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| import json | |
| import os | |
| import re | |
| import time | |
| import uuid | |
| from datetime import datetime | |
| from pathlib import Path | |
| import huggingface_hub | |
| import requests | |
| from huggingface_hub import HfApi | |
| from src.display.utils import LibraryType, Language, AssessmentStatus | |
| from src.display.formatting import styled_error, styled_warning, styled_message | |
| from src.envs import API, EVAL_REQUESTS_PATH, QUEUE_REPO, TOKEN, LOCAL_MODE | |
| from src.submission.check_validity import is_repository_valid, get_library_info | |
| def add_new_eval( | |
| library_name, | |
| library_version, | |
| repository_url, | |
| language, | |
| framework, | |
| library_type_str, | |
| ) -> str: | |
| """ | |
| Adds a new library to the assessment queue. | |
| Args: | |
| library_name: Name of the library (org/repo format) | |
| library_version: Version of the library | |
| repository_url: URL to the repository | |
| language: Programming language | |
| framework: Related framework/ecosystem | |
| library_type_str: Type of AI library | |
| Returns: | |
| A message indicating the status of the submission | |
| """ | |
| # Check if valid repository | |
| is_valid, validity_message, library_info = is_repository_valid(library_name, repository_url) | |
| if not is_valid: | |
| return styled_error(f"Invalid submission: {validity_message}") | |
| # Parse library type | |
| library_type = LibraryType.from_str(library_type_str) | |
| if library_type == LibraryType.Unknown: | |
| return styled_error("Please select a valid library type.") | |
| # Create a unique identifier for the submission | |
| uid = uuid.uuid4().hex[:6] | |
| timestamp = datetime.now().isoformat() | |
| request_filename = f"{library_name.replace('/', '_')}_eval_request_{timestamp}_{uid}.json" | |
| # Stars count and license info from library_info if available | |
| stars = library_info.get("stars", 0) | |
| license_name = library_info.get("license", "unknown") | |
| # Create the assessment request JSON | |
| assessment_request = { | |
| "library": library_name, | |
| "version": library_version, | |
| "repository_url": repository_url, | |
| "language": language, | |
| "framework": framework, | |
| "library_type": library_type.value.name, | |
| "license": license_name, | |
| "stars": stars, | |
| "status": "PENDING", | |
| "submitted_time": timestamp, | |
| "last_updated": timestamp, | |
| "assessment_id": uid | |
| } | |
| # Ensure directory exists | |
| os.makedirs(EVAL_REQUESTS_PATH, exist_ok=True) | |
| # Save the request locally | |
| request_file_path = os.path.join(EVAL_REQUESTS_PATH, request_filename) | |
| with open(request_file_path, "w") as f: | |
| json.dump(assessment_request, f, indent=2) | |
| # If in local mode, don't try to upload to HF | |
| if LOCAL_MODE: | |
| return styled_message(f"Library '{library_name}' (version {library_version}) has been added to the local assessment queue! Assessment ID: {uid}") | |
| # Try to upload to HF if not in local mode | |
| try: | |
| # Push the file to the HF repo | |
| path = Path(request_file_path) | |
| API.upload_file( | |
| path_or_fileobj=path, | |
| path_in_repo=request_filename, | |
| repo_id=QUEUE_REPO, | |
| repo_type="dataset", | |
| ) | |
| return styled_message(f"Library '{library_name}' (version {library_version}) has been added to the assessment queue! Assessment ID: {uid}") | |
| except Exception as e: | |
| return styled_warning(f"Saved locally but failed to upload to Hugging Face: {str(e)}") | |