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# Python 3 program to find the stem 
# of given list of words 

# function to find the stem (longest 
# common substring) from the string array 
def findstem(arr): 

	# Determine size of the array 
	n = len(arr) 

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[{"name": "sample 1", "image": "url 1"}, {"name": "sample 2", "image": "url 2"}]

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import cv2

# grayscale version of the single color image
image_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

# bilateral filter is effective when you want to
# keep the edges sharp while removing noise
image_gray = cv2.bilateralFilter(image_gray, 10, 50, 50)

# find contour in gray scale image after applying erosion and dilation

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# Reference: https://code.likeagirl.io/finding-dominant-colour-on-an-image-b4e075f98097
from sklearn.cluster import KMeans

# word block colors (color palette)
block_colors = []

# change dimension to (width x height, color-channels)
screen = screen.reshape((screen.shape[0] * screen.shape[1], 3))

# Collect 8 major colors in the image using KMeans clustering

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from gensim.models.keyedvectors import KeyedVectors

# load local word2vec model
model = KeyedVectors.load_word2vec_format(
    os.getenv('SEMANTRIS_SOLVER_WORD2VEC_PATH'),
    binary=True
)

# list of tuples containing the word and similarity score
associated_word_tuples = model.most_similar(word, topn=20)

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import pytesseract

image_text = pytesseract.image_to_string(cropped_image)

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import cv2
import numpy as np

# load and find the dimension attritbutes of the template image
template_img = cv2.imread('./gray-template.png', 0)
w, h = template_img.shape[::-1]

# OpenCV's template matching method
res_img = cv2.matchTemplate(
    screen2, template_img, cv2.TM_CCOEFF_NORMED

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import cv2
import pyautogui

# using a screen size specific region
screen = pyautogui.screenshot(region=(200, 100, 1000, 700))
screen_img_gray = cv2.cvtColor(np.array(screen), cv2.COLOR_RGB2GRAY)

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#
# Setup the Docker repository
#

echo "update apt package index"
sudo apt-get update

echo "allow apt to use HTTPS"
sudo apt-get install \
	apt-transport-https \

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document.addEventListener('DOMContentLoaded', function() {
  document.querySelectorAll('label')[0].classList.add('button-press');

  document.addEventListener('click', function(e) {
    var e = e || window.event,
        target = e.target || e.srcElement,
        text = target.textContent || target.innerText;

    if (target.tagName.toLowerCase() == 'label') {
      var labels = document.querySelectorAll("label");