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
from src.about import Training_Dataset


REQUESTED_MODELS = None

def add_new_eval(
    model_name : str = None,
    model_link : str = None,
    model_backbone : str = "Unknown",
    precision : str = None,
    model_parameters: float = 0,
    paper_name: str = "None",
    paper_link: str = "None",
    training_dataset: str = "",
    testing_type : str = None,
    cmap_value : float = 0,
    auroc_value : float = 0,
    t1acc_value : float = 0,
):

    global REQUESTED_MODELS
    if not REQUESTED_MODELS:
        REQUESTED_MODELS = already_submitted_models(EVAL_REQUESTS_PATH)

    if model_name is None or model_name == "":
        return styled_error("Please enter a model name")
    
    if model_link is None or model_link == "":
        return styled_error("Please provide a link to your model")

    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")

    training_dataset_type = Training_Dataset.from_str(training_dataset)
    if training_dataset_type.name != Training_Dataset.Other.name:
        training_dataset = training_dataset_type.name

    training_dataset = "_".join(training_dataset.split())
    model_name = "_".join(model_name.split())
    testing_type = testing_type.lower()

    print("Adding new eval")

    eval_entry = {
        "model_name": model_name,
        "model_link": model_link,
        "model_backbone": model_backbone,
        "precision": precision,
        "model_parameters": model_parameters,
        "paper_name": paper_name,
        "paper_link": paper_link,
        "status": "PENDING",
        "submitted_time": current_time,
        "training_dataset": training_dataset,
        "testing_type": testing_type,
        "claimed_cmap": cmap_value,
        "claimed_auroc": auroc_value,
        "claimed_t1acc": t1acc_value
    }

    if f"{model_name}_{training_dataset}_{testing_type}_{precision}" in REQUESTED_MODELS:
        return styled_warning("This model has been already submitted.")

    print("Creating eval file")
    try:
        OUT_DIR = f"{EVAL_REQUESTS_PATH}/{model_name}"
        os.makedirs(OUT_DIR, exist_ok=True)
        out_path = f"{OUT_DIR}/{model_name}_eval_request_{precision}_{training_dataset}_{testing_type}.json"

        with open(out_path, "w") as f:
            f.write(json.dumps(eval_entry))
    except:
        return styled_error("There was an error while creating your request. Make sure there are no \"/\" in your model name.")

    print("Uploading eval file")
    try:
        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}_{training_dataset}_{testing_type} to eval queue",
        )
    except:
        return styled_error("There was an error while uploading your request.")

    # 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."
    )