src/submission/submit.py

import time
from datetime import datetime, timezone, timedelta
import os
import requests

import pandas as pd
from datasets import Dataset, get_dataset_config_names
from datasets.exceptions import DatasetNotFoundError
from pandas.api.types import is_integer_dtype
import gradio as gr

from src.datamodel.data import F1Data
from src.display.formatting import styled_error, styled_message
from src.display.utils import ModelType
from src.envs import SUBMISSIONS_REPO, TOKEN
from src.logger import get_logger
from src.validation.validate import is_submission_file_valid, is_valid


logger = get_logger(__name__)

MIN_WAIT_TIME_PER_USER_HRS = 24


def add_new_solutions(
    lbdb: F1Data,
    username: str,
    user_id: str,
    system_name: str,
    org: str,
    sys_type: str,
    submission_path: str,
    is_warmup_dataset: bool,
    ensure_all_present: bool = False,
):
    # Users must wait MIN_WAIT_TIME_PER_USER_HRS hours between submissions.

    from huggingface_hub import HfApi

    api = HfApi()
    logger.info(f"Who am I: {api.whoami(token=TOKEN)}")

    try:
        submitted_ids = get_dataset_config_names(SUBMISSIONS_REPO, token=TOKEN)
    except (DatasetNotFoundError, FileNotFoundError):
        submitted_ids = []

    logger.info(f"Found submitted IDs: {submitted_ids}")

    user_last_submission_date = None
    for sid in submitted_ids:
        # Extract user ID (last part)
        past_user_id = sid.rsplit("_", 1)[-1]
        # Extract timestamp string (first two parts)
        ts_str = "_".join(sid.split("_", 2)[:2])

        logger.info(f"Comparing past user: {past_user_id} with current user ID: {user_id}")

        ts = datetime.strptime(ts_str, "%Y%m%d_%H%M%S").replace(tzinfo=timezone.utc)
        if past_user_id == user_id:
            if user_last_submission_date is None:
                user_last_submission_date = ts
            else:
                user_last_submission_date = max(user_last_submission_date, ts)

    if user_last_submission_date is not None:
        now = datetime.now(timezone.utc)
        elapsed = now - user_last_submission_date
        if elapsed < timedelta(hours=MIN_WAIT_TIME_PER_USER_HRS):
            remaining_hrs = MIN_WAIT_TIME_PER_USER_HRS - elapsed.total_seconds() / 3600
            logger.info(f"{username} must wait {remaining_hrs:.2f} more hours.")
            return styled_error(
                f"You must wait {MIN_WAIT_TIME_PER_USER_HRS} hours between submissions. "
                f"Remaining wait time: {remaining_hrs:.2f} hours"
            )

    logger.info(
        f"Adding new submission: {system_name=}, {org=}, {sys_type=} and {submission_path=}",
    )

    # Double-checking.
    for val in [system_name, org, sys_type]:
        assert is_valid(val)
    assert is_submission_file_valid(submission_path, is_warmup_dataset=is_warmup_dataset)

    sys_type = ModelType.from_str(sys_type).name

    try:
        submission_df = pd.read_json(submission_path, lines=True)
        if ensure_all_present:
            _validate_all_submissions_present(lbdb=lbdb, pd_ds=submission_df)
    except Exception:
        logger.warning("Failed to parse submission DF!", exc_info=True)
        return styled_error(
            "An error occurred. Please try again later."
        )  # Use same message as external error. Avoid infoleak.

    submission_id = f"{datetime.now(timezone.utc).strftime('%Y%m%d_%H%M%S')}_{username}_{user_id}"

    # Seems good, creating the eval.
    logger.info(f"Adding new submission: {submission_id}")
    submission_ts = time.time_ns()

    def add_info(row):
        return {
            **row,
            "system_name": system_name,
            "organization": org,
            "system_type": sys_type,
            "submission_id": submission_id,
            "submission_ts": submission_ts,
            "evaluation_id": "",  # This will be set later when the evaluation is launched in the backend
            "evaluation_start_ts": "",  # This will be set when the evaluation starts
        }

    ds = Dataset.from_pandas(submission_df).map(add_info)
    ds.push_to_hub(
        SUBMISSIONS_REPO,
        submission_id,
        private=True,
    )

    return styled_message(
        "Your request has been submitted to the evaluation queue!\n"
        + "Results may take up to 24 hours to be processed and shown in the leaderboard."
    )


def fetch_sub_claim(oauth_token: gr.OAuthToken | None) -> dict | None:
    if oauth_token is None:
        return None
    provider = os.getenv("OPENID_PROVIDER_URL")
    if not provider:
        return None
    try:
        oidc_meta = requests.get(f"{provider}/.well-known/openid-configuration", timeout=5)
        oidc_meta = oidc_meta.json()
        userinfo_ep = oidc_meta["userinfo_endpoint"]
        claims = requests.get(userinfo_ep, headers={"Authorization": f"Bearer {oauth_token.token}"}, timeout=5)
        logger.info(f"userinfo_endpoint response: status={claims.status_code}\nheaders={dict(claims.headers)}")
        claims = claims.json()
        # Typical fields: sub (stable id), preferred_username, name, picture
        return {
            "sub": claims.get("sub"),
            "preferred_username": claims.get("preferred_username"),
            "name": claims.get("name"),
        }
    except Exception as e:
        logger.warning(f"Failed to fetch user claims: {e}")
        return None


def _validate_all_submissions_present(
    lbdb: F1Data,
    pd_ds: pd.DataFrame,
):
    logger.info(f"Validating DS size {len(pd_ds)} columns {pd_ds.columns} set {set(pd_ds.columns)}")
    expected_cols = ["problem_id", "solution"]

    if set(pd_ds.columns) != set(expected_cols):
        return ValueError(f"Expected attributes: {expected_cols}, Got: {pd_ds.columns.tolist()}")

    if not is_integer_dtype(pd_ds["problem_id"]):
        return ValueError("problem_id must be str convertible to int")

    if any(type(v) is not str for v in pd_ds["solution"]):
        return ValueError("solution must be of type str")

    submitted_ids = set(pd_ds.problem_id.astype(str))
    if submitted_ids != lbdb.code_problem_ids:
        missing = lbdb.code_problem_ids - submitted_ids
        unknown = submitted_ids - lbdb.code_problem_ids
        raise ValueError(f"Mismatched problem IDs: {len(missing)} missing, {len(unknown)} unknown")
    if len(pd_ds) > len(lbdb.code_problem_ids):
        return ValueError("Duplicate problem IDs exist in uploaded file")