Saravanan666/pypi-org huggingface dataset:
Package: text-game-map-maker
This tool allows you to use a graphical interface to build maps for thetext_game_makerpython package.RequirementsPython 3InstallationInstall with pip:pip install text_game_map_makerStartingtext_game_map_makerRuntext_game_map_makervia the Python interpreter:python -m text_game_map_makerOptionally, for windows users, there is arun_text_game_map_maker.batscript
you can click on to start the application.
Package: text-gen
Text Gen :goat:Almost State-of-the-art Text Generation libraryText gen is a python library that allow you build a custom text generation model with ease :smile:
 Something sweet built with Tensorflow and Pytorch(coming soon) - This is the brain of Rosalove ai (https://rosalove.xyz/)How to use itInstall text-genpipinstall-Utext-genimport the libraryfromtext_genimportten_textgenasttgLoad your data. your data must be in a text format.Download the example data from theexample folderload datadata='rl.csv'text=ttg.loaddata(data)build our Model Architeturepipeline=ttg.tentext(text)seq_text=pipeline.sequence(padding_method='pre')configg=pipeline.configmodel(seq_text,lstmlayer=128,activation='softmax',dropout=0.25)train modelmodel_history=pipeline.fit(loss='categorical_crossentropy',optimizer='adam',batch=300,metrics='accuracy',epochs=500,verbose=0,patience=10)generate text using the phrasepipeline.predict('hello love',word_length=200,segment=True)plot loss and accuracypipeline.plot_loss_accuracy()Hyper parameter optimizationTune your model to know the best optimizer, activation method to use.pipeline.hyper_params(epochs=500)pipeline.saveModel('model')#use a saved model for prediction#the corpus is the train text filettg.load_model_predict(corpus=corpus,padding_method='pre',modelname='../input/model2/model2textgen.h5',sample_text='yo yo',word_length=100)Give us a star :star:🐉If you want to contribute, take a look at the issues and theFuturework.mdfileContributorsEmeka Boris AmaKing ZikieDavid
Package: text-generation-api
text-generation-api📢text-generation-apiis a simple yet comprehensive REST API Server for text generation with huggingface modelsCouldn't be more easy to use 🔥Comes with batteries included 🔋fromtext_generation_apiimportEndpointtga=Endpoint("http://<host>:<port>")result=tga.generate(model="gpt2",prompt="Here is a list of things I like to do:")Features 🏆Serve every🤗 huggingfacemodel 🔥Batteries included🔋Built-in stop text or stop tokenNice one line serving and generation 😎Installation ⚙️(optional) Create avirtualenv. You can usecondaor whatever you likevirtualenv --python=python3.10 text-generation-api-env
. ./text-generation-api-env/bin/activateInstallpytorch(ortensorflow). Again, you can use whatever package manager you likepip install "torch>=2.0.0"Installtext-generation-apipip install text-generation-apiRun the server 🌐Createyamlconfig files for the models you want to serveFor example to serveGPT2the config file should look likemodel:name:gpt2class:GPT2Modeltokenizer:name:gpt2class:GPT2TokenizerTo specify load arguments for the model or the tokenizer use theloadkey like:model:name:my-modelclass:GPT2Modelload:device_map:autotrust_remote_code:Truetokenizer:name:gpt2class:GPT2TokenizerYou can specify which device to use withdeviceand which backend to use withbackend: pytorchorbackend: tensorflowTo run the inference server runtext-generation-api ./path/to/config1.yaml ./path/to/config2.yamlFor example:text-generation-api ./example/gpt2.yaml  ./example/opt-125.yamlRun the client ✨To run the inference on the remote server using the client simply:fromtext_generation_apiimportEndpointtga=Endpoint("http://<host>:<port>")result=tga.generate(model="<MODEL-NAME>",prompt="<PROMPT>")You can passgenerateortokenizearguments with:fromtext_generation_apiimportEndpointtga=Endpoint("http://<host>:<port>")result=tga.generate(model="<MODEL-NAME>",prompt="<PROMPT>",generate=dict(temperature=0.2,top_p=0.2))Use the argumentstopto control the stop beheviourfromtext_generation_apiimportEndpointtga=Endpoint("http://<host>:<port>")result=tga.generate(model="gpt2",prompt="Human: How are you?\nAI:",generate=dict(temperature=0.2,top_p=0.2),stop=dict(words=["\n"]))Contributions and license 🪪The code is released as Free Software under theGNU/GPLv3license. Copying, adapting and republishing it is not only allowed but also encouraged.For any further question feel free to reach me atfederico.galatolo@unipi.itor on Telegram@galatolo
Package: textgenrnn
Easily train your own text-generating neural network of
any size and complexity on any text dataset with a few lines
of code, or quickly train on a text using a pretrained model.A modern neural network architecture which utilizes new techniques as
attention-weighting and skip-embedding to accelerate training
and improve model quality.Able to train on and generate text at either the
character-level or word-level.Able to configure RNN size, the number of RNN layers,
and whether to use bidirectional RNNs.Able to train on any generic input text file, including large files.Able to train models on a GPU and then use them with a CPU.Able to utilize a powerful CuDNN implementation of RNNs
when trained on the GPU, which massively speeds up training time as
opposed to normal LSTM implementations.Able to train the model using contextual labels,
allowing it to learn faster and produce better results in some cases.Able to generate text interactively for customized stories.
Package: text-grade
text-gradeA Python tool to assist text analysis.Usageimportloggingimportspacyfromtext_gradeimportDocument,formulaslogging.basicConfig(level=logging.DEBUG)TEXTO="""O algoritmo de Flesch é uma fórmula matemática que é usada para avaliar a legibilidade de um texto em inglês. Ele foi desenvolvido por Rudolf Flesch, um escritor e lexicógrafo austríaco, e é comumente usado por editores, escritores e professores para avaliar a qualidade e a facilidade de leitura de um texto.A fórmula do algoritmo de Flesch usa duas medidas básicas do texto: o número de palavras e o número de sentenças. Ele também usa o número de sílabas em cada palavra e o número de palavras com duas ou mais sílabas. A partir dessas informações, o algoritmo calcula duas pontuações:O Índice de Legibilidade de Flesch: esta pontuação varia de 0 a 100 e é baseada no número de palavras e sentenças do texto. Quanto maior a pontuação, mais fácil é o texto de ler.A Fórmula de Facilidade de Leitura de Flesch: esta pontuação varia de 0 a 100 e é baseada no número de sílabas em cada palavra e no número de palavras com duas ou mais sílabas. Quanto maior a pontuação, mais fácil é o texto de ler.Para calcular a pontuação do Índice de Legibilidade de Flesch, utiliza-se a seguinte fórmula:206,835 - (1,015 x número médio de palavras por sentença) - (84,6 x número médio de sílabas por palavra)Para calcular a pontuação da Fórmula de Facilidade de Leitura de Flesch, utiliza-se a seguinte fórmula:(0,39 x número médio de palavras por sentença) + (11,8 x número médio de sílabas por palavra) - 15,59Ambas as pontuações podem ser usadas para avaliar a legibilidade do texto. Em geral, um índice de legibilidade de Flesch de 60 a 70 é considerado fácil de ler para a maioria das pessoas, enquanto uma pontuação de 30 a 50 é considerada difícil. Já a fórmula de facilidade de leitura de Flesch geralmente produz uma pontuação entre 0 e 100, com textos mais fáceis de ler apresentando uma pontuação mais alta."""nlp=spacy.load('pt_core_news_sm')doc=nlp(TEXTO)document=Document(doc)# sentencesprint(document.sentences)# syllablesprint(document.syllables)# wordsprint(document.words)score=formulas.flesch_index_pt_br(document)print(score)
Package: textgraphs
title: TextGraphs
emoji: ✴
colorFrom: green
colorTo: gray
sdk: streamlit
sdk_version: 1.28.2
app_file: app.py
pinned: false
license: mitTextGraphsproject infoProject home:https://huggingface.co/spaces/DerwenAI/textgraphsFull documentation:https://derwen.ai/docs/txg/Sample code is provided indemo.pyrequirementsPython 3.10+deploy library from PyPiPrepare the virtual environment:python3-mvenvvenvsourcevenv/bin/activate
python3-mpipinstall-UpipwheelsetuptoolsInstall fromPyPi:python3-mpipinstall-Utextgraphsrun demos locallypython3demo.pystreamlitrunapp.pyinstall library from source locallypython3-mvenvvenvsourcevenv/bin/activate

python3-mpipinstall-Upipwheelsetuptools
python3-mpipinstall-e.To run the Streamlit or JupyterLab demos, also install:python3-mpipinstall-rrequirements-dev.txtlicense and copyrightSource code forTextGraphsplus its logo, documentation, and
examples have anMIT licensewhich is succinct and simplifies use in commercial applications.All materials herein are Copyright © 2023-2024 Derwen, Inc.attributionPlease use the following BibTeX entry for citingTextGraphsif you
use it in your research or software:@software{TextGraphs,author={Paco Nathan},title={{TextGraphs + LLMs + graph ML for entity extraction, linking, ranking, and constructing a lemma graph}},year=2023,publisher={Derwen},doi={10.5281/zenodo.10431783},url={https://github.com/DerwenAI/textgraphs}}star history
Package: text-hammer
Dependenciespip install spacy==2.2.3
python -m spacy download en_core_web_sm
pip install beautifulsoup4==4.9.1
pip install textblob==0.15.3INSTALLATION
'''
pip install text_hammer'''How to use it for preprocessingYou have to have installed spacy and python3 to make it work.
import text_hammer as thdef get_clean(x):
    x = str(x).lower().replace('\\', '').replace('_', ' ')
    x = th.cont_exp(x)
    x = th.remove_emails(x)
    x = th.remove_urls(x)
    x = th.remove_html_tags(x)
    x = th.remove_rt(x)
    x = th.remove_accented_chars(x)
    x = th.remove_special_chars(x)
    x = re.sub("(.)\\1{2,}", "\\1", x)
    return xUse this if you want to use one by oneimport pandas as pd
import numpy as np
import text_hammer as th

df = pd.read_csv('imdb_reviews.txt', sep = '\t', header = None)
df.columns = ['reviews', 'sentiment']

# These are series of preprocessing
df['reviews'] = df['reviews'].apply(lambda x: th.cont_exp(x)) #you're -> you are; i'm -> i am
df['reviews'] = df['reviews'].apply(lambda x: th.remove_emails(x))
df['reviews'] = df['reviews'].apply(lambda x: th.remove_html_tags(x))
df['reviews'] = df['reviews'].apply(lambda x: th.remove_urls(x))

df['reviews'] = df['reviews'].apply(lambda x: th.remove_special_chars(x))
df['reviews'] = df['reviews'].apply(lambda x: th.remove_accented_chars(x))
df['reviews'] = df['reviews'].apply(lambda x: th.make_base(x)) #ran -> run, 
df['reviews'] = df['reviews'].apply(lambda x: th.spelling_correction(x).raw_sentences[0]) #seplling -> spellingNote: Avoid to usemake_baseandspelling_correctionfor very large dataset otherwise it might take hours to process.Extrax = 'lllooooovvveeee youuuu'
x = re.sub("(.)\\1{2,}", "\\1", x)
print(x)
---
love you
Package: text-highlighter
Text HighlighterA Streamlit component for annotating text using text highlighting. Useful for NLP tasks.InstallationYou can install the Text Highlighter package using the following command:pipinstall--upgradetext-highlighterUsageThe package can be used as follows:fromtext_highlighterimporttext_highlighterimportstreamlitasst# Basic usageresult=text_highlighter(text="John Doe is the founder of MyComp Inc. and lives in New York with his wife Jane Doe.",labels=[("PERSON","red"),("ORG","#0000FF")],# Optionally you can specify pre-existing annotations:annotations=[{"start":0,"end":8,"tag":"PERSON"},{"start":27,"end":38,"tag":"ORG"},{"start":75,"end":83,"tag":"PERSON"},],)# Show the results (in XML format)st.write(result.to_xml())# Show the results (as a list)st.write(result)In theexample.pyscript you can find the above example. You can run the example as follows:streamlitrunexample.pyThe output will look like this:ContributeFeel free to add a pull request or open an issue if you have any questions or suggestions.
Package: text_histogram
text_histogramHistograms are great for exploring data, but numpy and matplotlib are heavy and
overkill for quick analysis. They also can’t be easily used on remote servers
over ssh. Don’t even get me started on installing them.Bit.ly’s data_hackshistogram.py is
great but difficult to use from python code directly (it requires anoptparse.OptionParserto pass histogram options). This is histogram.py
repackaged for convenient script use.>>> from text_histogram import histogram
>>> import random
>>> histogram([random.gauss(50, 20) for _ in xrange(100)])
# NumSamples = 100; Min = 1.42; Max = 87.36
# Mean = 51.848095; Variance = 332.055832; SD = 18.222399; Median 53.239251
# each ∎ represents a count of 1
    1.4221 -    10.0159 [     3]: ∎∎∎
   10.0159 -    18.6098 [     3]: ∎∎∎
   18.6098 -    27.2036 [     6]: ∎∎∎∎∎∎
   27.2036 -    35.7974 [     4]: ∎∎∎∎
   35.7974 -    44.3913 [    17]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
   44.3913 -    52.9851 [    16]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
   52.9851 -    61.5789 [    17]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
   61.5789 -    70.1728 [    20]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
   70.1728 -    78.7666 [     8]: ∎∎∎∎∎∎∎∎
   78.7666 -    87.3604 [     6]: ∎∎∎∎∎∎Installation$pipinstalltext_histogramSource:https://github.com/Kobold/text_histogram
Package: text-histogram3
text_histogram3Histograms are great for exploring data, but numpy and matplotlib are heavy and
overkill for quick analysis. They also can’t be easily used on remote servers
over ssh. Don’t even get me started on installing them.Bit.ly’s data_hackshistogram.py is
great but difficult to use from python code directly (it requires anoptparse.OptionParserto pass histogram options). This is histogram.py
repackaged for convenient script use.>>> from text_histogram3 import histogram
>>> import random
>>> histogram([random.gauss(50, 20) for _ in range(100)])
# NumSamples = 100; Min = 1.42; Max = 87.36
# Mean = 51.848095; Variance = 332.055832; SD = 18.222399; Median 53.239251
# each ∎ represents a count of 1
    1.4221 -    10.0159 [     3]: ∎∎∎
   10.0159 -    18.6098 [     3]: ∎∎∎
   18.6098 -    27.2036 [     6]: ∎∎∎∎∎∎
   27.2036 -    35.7974 [     4]: ∎∎∎∎
   35.7974 -    44.3913 [    17]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
   44.3913 -    52.9851 [    16]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
   52.9851 -    61.5789 [    17]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
   61.5789 -    70.1728 [    20]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
   70.1728 -    78.7666 [     8]: ∎∎∎∎∎∎∎∎
   78.7666 -    87.3604 [     6]: ∎∎∎∎∎∎Installation$pipinstalltext_histogram3Source:https://github.com/basnijholt/text_histogram3
Package: texthub
text-hub通用文字自動標注與預測訓練系統目標整理文字標注與預測訓練的流程，並提供一個通用的系統，讓使用者可以快速的標注與訓練文字預測模型。包含組件資料預處理分段分句過濾輸出方便標注的格式資料訓練輸入標注好的格式訓練模型資料預測輸入要標注的格式選擇對應的模型預測輸出結果使用方式安裝# 建立虛擬環境condacreate-ntext-hubpython=3.8
condaactivatetext-hub# 安裝套件pipinstall-rrequirements.txt使用streamlitrunhome.py
Package: text-ide-Zuap
## Linguistic IDE![screenshot](http://www.paulojorgepm.net/static/images/print_linguistic_ide.png)Text IDE for linguistics purposes. Objective: to create a text editor software, but focusing only in the linguistics (academic aspect), not visual aspects like the traditional ones, analyzing in real time linguistics aspects like grammar, morphology, corpora techniques, statistics, automatic translation, syntax, semantics, etc. Ideally it will have a parallel website, where academics/public can update the database and contribute with updates or new plugins. In the future I plan to migrate sobre libraries to Rust language and integrate part in Rust (faster).The core package gives acess to NLP tools that can be used without the GUI aspect, just import the one you need and get data using their functions, e.g. for translating text from english to portuguese:` from core.translation import Translation translation =Translation(lang='pt')translated =translation.translate('Exampletext') `—## Want to run this?You’ll need: Python 3 installed; for GUI kivy is used. The file “main.py” starts the application. The following libraries will be needed:` kivy (see their homepage for instalation and dependencies instructions) pip install nltk pip install pickle pip installmarisa-triepip install googletrans pip install pyspellchecker pip install nlpnet pip3 install bllipparser pip install-Utextblob `—## Contact me:[http://www.paulojorgepm.net](http://www.paulojorgepm.net)
Package: textify
textifyA Simple Text Cleaning and Normalization Package For NLPInstallationpipinstalltextifyUsageClean TextClean text by removing emails,numbers,etc>>>fromtextifyimportTextCleaner>>>docx=TextCleaner()>>>docx.text="your text goes here">>>docx.clean_text()Remove Emails,Numbers,Phone Numbers>>>docx.remove_emails()>>>docx.remove_numbers()>>>docx.remove_phone_numbers()Remove Special Characters>>>docx.remove_special_characters()Replace Emails,Numbers,Phone Numbers>>>docx.replace_emails()>>>docx.replace_numbers()>>>docx.replace_phone_numbers()Using TextExtractorTo Extract emails,phone numbers,numbers from text>>>fromtextifyimportTextExtractor>>>docx=TextExtractor()>>>docx.text="your text with example@gmail.com goes here">>>docx.extract_emails()ByJesse E.Agbe(JCharis)Jesus Saves @JCharisTechNBContributions Are WelcomedNotice a bug, please let us know.Thanks A lot
Package: textile2rst
VERSIONDOWNLOADSTESTSCOVERAGESmall utility to help moving from Jekyll/Octopress Textile file to standard ReStructured Text.Installationpipinstalltextile2rstUsagepython-mtextile2rstfilename.textileWhat does it do?Replaces<!--more-->by.. moreReplaces<br/>by----Replaces section titlesReplaces Jekyll/Octopress code style with directivehighlightinto RST format with.. code::Replaces inline links by RST links, gathering them all at the end.What doesn’t it do?If you used underscores to set bold or italic, it won’t translate it and RST parsers will fail.It doesn’t change the metadata in YAMLChange the internal references with Jekyll directivepost_urlManage any other directive.
Package: textimage
Description
Write text on image with text wrap.Libraries used
from PIL import Image
from PIL import ImageFont
from PIL import ImageDraw
import textwrap
import random
import stringUsage
from textimage import textimage
writetext(‘text’, ‘font_directory’, ‘font_size’, ‘image_directory’, ‘width’, ‘height’, ‘output_directory’)Change Log1.0.0 (28/12/2020)First Release1.0.1 (28/12/2020)Fixed required ‘install_requires’ in ‘setup.py’1.0.2 (28/12/2020)Bugfixes
Package: text-image-generator
一些碎碎唸2022 年 7 月 19 日信標委正式發佈了 GB 18030-2022 《信息技術 中文編碼字符集》。該標準將於 2023 年 8 月 1 日開始正式實施。在可以預見的未來，在公共服務中處理生僻字將不再是障礙，對生僻字 OCR 文字識別的需求也將不断增加。因此，小吳想要針對目前已經編碼的中日韓統一表意文字訓練一個 OCR 模型，方便自己日後使用。由於收集眞實文本圖像作爲訓練集需要人工標註，工作量較大，所以通行的手段是基於多種字體生成文本圖像進行訓練。可惜目前開源的合成文本工具都沒有考慮到對擴展區漢字的支援，且合成图像的速度也不盡如人意，我就索性用 Rust 自己寫一個文本合成工具了。（現在 Rust 的文本整形 crates 都已經很成熟了，讚一個）這個工具可以和 Pytorch 的 Dataset 組合起來，做到在訓練時實時生成訓練集，避免了訓練集在服務器間傳輸的不便。安裝這個工具已發布至 PyPIpip install text-image-generator編譯這個工具的編譯需要安裝 Rust，若尚未安裝，請運行：curl --proto '=https' --tlsv1.2 https://sh.rustup.rs -sSf | sh然後透過 pip 安裝 maturin 用於構建 pyo3：pip install maturin將本工具的 git 倉庫下載到本地：git clone https://github.com/wcshds/text-image-generator.git
cd text-image-generator生成 wheel：maturin build --release在生成 whl 文件後，透過 pip 命令安裝即可。pip install text_image_generator-0.1.0-cp310-cp310-manylinux_2_34_x86_64.whl使用前的準備事先需要找到足夠多的字體文件放到目錄中，這些字體文件需要覆蓋想要生成的所有字符。字體主目錄下可以有子目錄，工具會遞歸查找指定的字體主目錄下所有字體文件。注意：暫時不支援可變字體。字體文件可以在這裡下載。準備一個文本文件，其中包含想要生成的所有字符的全集及其對應的字頻，字符與字頻之間應用製表符分割（字頻可省略），具體格式如下：〇	1
一	1
乙	1
二	1
十	1
丁	1
厂	1準備一個文本文件，其中包含 fallback 字體的名稱，格式如下：SimSun
TW-Sung
KaiTi使用示例importcv2fromtext_image_generatorimportGeneratorgen=Generator(config_path="./config.yaml",)text="𢞁𢞂𢞃𢞄𢞅"text_with_font_list=gen.wrap_text_with_font_list(text)img=gen.gen_image_from_text_with_font_list(text_with_font_list,text_color=(255,105,105),background_color=(166,208,221),apply_effect=False,)cv2.cvtColor(img,cv2.COLOR_RGB2BGR,dst=img)cv2.imwrite("test.png",img)生成圖片如下：
Package: textime
Convert plain text to datetimeCurrently only supporting English.Python library that converts a date written as plain text to a datetime object.How to use it :computer:First you install it in you environment like thispipinstalltextimeThen you can start using it already! Pass any string to thetextparameter containing a date in the format: "DAYofMONTHofYEAR" whereYEARmust be written out completely, like "first of May of two thousand and twenty one" or "second of August of one thousand nine hundred and ninety nine".fromdatetimeimportdatetimefromtextimeimporttextimetext_to_dt:datetime=textime.to_datetime(text="Fifteenth of July of two thousand and eight")Contribution :pencil:This project is open source. Contributions are welcome and appreciated. Please check ourguide for contributingand ourcode of conductas well.
Package: text-indexer
# text-indexer[![Build Status](https://travis-ci.org/Yoctol/text-indexer.svg?branch=master)](https://travis-ci.org/Yoctol/text-indexer)[![PyPI version](https://badge.fury.io/py/text-indexer.svg)](https://badge.fury.io/py/text-indexer)Indexer transforms list of strings to list of integers according to the string2int mapping function(e.q. a look-up table).## Getting Started### Prerequisites1. Please create a virtual environment with python 3.62. Type the following command in terminal:```make install```### Running the tests```make test```## How to completely save and load indexer?### 1. Save```pythonfrom text_indexer.io import save_indexercustom_indexer_instance = XXIndexer()save_indexer(indexer=custom_indexer_instance, output_dir='directory-to-export-indexer')```If output_dir is `/home/user/example/`, you will get a tar file  `/home/user/example-all.tar.gz` after calling`save_indexer`.### 2. Load```pythonfrom text_indexer.io import load_indexeryour_indexer = load_indexer(path='indexer-tar-filepath')```
Package: texting
textingmisc for stringFunctionshas_ansilangepad: lpad, mpad, rpadstr_valueUsagefromtexting.has_ansiimporthas_ansifromtexting.langeimportlangewords=['peace','\u001B[4mwar\u001B[0m','\u001b[38;2;255;255;85mtolstoy\u001b[0m',]forwordinwords:print(f'[{word}] [has_ansi] ({has_ansi(word)}) [lange] ({lange(word)})')
Package: textinput
Typical use is:import textinputfor line in textinput.lines():process(line)This iterates over the lines of all files listed in sys.argv[1:],defaulting to sys.stdin if the list is empty.  If a filename is '-' itis also replaced by sys.stdin.  To specify an alternative list offilenames, pass it as the argument to input().  A single file name isalso allowed.
Package: textio
About Module
Hey This Is A Module For Python It Makes Easy To Do Some Animated Text first do:import textiotextio has an alphs variable which has a list of alphabets to import use:textio.alphsand textio also has arenderText()function which is different fromprint()you are thinking what is different fromprint()? because this function has animated text it uses 2 args 1 isstring2 iswritetimeyou can putwritetime0.1 to 0.10 now don't think more aboutwritetimeput any number from this. then second function isdeleteText()which will delete therenderText()you have written before it takes 1 argwaitwhich is how many seconds after it should delete the text thats the brief explaination of TextIO.