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| import json | |
| import os | |
| from datetime import datetime, timezone | |
| import src.display.formatting as formatting | |
| import src.envs as envs | |
| import src.submission.check_validity as check_validity | |
| REQUESTED_MODELS = None | |
| USERS_TO_SUBMISSION_DATES = None | |
| 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 = check_validity.already_submitted_models(envs.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 formatting.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, _ = check_validity.is_model_on_hub(model_name=base_model, revision=revision, token=envs.TOKEN, test_tokenizer=True) | |
| if not base_model_on_hub: | |
| return formatting.styled_error(f'Base model "{base_model}" {error}') | |
| if not weight_type == "Adapter": | |
| model_on_hub, error, _ = check_validity.is_model_on_hub(model_name=model, revision=revision, test_tokenizer=True) | |
| if not model_on_hub: | |
| return formatting.styled_error(f'Model "{model}" {error}') | |
| # Is the model info correctly filled? | |
| try: | |
| model_info = envs.API.model_info(repo_id=model, revision=revision) | |
| except Exception: | |
| return formatting.styled_error("Could not get your model information. Please fill it up properly.") | |
| model_size = check_validity.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 formatting.styled_error("Please select a license for your model") | |
| modelcard_OK, error_msg = check_validity.check_model_card(model) | |
| if not modelcard_OK: | |
| return formatting.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 formatting.styled_warning("This model has been already submitted.") | |
| print("Creating eval file") | |
| OUT_DIR = f"{envs.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") | |
| envs.API.upload_file( | |
| path_or_fileobj=out_path, | |
| path_in_repo=out_path.split("eval-queue/")[1], | |
| repo_id=envs.QUEUE_REPO, | |
| repo_type="dataset", | |
| commit_message=f"Add {model} to eval queue", | |
| ) | |
| # Remove the local file | |
| os.remove(out_path) | |
| return formatting.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." | |
| ) | |