shaun-jacks

import * as fs from 'fs'
import * as path from 'path'

class Input {
  buffer: Buffer | undefined;
  parsed: number[][][]  | undefined
  readBuffer() {
    this.buffer = fs.readFileSync(path.join(process.cwd(), 'day5', 'input', 'input.txt'))
  }
  parseBuffer() {

shaun-jacks

import * as fs from 'fs'
import * as path from 'path'
import * as process from 'process'
const inputDir = path.join(process.cwd(), 'day4', 'input');
import { BehaviorSubject, combineLatest, concatMap, Observable, Subject } from 'rxjs'

function parseDraws(): number[] {
  const drawsString = fs.readFileSync(path.join(inputDir, 'random_numbers.txt'), 'utf-8').toString();
  return drawsString.split(",").map(num => parseInt(num, 10))
}

shaun-jacks

# This generates a string that will be used within the render fxn
# in handsontable(). The string needs two parameters for this fxn to work:
# 1. A list of columns: so a list named col_highlight, with keys:
# colhighlight1, colhighlight2 ... up to vars_len. Each key has a corresponding
# value which is the column number within the handsontable to color
# 1. A list of lists of rows: so a list named row_highlight, with keys:
# rowhighlight1, row_highlight2 ... up to vars_len. Each key has a corresponding
# value which is a vector of row numbers within the handsontable to color 

# The highlighted cells are the cells represented by colhighight1, rowhighlight1,...

shaun-jacks

values$img_res
|
|____$fdBoundingPoly
|      |
|      |_____$vertices
|             |_____[[1]] # face 1
|                   |_______$x:   [[1]] [[2]] [[3]] [[4]]
|                   |       |____1285, 2322, 2322, 1285
| |

shaun-jacks

  # on click of restart button, reset all reactive values
  observeEvent(input$reset, {
    values$img_res = NULL
    values$file_path = ""
    photo = NULL
    values$analysis_type = ""
    values$analysis_selected = F
    values$warning_inputs = ""
    values$file_uploaded = F
    values$upload_step = 1

shaun-jacks

  #### Analysis Table ####
  output$results <- DT::renderDataTable({
    if (values$analysis_type == "FACE_DETECTION") {
      as.data.frame(
        list(
          joy = values$img_res$joyLikelihood, 
          sorrow = values$img_res$sorrowLikelihood,
          anger = values$img_res$angerLikelihood, 
          surprise = values$img_res$surpriseLikelihood
        )

shaun-jacks

shaun-jacks

## app.R ##
library(shinydashboard)

ui <- dashboardPage(
  dashboardHeader(title = "Basic dashboard"),
  ## Sidebar content
  dashboardSidebar(
    sidebarMenu(
      menuItem("Dashboard", tabName = "dashboard", icon = icon("dashboard")),
      menuItem("Widgets", tabName = "widgets", icon = icon("th"))

shaun-jacks

# stage 2 within our renderUI function
shiny::fluidPage(
  shiny::tagList(
    # Row 1: our analyzed image
    shiny::fluidRow(
      column(8, align = "center", 
        shinycssloaders::withSpinner(  # Spinner loading bar
          imageOutput("picture2"),  # Outputted Image
          color = '#999999'
        )

shaun-jacks

## UI STAGE 1 CODE #
# begin a sidebar ui page
shiny::sidebarLayout(
  shiny::sidebarPanel(
    title = "Image and Analysis Selection",
    # ask for file input from user
    shiny::fileInput(
      inputId = "file1",
      label   = "Input Image",
      accept  = c(