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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.envs import API, EVAL_REQUESTS_PATH, QUEUE_REPO, TOKEN
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 f"⚠️ Invalid submission: {validity_message}"
# Parse library type
library_type = LibraryType.from_str(library_type_str)
if library_type == LibraryType.Unknown:
return "⚠️ 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
}
# Save the request
os.makedirs(EVAL_REQUESTS_PATH, exist_ok=True)
with open(os.path.join(EVAL_REQUESTS_PATH, request_filename), "w") as f:
json.dump(assessment_request, f, indent=2)
try:
# Push the file to the HF repo
path = Path(os.path.join(EVAL_REQUESTS_PATH, request_filename))
API.upload_file(
path_or_fileobj=path,
path_in_repo=request_filename,
repo_id=QUEUE_REPO,
repo_type="dataset",
)
return f"✅ Library '{library_name}' (version {library_version}) has been added to the assessment queue! Assessment ID: {uid}"
except Exception as e:
return f"Error uploading assessment request: {str(e)}"
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