import huggingface_hub as hf_hub
import uuid
import glob
import json

import constants

hf_api = hf_hub.HfApi(
    endpoint="https://huggingface.co", 
    token=constants.TOKEN_HUB, 
)


#
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#


def is_model_on_hub(model_id):
    """Check if a model is on the hub and return a failure message if not."""

    try:
        author = model_id.split("/")[0]
        model_name = model_id.split("/")[1]
        if len(author) == 0 or len(model_name) == 0:
            return "is not a valid model name. Please use the format `author/model_name`."
    except:
        return "is not a valid model name. Please use the format `author/model_name`."

    try:
        models = list(hf_api.list_models(author=author, search=model_name))
        matched = [model_id for m in models if m.modelId == model_id]
        if len(matched) != 1:
            return "was not found on the hub!"
        else:
            return None
    except:
        return "was not found on hub!"


def request_model(model_id, evaluated_models, requested_models):

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

    def styled_message(message):
        return f"<p style='color: green; font-size: 20px; text-align: center;'>{message}</p>"
    
    # Check if the model is on the hub
    error_msg = is_model_on_hub(model_id)
    if error_msg != None:
        return styled_error(f"{model_id} {error_msg}")
    
    # Check if the model was already evaluated
    if model_id in evaluated_models:
        return styled_error(f"{model_id} has already been evaluated.")
    
    # Check if the model was already requested
    if model_id in requested_models:
        return styled_error(f"A request for {model_id} was already made.")
    
    # Add the model to the local evaluation queue
    with open("requested_models.txt", "a") as f:
        f.write(model_id + "\n")

    # Push the request file to the private dataset repo
    unique_request_filename = str(uuid.uuid4()) + ".json"
    with open(unique_request_filename, "w") as f:
        json.dump(model_id, f)
    hf_api.upload_file(
            path_or_fileobj=unique_request_filename,     #local_filepath
            path_in_repo=unique_request_filename,
            repo_id=constants.DATASET_REPO,
            token=constants.TOKEN_HUB,
            repo_type="dataset",
            commit_message=f"{model_id} added to the evaluation queue.",
    )

    # Record the request
    requested_models.append(model_id)

    return styled_message("🤗 Your request has been submitted and will be evaluated as soon as possible!")


def get_requested_models():
    """Returns the list of requested models present in the private dataset repo."""

    path_ = hf_api.snapshot_download(
                repo_id=constants.DATASET_REPO,
                repo_type="dataset",
                allow_patterns="*.json",
                # token=constants.TOKEN_HUB,
            )
    
    requested_models = []
    for json_file in glob.glob(path_ + "/*.json"):
        with open(json_file, "r") as f:
            model_id = json.load(f)
        requested_models.append(model_id)
    
    return requested_models