repo_id, repo_type="dataset", ) # upload the readme to the hub domain_seed_data = json.load(open(domain_seed_data_path)) hf_api.upload_file( path_or_fileobj=create_readme( domain_seed_data=domain_seed_data, project_name=project_name, domain=domain ), path_in_repo="README.md", token=hub_token, repo_id=repo_id, repo_type="dataset", ) def push_pipeline_to_hub( pipeline_path, hub_username, hub_token: str, project_name, ): repo_id = f"{hub_username}/{project_name}" # upload the pipeline to the hub hf_api.upload_file( path_or_fileobj=pipeline_path, path_in_repo="pipeline.yaml", token=hub_token, repo_id=repo_id, repo_type="dataset", ) for code_path in REMOTE_CODE_PATHS: hf_api.upload_file( path_or_fileobj=code_path, path_in_repo=code_path, token=hub_token, repo_id=repo_id, repo_type="dataset", ) print(f"Dataset uploaded to {repo_id}") def pull_seed_data_from_repo(repo_id, hub_token): # pull the dataset repo from the hub hf_api.hf_hub_download( repo_id=repo_id, token=hub_token, repo_type="dataset", filename=SEED_DATA_PATH ) return json.load(open(SEED_DATA_PATH)) def push_argilla_dataset_to_hub( name: str, repo_id: str, url: str, api_key: str, workspace: str = "admin" ): rg.init(api_url=url, api_key=api_key) feedback_dataset = rg.FeedbackDataset.from_argilla(name=name, workspace=workspace) local_dataset = feedback_dataset.pull() local_dataset.push_to_huggingface(repo_id=repo_id)