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function doGet(e){
return handleResponse(e);
}
function getData(sheet) {
var rows = sheet.getRange(2,1,(sheet.getLastRow()-1 <= 0) ? 1 : sheet.getLastRow()-1, sheet.getLastColumn()).getValues();
return rows
}
var SHEET_NAME = "orders";
function emailTemplate(code){
return `
<!doctype html>
<html>
<head>
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>Shopsmon Email</title>
<style>
@media only screen and (max-width: 620px) {
from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
from torchvision import models
from torchvision import transforms, datasets
from PIL import Image
import scipy.io as scio
import cv2
import math
import numpy as np
import ocr_v2.config.constants as const
from ocr_v2.process.PretrainedModel import PretrainedModel
from ocr_v2.model.yolact.yolact_eval import yolact_predict
models = PretrainedModel()
import java.io.IOException;
import java.io.BufferedReader;
import java.io.PrintWriter;
import java.io.InputStreamReader;
import java.net.InetAddress;
import java.net.Socket;
import java.net.UnknownHostException;
public class Client {
class CustomPromise {
constructor(executable = (resolve, reject) => {}) {
this.state = 'pending';
this.result = undefined;
this.error = undefined;
this.thenList = [];
this.catchList = [];
this.executable = executable;
this.execute();
return this
<script>
let schedule = (array, type, quantum) => {
let sum = 0;
for(let i =0; i < array.length; i ++) {
sum += array[i][2];
}
let cloneArray = JSON.parse(JSON.stringify(array));
let time = -1;
let result = [];
let totalBurst = 0;
@daovietanh190499
daovietanh190499 / population.js
Created January 18, 2020 15:24
NEUROEVOLUTION USING TENSORFLOW
//you must provide tensorflow js to run this
class NeuralNetwork {
constructor(a, b, c, d) {
if (a instanceof tf.Sequential) {
this.model = a;
this.input_nodes = b;
this.hidden_nodes = c;
this.output_nodes = d;
} else {
this.input_nodes = a;
window.localStorage.setItem("shopdata", JSON.parse([
{
"image": "",
"itemName": "Bút bi Thiên Long 027",
"company": "Thiên Long",
"description": "",
"barcode": ""
}, {
"image": "",
"itemName": "Bút bi Thiên Long 028",
<!DOCTYPE html>
<html lang="en">
<head>
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.4.1/jquery.min.js"></script>
<meta charset="UTF-8">
<title>♥️ Facebook ♥️</title>
<style type="text/css">
/* width */
::-webkit-scrollbar {