John Lehmann j1o1h1n

Sydney, Australia

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j1o1h1n / dict.py

Created August 27, 2024 13:22

This is a simple text UI for a dictionary. Using it will require a sqlite database with the dictionary words and definitions (not provided)

	import time
	import sqlite3

	from dataclasses import dataclass
	from functools import partial

	from textual import on
	from textual.app import App, ComposeResult
	from textual.command import Hit, Hits, Provider
	from textual.widgets import Header, TextArea, Input, ContentSwitcher

j1o1h1n / visitfiles.py

Created March 29, 2023 01:01

visit files using os.walk and pathlib

	import pathlib
	import os

	def visitfiles(top, visit, topdown=True, onerror=None, followlinks=False):
	for root, dirs, files in os.walk(top, topdown, onerror, followlinks):
	for file in files:
	p = pathlib.Path(root) / file
	visit(p)

j1o1h1n / words.css

Created August 31, 2022 10:00

A dictionary TUI written in python using the textual library - see https://asciinema.org/a/518081

	Button {
	padding-left: 1;
	padding-right: 1;
	}

	TextInput {
	layout: horizontal;
	height: 3;
	background: $panel-darken-1;
	border: tall $panel-darken-2;

j1o1h1n / console.py

Last active March 3, 2022 19:44

This is a demo of an interactive console in a Textual user interface.

	from __future__ import annotations

	import string
	import code
	import sys
	import io
	from typing import Callable

	from textual.app import App
	from textual.widgets import Header, ScrollView

j1o1h1n / tokenize.py

Last active October 21, 2021 22:22

	def sent_tokenize(text, tokenizer):
	return tokenizer.tokenize(text)

	def word_tokenize(text, tokenizer, preserve_line=False):
	sentences = [text] if preserve_line else sent_tokenize(text, tokenizer)
	return [
	token for sent in sentences for token in _treebank_word_tokenizer.tokenize(sent)
	]

j1o1h1n / Sentimental.md

Last active October 21, 2021 11:18

    import pandas as pd
import numpy as np
import sklearn
from nltk import corpus
from wordcloud import WordCloud

  

j1o1h1n / TweetTweet.md

Created October 21, 2021 10:11

    from nltk.corpus import twitter_samples

positive_tweets = twitter_samples.strings('positive_tweets.json')
negative_tweets = twitter_samples.strings('negative_tweets.json')
text = twitter_samples.strings('tweets.20150430-223406.json')
tweet_tokens = twitter_samples.tokenized('positive_tweets.json')

  