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<!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 { |
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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", |
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//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; |
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<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; |
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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 |
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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 { |
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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() | |
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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 |
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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) { |
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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"; |