import json
import os
from datetime import datetime, timezone
from ast import literal_eval

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

class CustomJSONEncoder(json.JSONEncoder):
    def default(self, obj):
        try:
            return super().default(obj)
        except TypeError:
            return str(obj)  # Convert non-serializable object to string

def add_new_eval_json(eval_entry, out_path):
    with open(out_path, "w") as f:
        f.write(json.dumps(eval_entry, cls=CustomJSONEncoder))

def add_new_eval(
    author,
    email,
    relbench_version,
    model,
    official_or_not,
    test_performance,
    valid_performance,
    paper_url,
    github_url,
    #parameters,
    honor_code,
    task_track
):
    global REQUESTED_MODELS
    global USERS_TO_SUBMISSION_DATES
    if not REQUESTED_MODELS:
        REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)

    if task_track in ['Node Classification', 'Entity Classification']:
        task_type = 'nc'
    elif task_track in ['Node Regression', 'Entity Regression']:
        task_type = 'nr'
    elif task_track in ['Link Prediction', 'Recommendation']:
        task_type = 'lp'

    model_path = model + '_' + task_type

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

    #model_size = parameters

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

    eval_entry = {
        "model": model,
        "author": author,
        "email": email,
        "relbench_version": relbench_version,
        "official_or_not": official_or_not,
        "test": test_performance,
        "valid": valid_performance,
        "paper_url": paper_url,
        "github_url": github_url,
        "honor_code": honor_code,
        "status": "PENDING",
        "submitted_time": current_time,
        #"params": model_size,
        "task": task_track,
        "private": False,
    }
    
    ## add a checking to verify if the submission has no bug
    try:
        xx = literal_eval(eval_entry["test"])
        xx = literal_eval(eval_entry["valid"])
    except:
        return styled_error("The testing/validation performance submitted do not follow the correct format. Please check the format and resubmit.")

    # TODO: Check for duplicate submission
    #if f"{model}" in REQUESTED_MODELS:
    #    return styled_error("This model has been already submitted.")

    print("Creating eval file")
    OUT_DIR = f"{EVAL_REQUESTS_PATH}/{model}"
    os.makedirs(OUT_DIR, exist_ok=True)
    out_path = f"{OUT_DIR}/{model_path}_eval_request_False.json"

    print(eval_entry)

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

    add_new_eval_json(eval_entry, out_path)

    print("Uploading eval file")
    print(out_path)
    print(QUEUE_REPO)
    print(TOKEN)
    print(API)
    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."
    )