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,
)

REQUESTED_MODELS = None
USERS_TO_SUBMISSION_DATES = None

OUT_DIR = f"{EVAL_REQUESTS_PATH}"
RESULTS_PATH = f"{OUT_DIR}/evaluation.json"

# def add_new_eval(
#     model: str,
#     base_model: str,
#     revision: str,
#     precision: str,
#     weight_type: str,
#     model_type: str,
# ):
#     global REQUESTED_MODELS
#     global USERS_TO_SUBMISSION_DATES
#     if not REQUESTED_MODELS:
#         REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)

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

#     precision = precision.split(" ")[0]
#     current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")

#     if model_type is None or model_type == "":
#         return styled_error("Please select a model type.")

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

#     # Is the model on the hub?
#     if weight_type in ["Delta", "Adapter"]:
#         base_model_on_hub, error, _ = is_model_on_hub(
#             model_name=base_model, revision=revision, token=TOKEN, test_tokenizer=True
#         )
#         if not base_model_on_hub:
#             return styled_error(f'Base model "{base_model}" {error}')

#     if not weight_type == "Adapter":
#         model_on_hub, error, _ = is_model_on_hub(model_name=model, revision=revision, token=TOKEN, test_tokenizer=True)
#         if not model_on_hub:
#             return styled_error(f'Model "{model}" {error}')

#     # Is the model info correctly filled?
#     try:
#         model_info = API.model_info(repo_id=model, revision=revision)
#     except Exception:
#         return styled_error("Could not get your model information. Please fill it up properly.")

#     model_size = get_model_size(model_info=model_info, precision=precision)

#     # Were the model card and license filled?
#     try:
#         license = model_info.cardData["license"]
#     except Exception:
#         return styled_error("Please select a license for your model")

#     modelcard_OK, error_msg = check_model_card(model)
#     if not modelcard_OK:
#         return styled_error(error_msg)

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

#     eval_entry = {
#         "model": model,
#         "base_model": base_model,
#         "revision": revision,
#         "precision": precision,
#         "weight_type": weight_type,
#         "status": "PENDING",
#         "submitted_time": current_time,
#         "model_type": model_type,
#         "likes": model_info.likes,
#         "params": model_size,
#         "license": license,
#         "private": False,
#     }

#     # Check for duplicate submission
#     if f"{model}_{revision}_{precision}" in REQUESTED_MODELS:
#         return styled_warning("This model 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}_eval_request_False_{precision}_{weight_type}.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} 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 model to show in the PENDING list."
#     )


def format_error(msg):
    return f"<p style='color: red; font-size: 20px; text-align: center;'>{msg}</p>"


def format_warning(msg):
    return f"<p style='color: orange; font-size: 20px; text-align: center;'>{msg}</p>"


def format_log(msg):
    return f"<p style='color: green; font-size: 20px; text-align: center;'>{msg}</p>"


def model_hyperlink(link, model_name):
    return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'


def input_verification(model, model_family, forget_rate, url, path_to_file, organisation, mail):
    for input in [model, model_family, forget_rate, url, organisation]:
        if input == "":
            return format_warning("Please fill all the fields.")

    # Very basic email parsing
    _, parsed_mail = parseaddr(mail)
    if not "@" in parsed_mail:
        return format_warning("Please provide a valid email adress.")

    if path_to_file is None:
        return format_warning("Please attach a file.")

    return parsed_mail


def add_new_eval(
    model: str,
    model_family: str,
    forget_rate: str,
    url: str,
    path_to_file: str,
    organisation: str,
    mail: str,
):

    parsed_mail = input_verification(model, model_family, forget_rate, url, path_to_file, organisation, mail)

    # load the file
    df = pd.read_csv(path_to_file)

    # modify the df to include metadata
    df["model"] = model
    df["model_family"] = model_family
    df["forget_rate"] = forget_rate
    df["url"] = url
    df["organisation"] = organisation
    df["mail"] = parsed_mail
    df["timestamp"] = datetime.datetime.now()

    # upload to spaces using the hf api at

    path_in_repo = f"versions/{model_family}-{forget_rate.replace('%', 'p')}"
    file_name = f"{model}-{organisation}-{datetime.datetime.now().strftime('%Y-%m-%d')}.csv"

    # upload the df to spaces
    import io

    buffer = io.BytesIO()
    df.to_csv(buffer, index=False)  # Write the DataFrame to a buffer in CSV format
    buffer.seek(0)  # Rewind the buffer to the beginning

    API.upload_file(
        repo_id=RESULTS_PATH,
        path_in_repo=f"{path_in_repo}/{file_name}",
        path_or_fileobj=buffer,
        token=TOKEN,
        repo_type="space",
    )

    return format_log(
        f"Model {model} submitted by {organisation} successfully. \nPlease refresh the leaderboard, and wait a bit to see the score displayed"
    )