src/submission/submit.py

import json
import os
from datetime import datetime, timezone

from src.display.formatting import styled_error, styled_message, styled_warning
from src.envs import API, EVAL_REQUESTS_PATH, TOKEN, QUEUE_REPO
from src.submission.check_validity import (
    already_submitted_models,
    check_model_card,
    get_model_size,
    is_model_on_hub,
    is_valid_predictions,
)

REQUESTED_MODELS = None
USERS_TO_SUBMISSION_DATES = None

def add_new_eval(
    model_name: str,
    model_id: str,
    revision: str,
    track: str,
    predictions: dict,
):
    global REQUESTED_MODELS
    global USERS_TO_SUBMISSION_DATES
    if not REQUESTED_MODELS:
        REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)

    out_message = ""
    print(predictions)  # debugging

    user_name = ""
    model_path = model_name
    if "/" in model_name:
        user_name = model_name.split("/")[0]
        model_path = model_name.split("/")[1]

    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
    
    if track is None:
        return styled_error("Please select a track.")

    # Does the model actually exist?
    if revision == "":
        revision = "main"
    
    out_message = ""

    # Is the model info correctly filled?
    try:
        model_info = API.model_info(repo_id=model_id, revision=revision)
    except Exception:
        out_message += styled_warning("Could not get your model information. The leaderboard entry will not have a link to its HF repo.") + "<br>"

    modelcard_OK, error_msg = check_model_card(model_name)
    if not modelcard_OK:
        out_message += styled_warning(error_msg) + "<br>"

    predictions_OK, error_msg = is_valid_predictions(predictions)
    if not predictions_OK:
        return styled_error(error_msg) + "<br>"

    # Seems good, creating the eval
    print("Adding new eval")

    eval_entry = {
        "model_name": model_name,
        "hf_repo": model_id,
        "revision": revision,
        "track": track,
        "predictions": predictions,
        "status": "PENDING",
        "submitted_time": current_time,
        "private": False,
    }

    # Check for duplicate submission
    if f"{model_name}_{revision}_{track}" in REQUESTED_MODELS:
        return styled_error("A model with this name has been already submitted.")

    print("Creating eval file")
    OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
    os.makedirs(OUT_DIR, exist_ok=True)
    out_path = f"{OUT_DIR}/{model_path}_{revision}_eval_request_False_{track}.json"

    with open(out_path, "w") as f:
        f.write(json.dumps(eval_entry))

    print("Uploading eval file")
    API.upload_file(
        path_or_fileobj=out_path,
        path_in_repo=out_path.split("eval-queue/")[1],
        repo_id=QUEUE_REPO,
        repo_type="dataset",
        commit_message=f"Add {model_name} to eval queue",
    )

    # Remove the local file
    os.remove(out_path)

    return styled_message(
        "Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the request to show in the PENDING list."
    )