Draw wordcloud in Python

wordcloud.py

from collections import Counter
from io import BytesIO
import os

# Optional: If you already downloaded NLTK resources at a non-standard location
# Espcially useful in case of AWS Lambda - you can put already downloaded nltk resources in layer and use in AWS Lambda
# Here path = /opt because that's where AWS Lambda puts all Layer contents
# ALREADY_DOWNLOADED_NLTK_PATH = '/opt'
ALREADY_DOWNLOADED_NLTK_PATH = None       # nltk resources not already downloaded, to download

if ALREADY_DOWNLOADED_NLTK_PATH is not None:
  print("Using already downloaded nltk resources...")
  os.environ['NLTK_DATA'] =  ALREADY_DOWNLOADED_NLTK_PATH
  # using following already downloaded ntlk resources (paths inside folder ALREADY_DOWNLOADED_NLTK_PATH)
  # * corpora/stopwords/
  # * tokenizers/punkt/

from PIL import Image
from wordcloud import WordCloud
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
import grapheme

if ALREADY_DOWNLOADED_NLTK_PATH is None:
  print("Downloading nltk resources...")
  nltk.download('punkt')
  nltk.download('stopwords')

english_stopwords = stopwords.words('english')

# Download this font from https://github.com/stamen/toner-carto/blob/master/fonts/Arial-Unicode-Regular.ttf and then put location in this variable
# Reason for using Arial-Unicode-Regular.ttf font is that it supports ALL languages' unicode rendering glyphs
font_path = '/path/to/Arial-Unicode-Regular.ttf'   

def pillow_image_to_bytes(image: Image, extension: str) -> bytes:
    out = BytesIO()
    image.save(out, extension)
    return out.getvalue()
  
def wordcloud(text: str, image_name: str) -> bytes:
    """Draw wordcloud of text, return bytes of rendered wordcloud image.
    Stopwords and singe-character words are removed before drawing wordcloud.
    """
    words = [
        word 
        for word in word_tokenize(text) 
        if word not in english_stopwords and grapheme.length(word) > 1
    ]
    print("Words after removing stopwords:", words)
    word_freqs = Counter(words)
    wc = WordCloud(width=800, height=400, background_color='white', font_path=font_path)
    wc.generate_from_frequencies(word_freqs)
    image = wc.to_image()
    return pillow_image_to_bytes(image, 'JPEG')