import os import json import datetime from email.utils import parseaddr from io import BytesIO from huggingface_hub import HfApi import gradio as gr from eval_utils import get_evaluation_scores # Constants LEADERBOARD_PATH = "Exploration-Lab/IL-TUR-Leaderboard" SUBMISSION_FORMAT = "predictions" TOKEN = os.environ.get("TOKEN", None) YEAR_VERSION = "2024" api = HfApi(token=TOKEN) # Helper functions for formatting messages def format_message(msg, color): return f"
{msg}
" def format_error(msg): return format_message(msg, "red") def format_warning(msg): return format_message(msg, "orange") def format_log(msg): return format_message(msg, "green") def model_hyperlink(link, model_name): return f'{model_name}' def input_verification(method_name, url, path_to_file, organisation, mail): """Verify the input fields for submission.""" # Check if any field is empty if any( input == "" for input in [method_name, url, path_to_file, organisation, mail] ): return format_warning("Please fill all the fields.") # Check if file is attached if path_to_file is None: return format_warning("Please attach a file.") return def add_new_eval( method_name: str, submitted_by: str, url: str, path_to_file: str, organisation: str, mail: str, ): """Add a new evaluation to the leaderboard.""" # Verify input # Check if any field is empty if any( input == "" for input in [method_name, url, path_to_file, organisation, mail] ): return format_warning("Please fill all the fields.") # Check if file is attached if path_to_file is None: return format_warning("Please attach a file.") # Verify email format _, parsed_mail = parseaddr(mail) if "@" not in parsed_mail: print(parseaddr(mail)) return format_warning("Please provide a valid email address.") # Process submission if SUBMISSION_FORMAT == "predictions": # Read submission and gold data with open(path_to_file, "r") as f: submission_data = json.load(f) with open("submissions/baseline/IL_TUR_eval_gold.json", "r") as f: gold_data = json.load(f) # Get evaluation scores submission = get_evaluation_scores(gold_data, submission_data) else: # Read submission directly if it's not in predictions format with open(path_to_file, "r") as f: submission = json.load(f) # Update results with open("submissions/baseline/results.json", "r") as f: results = json.load(f) results.append(submission[0]) # Prepare buffer for upload leaderboard_buffer = BytesIO(json.dumps(results).encode()) leaderboard_buffer.seek(0) # Upload to Hugging Face api.upload_file( repo_id=LEADERBOARD_PATH, path_in_repo="submissions/baseline/results.json", path_or_fileobj=leaderboard_buffer, token=TOKEN, repo_type="space", ) return format_log( f"Method {method_name} submitted by {organisation} successfully. \n" "Please refresh the leaderboard, and wait for the evaluation results." )