🚧🚧🚧🚧🚧 Textile 🚧🚧🚧🚧🚧

Textile is your ultimate companion for mastering robust German texts and building your vocabulary. It serves as a powerful tool that not only showcases grammar cases but also aids in reinforcing language rules while immersing yourself in the text.

Textile uses the spaCy library for natural language processing, relying on the de_core_news_sm model provided by spaCy:

• spaCy: Industrial-strength Natural Language Processing in Python. Explosion AI. Available at: https://spacy.io/.

it is still under construction.

How it works?

As you read through sturdy German texts, Textile enhances your learning experience by providing real-time assistance. It intelligently showcases grammar cases, explains word meanings, and offers insights into language rules. The extracted vocabulary becomes the foundation for personalized Anki flashcards, enabling you to reinforce your memory and grasp German language intricacies effortlessly.

Features

• meaning of the words.
• cases (nominative, accusative, dative or genitive).
• gender of words.
• tables of conjunction.
• generates anki flashcards for later memorization.
• builds an offline database.

roadmap

• input a text from a txt file

major dependencies

• pip3 install pandas
• pip3 install genanki
• pip3 install spacy
• python3 -m spacy download de_core_news_sm
• pip3 install pandarallel
• pip3 install rich
• pip3 install textual
• pip3 install bs4
• pip3 install german_nouns

install

pip3 install -r requirements.txt
python3 -m spacy download de_core_news_sm
pip install .

running pip install . from within the source folder will expose textile launcher.

code format for development

from within the src folder:

autopep8 --in-place --aggressive --aggressive --max-line-length 100 --indent-size 4 ./*.py
# or
python3 -m black .

usage

python3 -m textile

Roadmap

• add logging system
• spread logging on all parts
• use similar strategy as nouns to save the order of analyzis for verbs, adj ...
• save the the scroll offset for each word in a dict of uinque values
• make hover active for specific categories: nouns, verbs, adjective and so on
• scroll to offset based on hovering action
• make analysis color based on the word gender not plural (Msc -> green, Fmn->red, Neut->blue)
• add exit button that closes textile
• none is not acceptable especially for easy texts, add more nouns parsers
• change csv into a faster database like sqlite3
• enhance nouns parsing performance
• performance overall
• enhance anki tickets

categories of part of speech that spacy outputs

• ADJ: adjective, e.g. big, old, green, incomprehensible, first
• ADP: adposition, e.g. in, to, during
• ADV: adverb, e.g. very, tomorrow, down, where, there
• AUX: auxiliary, e.g. is, has (done), will (do), should (do)
• CONJ: conjunction, e.g. and, or, but
• CCONJ: coordinating conjunction, e.g. and, or, but
• DET: determiner, e.g. a, an, the
• INTJ: interjection, e.g. psst, ouch, bravo, hello
• NOUN: noun, e.g. girl, cat, tree, air, beauty
• NUM: numeral, e.g. 1, 2017, one, seventy-seven, IV, MMXIV
• PART: particle, e.g. ’s, not,
• PRON: pronoun, e.g I, you, he, she, myself, themselves, somebody
• PROPN: proper noun, e.g. Mary, John, London, NATO, HBO
• PUNCT: punctuation, e.g. ., (, ), ?
• SCONJ: subordinating conjunction, e.g. if, while, that
• SYM: symbol, e.g. $, %, §, ©, +, −, ×, ÷, =, :), 😝
• VERB: verb, e.g. run, runs, running, eat, ate, eating
• X: other, e.g. sfpksdpsxmsa
• SPACE: space, e.g.