src/submission/submit.py

import json
import os
from glob import glob
from datetime import datetime, timezone
import numpy as np
import pandas as pd
from src.display.formatting import styled_error, styled_message, styled_warning
from src.envs import API, EVAL_REQUESTS_PATH, TOKEN, QUEUE_REPO, RESULTS_REPO
from src.submission.check_validity import (
    already_submitted_models,
    check_model_card,
    get_model_size,
    is_model_on_hub,
)

from src.display.utils import (
    BENCHMARK_COLS,
    BENCHMARK_COL_IDS,
    COLS
)

REQUESTED_MODELS = None
USERS_TO_SUBMISSION_DATES = None


def add_new_eval(
    eval_name: str,
    upload: object,
    precision: str,
    hf_model_id: str,
    contact_email: str,
    captcha_ok: bool,
):
    try:
        if not eval_name:
            return styled_error("Please provide a model name."), captcha_ok
        
        if not precision:
            return styled_error("Please select precision."), captcha_ok
        
        if not contact_email:
            return styled_error("Please provide your contact email."), captcha_ok
        
        if not upload:
            return styled_error("Please upload a results file."), captcha_ok
        
        if not captcha_ok:
            return styled_error("Please prove you are a human!"), captcha_ok
        
        with open(upload, mode="r") as f:
            data = json.load(f)    
        
        results = data['results']

        acc_keys = ['exact_match,none', 'exact_match,flexible-extract', 'exact_match,strict-match']

        ret = {
            'eval_name': eval_name,
            'precision': precision,
            'hf_model_id': hf_model_id,
            'contact_email': contact_email
            }

        for k, v in results.items():
            for acc_k in acc_keys:
                if acc_k in v and k in BENCHMARK_COL_IDS:
                    ret[k] = v[acc_k]    
        #validation
        for k,v in ret.items():
            if k in ['eval_name', 'precision', 'hf_model_id', 'contact_email']:
                continue
            if k not in BENCHMARK_COL_IDS:
                print(f"Missing: {k}")
                return styled_error(f'Missing: {k}'), captcha_ok

        if len(BENCHMARK_COL_IDS) != len(ret) - 4:
            print(f"Missing columns")
            return styled_error(f'Missing result entries'), captcha_ok

        # TODO add complex validation
        #print(results.keys())
        #print(BENCHMARK_COLS)
        #for input_col in results.keys():
        #    if input_col not in BENCHMARK_COLS:
        #        print(input_col)
        #        return styled_error(f'Missing: {input_col}')
        #ret.update({i:j['acc,none'] for i,j in results.items()})
        # fake data for testing...
        #ret.update({i:round(np.random.normal(1, 0.5, 1)[0], 2) for i,j in results.items()})
        
        user_name = "czechbench_leaderboard"
        OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"

        existing_eval_names = []
        for fname in glob(f"{OUT_DIR}/*.json"):
            with open(fname, mode="r") as f:
                existing_eval = json.load(f)
            existing_eval_names.append(existing_eval['eval_name'])

        if ret['eval_name'] in existing_eval_names:
            print(f"Model name {ret['eval_name']} is used!")
            return styled_error(f"Model name {ret['eval_name']} is used!"), captcha_ok

        out_path = f"{OUT_DIR}/{eval_name}_eval_request.json"

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


        print("Uploading eval file")

        print("path_or_fileobj: ", out_path)
        print("path_in_repo: ",out_path.split("eval-queue/")[1])
        print("repo_id: ", RESULTS_REPO)
        print("repo_type: ", "dataset")

        response = API.upload_file(
            path_or_fileobj=out_path,
            path_in_repo=out_path.split("eval-queue/")[1],
            repo_id=RESULTS_REPO,
            repo_type="dataset",
            commit_message=f"Add {eval_name} to eval queue",
        )

        """
        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, 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,
        }

        # 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 results have been successfully submitted. They will be added to the leaderboard upon verification."
        ), False
    
    except Exception as e:
        return styled_error(f"An error occurred: {e}"), captcha_ok