from os import walk
from os import listdir
from os.path import isfile, join

path = '/tmp'

onlyfiles = [f for f in listdir(path) if isfile(join(path, f))]

onlyfiles

['.BBE72B41371180178E084EEAF106AED4F350939DB95D3516864A1CC62E7AE82F']

for fle in onlyfiles:
    print(fle)

.BBE72B41371180178E084EEAF106AED4F350939DB95D3516864A1CC62E7AE82F

# Disclaimer: some code copied form this https://towardsdatascience.com/multi-class-text-classification-model-comparison-and-selection-5eb066197568

import logging
import pandas as pd
import numpy as np
from numpy import random
import gensim
import nltk
from sklearn.model_selection import train_test_split
from …

from nltk.stem import RegexpStemmer

re_stemmer = RegexpStemmer("ing$|s$|e$|able$", min=7)

words = [
    "wheels",
    "breaking",
    "thrones",
    "breakable"
]

words

['wheels', 'breaking', 'thrones', 'breakable']

result = [re_stemmer.stem(word) for word in words]

result

['wheels', 'break', 'throne', 'break']

from nltk.book import text1

*** Introductory Examples for the NLTK Book ***
Loading text1, ..., text9 and sent1, ..., sent9
Type the name of the text or sentence to view it.
Type: 'texts()' or 'sents()' to list the materials.
text1: Moby …

import re
from nltk.tokenize import word_tokenize
from collections import Counter
from nltk.corpus import stopwords

text = ("""The cat is in the box. The cat likes the box. The box is over the cat.""")

tokens = [w for …

from nltk.stem.snowball import SnowballStemmer

words = [
    "hunting",
    "bunnies",
    "thinking"
]

words

['hunting', 'bunnies', 'thinking']

stemmer = SnowballStemmer("english")

result = [stemmer.stem(word) for word in words]

result

['hunt', 'bunni', 'think']

import speech_recognition as sr

def startpy():

    # obtain audio from the microphone
    r = sr.Recognizer()
    d= ''
    while (d!='exit' and d!='quit'):
        with sr.Microphone() as source:
            print("Say something!")
            audio = r.listen(source)

    # recognize speech using Google …

from nltk.stem.snowball import SnowballStemmer

stemmer = SnowballStemmer("english")

print(stemmer.stem("having"))

have

stemmer2 = SnowballStemmer("english",  ignore_stopwords = True)

print(stemmer2.stem("having"))

having