Shawn Graham shawngraham

Getting Pastec up and running

Pastec is an open source index and search engine for image recognition. This is how I got it working with lots of help from the hard work of Ryan Baumann, Shawn Graham and Matthew Lincoln.

Installation

Either install Ubuntu 14.04.5 as an operating system, or get a virtual machine from osboxes. Fire up with VirtualBox. Ensure VM is connected to the network (Settings>Network).

Install Pastec by following the documentation. Be sure to download and unzip visualWordsORB.dat into the build subdirectory of Pastec.

	// SPDX-License-Identifier: MIT
	//
	// This code will add a GPT3() function in your Google Sheets
	// This code is originally inspired from https://twitter.com/fabianstelzer/status/1572926883179778050
	// To use it, insert your API key below, open Google Sheets -> Extensions -> Apps Script -> Copy & Paste this -> Save
	//
	// Usage: =GPT3(prompt, max_tokens (default=15), model (default=davinci))
	// Example usage: =GPT3("Once upon a time,", 1000, "davinci")

	var API_KEY = "your-API-key";


	#!/usr/bin/python
	## Split audio files into chunks
	## Daniel Pett 1/5/2020
	__author__ = 'portableant'
	## Tested on Python 2.7.16 - yes I know I need to upgrade.

	import argparse
	import os
	import speech_recognition as sr

	# This script builds on Aleszu Bajak's excellent
	# [tutorial on building a course website using R Markdown and Github pages](http://www.storybench.org/convert-google-doc-rmarkdown-publish-github-pages/).
	# I was excited about the concept but wanted to automate a few of the production steps: namely generating the HTML files
	# for the site from the RMD pages (which Aleszu describes doing one-by-one) and generating the site navigation menu,
	# which Aleszu handcodes in the `_site.yml` file. This script should automate both processes, though it may have some quirks
	# unique to my setup that you'd want to tweak to fit your own. It's likely more loquacious than necessary as well, so feel free
	# to condense as you can. Ideally, each time you make updates to your RMD files you can run this script to generate updated HTML
	# pages and a new `_site.yml`. Then commit changes to Github and you're up and running!

	# Once you've got everything configured for your own site below, you should be able to run `source('rend

	---
	title: "Programming Literary Bots"
	author: "Ryan Cordell"
	date: "3/12/2017"
	output: html_document
	---

	## Acknowledgements

	This version of my twitterbot assignment was adapted from [an original written in Python](https://www.dropbox.com/s/r1py3zazde2turk/Trendingmore.py?dl=0), which itself adapted code written by Mark Sample. That orginal bot tweeted (I've since stopped it) at [Quoth the Ravbot](https://twitter.com/Quoth__the). The current version owes much to advice and code borrowed from two colleagues at Northeastern University: Jonathan Fitzgerald and Benjamin Schmidt.

	from __future__ import absolute_import, division, print_function

	"""

	This is a modification of the classify_images.py
	script in Tensorflow. The original script produces
	string labels for input images (e.g. you input a picture
	of a cat and the script returns the string "cat"); this
	modification reads in a directory of images and
	generates a vector representation of the image using

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)



	# get recent changes from wikipedia
	library(rvest)
	n_changes <- 5000
	recent_changes_url <- paste0("https://en.wikipedia.org/w/index.php?title=Special:RecentChanges&limit=", n_changes , "&days=1")

	# connect to website
	html <- read_html(recent_changes_url)

	# This line imports the modules we will need. The first is the sys module used
	# to read the command line arguments. Second the Python Imaging Library to read
	# the image and third numpy, a linear algebra/vector/matrix module.
	import sys; from PIL import Image; import numpy as np

	# This is a list of characters from low to high "blackness" in order to map the
	# intensities of the image to ascii characters
	chars = np.asarray(list(' .,:;irsXA253hMHGS#9B&@'))

	# Check whether all necessary command line arguments were given, if not exit and show a

	# get data
	setwd("C:/Downloads/html") # this folder has only the HTML files
	html <- list.files()

	# load packages
	library(tm)
	library(RCurl)
	library(XML)
	# get some code from github to convert HTML to text
	writeChar(con="htmlToText.R", (getURL(ssl.verifypeer = FALSE, "https://raw.github.com/tonybreyal/Blog-Reference-Functions/master/R/htmlToText/htmlToText.R")))