def get_subwords(word):
logging.info(f"Iterating over permutations of {word}")
print_and_say(word, next_voice=True)
variations = variants(word)
# skip = next(variations)
for i, variant in enumerate(variations, start=1):
prefix = f"{i}.\t"
if variant in words:
yield variant
def read_antonyms(word):
antonyms = dictionary.antonym(word)
if antonyms is None:
print_and_say(f"No antonyms known for {word}.")
else:
print_and_say(f"Found {len(antonyms)} antonyms for {word}.")
for n, antonym in enumerate(antonyms):
print_and_say(antonym, print_prefix=f"\t{n + 1}.\t")
yield antonym
def read_synonyms(word):
synonyms = dictionary.synonym(word)
if synonyms is None:
print_and_say(f"No synonyms known for {word}.")
else:
print_and_say(f"Found {len(synonyms)} synonyms for {word}.")
for n, synonym in enumerate(synonyms):
print_and_say(synonym, print_prefix=f"\t{n + 1}.\t", print_suffix='.')
yield synonym
def read_definitions(word):
definition = dictionary.meaning(word)
if definition is None:
print_and_say(f"The definition for {word} is unknown.")
unknown_words.add(word)
else:
for part_of_speech, meanings in definition.items():
print_and_say(f"{word}, the {part_of_speech}, has {len(meanings)} meanings.")
for n, meaning in enumerate(meanings):
print_and_say(f"\t{n + 1}.\t{meaning}")
for related_word in meaning.split(" "):
yield related_word
def lookup(word):
print_and_say(word)
for definition in urbandict.define(word):
print_and_say(f"Definition: {definition['def']}", print_prefix="\t")
for related_entry in filter(lambda rw: rw not in en_stopwords,
definition['def'].split()):
yield related_entry
print_and_say(f"For example: {definition['example']}",
print_prefix="\t")
for related_entry in filter(lambda rw: rw not in en_stopwords,
definition['example'].split()):
yield related_entry
from random import sample, randint
from string import ascii_letters
from nltk.corpus import brown
from misty.utils import print_and_say
category = sample(brown.categories(), 3)[0]
category = None
# print_and_say(f"Selected {category} for sub words")
logging.warning("Loading Brown University corpus of words")
words = set(map(str.lower,
brown.words(categories=category))) # nltk.corpus.brown
print_and_say(f'Loaded {len(words)} "{category}" words.')
def variants(charset):
for l in range(len(charset), 1, -1):
for combination in permutations(charset, l):
yield ''.join(combination)
def get_subwords(word):
logging.info(f"Iterating over permutations of {word}")
print_and_say(word, next_voice=True)
variations = variants(word)
# skip = next(variations)
for i, variant in enumerate(variations, start=1):
prefix = f"{i}.\t"
from yelpapi import YelpAPI
from misty.utils import print_and_say
YELP_API_KEY = 'h81ylaT0alwtJCUUyI7RazCCHNHleVGnhD9ZONPT1s4kL9v5qhCXPZrcI20H4LYisDEjJZu_j4ibEsSTpM2ISDpWBeraK3t42rwV_PhxtYvmatDn2xquIUKdueYtYHYx' # plz no steal my api keyz!
client = YelpAPI(YELP_API_KEY)
biz_ids = [
'pierce-j-r-plumbing-co-inc-of-sacramento-rocklin',
'ncm-roseville',
]
random.shuffle(biz_ids)
for biz_id in biz_ids:
result = client.business_query(id=biz_id)
print_and_say(
f"{result['name']}. Phone number: {result['display_phone']}. Address: {''.join(result['location']['display_address'])}",
next_voice=True)
reviews = client.reviews_query(id=result['id'])
print_and_say(
f"Retrieved {len(reviews['reviews'])} of {reviews['total']} reviews.",
next_voice=True)
for review in reviews['reviews']:
print_and_say(
f"On {review['time_created']} {review['user']['name']} gave a rating of {review['rating']} stars, stating: {review['text']}.",
next_voice=True)
def lookup(word):
word = "".join(x for x in word if x in set(string.ascii_letters))
print_and_say(word, print_prefix=bcolors.UNDERLINE + bcolors.BOLD, print_suffix=bcolors.END)
yield from read_synonyms(word)
yield from read_antonyms(word)
yield from read_definitions(word)