AI tips have finally made their way onto our test site, after having spent some time coding the hub to process the received data each second (using Python) and read from the files produced by this process when the tips dialog box is opened. The adjusted Python code for the hub is as follows: import os, pause from datetime import datetime def updateTips(): messages = ["outlet,message\n"] bad_usage =  amazing_usage =  rising_usage =  lowering_usage =  for file in os.listdir(os.fsencode("rates/daily")): outlet = os.fsdecode(file).replace(".csv", "") now = datetime.datetime.now() with open("totals/daily.csv", 'r') as file: data = file.readlines() line_number = [index for index, item in enumerate(data) if (outlet + ",") in item] daily_value = float(data[line_number]) with open("totals/weekly.csv", 'r'
Showing posts from March, 2019
- Other Apps
- Other Apps
I've finished coding the website! I used the Material Design Lite Dashboard template ( https://getmdl.io ), and made significant theme and content changes (including creating a dark theme, based on the two main colours in our logo - #1A1A1A & 006DCC). The dashboard now looks like this: It depends on the following resources: Material Design (material.min.js, material-theme.min.css) Material Icons (material-icons.css) jQuery (jquery.min.js) Morris JS (morris.min.js, morris.css) Raphaël (raphael.min.js) Palette JS (palette.js) jQuery CSV is used to retrieve the data from CSV files on the local server, and Morris JS (which depends on Raphaël) is for the charts that are created from the CSV data, and Palette JS is used to create an array of colours forming a rainbow for each doughnut chart. The charts are configured to update every second; the code is all available open-source on GitHub . We are yet to implement the AI tips service, and are in the process of org