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from __future__ import annotations

from uuid import UUID

from litestar import Litestar, get
from litestar.contrib.sqlalchemy.base import UUIDBase
from litestar.contrib.sqlalchemy.dto import SQLAlchemyDTO, SQLAlchemyDTOConfig
from litestar.contrib.sqlalchemy.plugins import (
    AsyncSessionConfig,
    SQLAlchemyAsyncConfig,

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import json

import dash
import dash_bootstrap_components as dbc
import dash_html_components as html
from dash.dependencies import ALL, Input, Output, State
from dash.exceptions import PreventUpdate

FONT_AWESOME = "https://use.fontawesome.com/releases/v5.13.0/css/all.css"
app = dash.Dash(external_stylesheets=[dbc.themes.BOOTSTRAP, FONT_AWESOME])

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"""
Fairlearn expects scikit-learn models. This submodule has some shims to work
around that issue.
"""
import numpy as np
import tensorflow as tf


class KerasWrapper:
    def __init__(self, model, model_path="/tmp/corrected-model.h5"):

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import matplotlib.pyplot as plt
import numpy as np
import requests

# data source
URL = "http://www.stat.columbia.edu/~gelman/book/data/light.asc"

# simulation hyperparams
N_SIM = 200  # number of simulations

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# -*- coding: utf-8 -*-
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
import pandas as pd
import datetime as dt
from io import StringIO

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# -*- coding: utf-8 -*-
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
import pandas as pd
import datetime as dt
from io import StringIO

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import dash
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State


def parse_query_params(s):
    s = s.lstrip("?")
    return dict(pair.split("=") for pair in s.split("&"))