"""This is the main app that runs in your AWS Lambda function."""

import base64
import io
import json
import logging
import os
# Import libraries
import tempfile

import boto3
import botocore
import joblib
import numpy as np
import pandas as pd
from digital_twin import DigitalTwin
from quantile_regression import QuantileRegression

import optuna

features = [
    "flow",
    "pressure",
]
target = "power"
config_col = "config"

twin = joblib.load("model.joblib")


def lambda_handler(event, context):
    """Main entry point for the AWS Lambda function.

    :event: Must contain 'body' and 'key' where
        'body' = the observed number from your machine
        'key' = the model location in your S3 bucket
    :context: The AWS Lambda context sent to the function.

    :return: The dict with status code and output_body

    """
    boundary = {
        "c1c2": [180, 390],
        "c1c2c4": [180, 390],
        "c1c4": [180, 390],
        "c1c3c4": [180, 460],
        "c1c2c3c4": [180, 460],
        "c1c3": [180, 390],
        "c1c2c3": [180, 460],
        "c3c4": [180, 390],
        "c2c3c4": [180, 460],
        "c2c4": [180, 390],
        "c2c3": [180, 390],
    }

    clf = [
        "c1c2",
        "c1c2c4",
        "c1c4",
        "c1c3c4",
        "c1c2c3c4",
        "c1c3",
        "c1c2c3",
        "c3c4",
        "c2c3c4",
        "c2c4",
        "c2c3",
    ]
    flow = json.loads(event["flow"])
    press = json.loads(event["pressure"])
    n = int(json.loads(event["simulations"]))
    no_of_trials = int(json.loads(event["no_of_trials"]))
    train_error_weight = float(json.loads(event["train_error_weight"]))

    results = twin.run_monte_carlo(
        clf=clf,
        flow=flow,
        press=press,
        n=n,
        no_of_trials=no_of_trials,
        boundary=boundary,
        train_error_weight=train_error_weight,
        algorithm="keras_regression"
    )

    # Return your response
    results = [{list(x.keys())[0]: float(x[list(x.keys())[0]]/n)} for x in results]

    logging.info("Return...")
    return {
        "statusCode": 200,
        "body": json.dumps(results),
    }