Saravanan666/pypi-org huggingface dataset:
Package: wikiext
📂 WikiExtractor 💡Dumping hole wiki content,which can :clean unused symbol,mark,labelextract knowledge - synonym,concept,relationship這是一個 wiki 的預處理工具，可以:清理 wiki 中沒有用的内容：標簽，符號...提取出一些有用的知識：同義詞，關係，翻譯Installationpip install wikiextUsagewikiext -h
usage: wikiext [-h] [--lang LANG]
               [--dump {entity, redirect_pair,langlink,category,articles,all} [{redirect_pair,langlink,category,articles,all} ...]]
               [--savedir SAVEDIR] [--type {csv,dict}] [--s2t]

optional arguments:
  -h, --help            show this help message and exit
  --lang LANG           default:zhwiki, from
                        https://dumps.wikimedia.org/backup-index-bydb.html
  --dump {entity, redirect_pair,langlink,category,articles,all} [{redirect_pair,langlink,category,articles,all} ...]
                        select what to extract
  --savedir SAVEDIR     save dir, default /dump_result
  --type {csv,dict}exmaple:wikiext--langzh_yuewiki--dumparticles--savedirwiki--typecsvresult filetitle, article
......FunctioninitfromwikiimportWikiExtwiki=WikiExt(language_source="zh_yuewiki",s2t=False)Argumentslanguage_source(String): example:"zhwiki",all of the code can find it
onhttps://dumps.wikimedia.org/backup-index-bydb.htmls2t(Boolean): translate all text to traditional or notdump_articles(outfile, type="csv")Argumentsoutfile(String): name of output filetype(String): csv or text Resultcsv :
數學,"歐幾裏得，西元前三世紀的古希臘數學家，現在被認為是幾何之父，此畫為拉斐爾的作品《雅典學院》。
數學是利用符號語言研究數量、結構、變化以及空間等概念的一門學科，從某種角度看屬於形式科學的一種。數學透過抽象化和邏輯推理的使用，由計數、計算、量度和對物體形狀及運動的觀察而產生。數學家們拓展這些概念，為了公式化新的猜想以及從選定的公理及定義中建立起嚴謹推導出的定理。
......
text :
數學
歐幾裏得，西元前三世紀的古希臘數學家，現在被認為是幾何之父，此畫為拉斐爾的作品《雅典學院》。
數學是利用符號語言研究數量、結構、變化以及空間等概念的一門學科，從某種角度看屬於形式科學的一種。數學透過抽象化和邏輯推理的使用，由計數、計算、量度和對物體形狀及運動的觀察而產生。數學家們拓展這些概念，為了公式化新的猜想以及從選定的公理及定義中建立起嚴謹推導出的定理。dump_redirect_pair(outfile, type)get all redirect pair Argumentsoutfile(String): name of output filetype(String): csv or dict Resultcsv:
origin.redirect to
鋼の錬金術師,鋼之鍊金術師
香港仔海旁道,香港仔海傍道
飛機外部燈光,航行燈
螢幕八爪娛,熒幕八爪娛
司农卿,大司農
大司农卿,大司農
司農,大司農
司农,大司農
Earth 2160,地球2160
图勒凯尔姆,图勒凯尔姆省
盖勒吉利耶,盖勒吉利耶省
......
dict
鋼の錬金術師
鋼之鍊金術師
香港仔海旁道
香港仔海傍道
飛機外部燈光
航行燈
螢幕八爪娛
熒幕八爪娛
司农卿
大司農
大司农卿
大司農
司農
大司農
司农
大司農
Earth 2160
地球2160
图勒凯尔姆
图勒凯尔姆省
盖勒吉利耶
盖勒吉利耶省dump_entity(outfile, type):Argumentsoutfile(String): name of output filetype(String): csv or dictdump_langlink(outfile, type):Argumentsoutfile(String): name of output filetype(String): csv or dictdump_category(outfile, type="csv"):use this to extract specific categories noun Argumentsoutfile(String): name of output filetype(String): csv or dict
Package: wikifaces
Wiki Faces:Figure 1: Joko Widodo's Wikipedia page, which includes am image of his face. The cropped image on the right is download into a directory named "Joko_Widodo."TLDRThis project downloads images from a Wiki that include human faces. Specifically, images that are associated with
certain wikipedia categories.InstallationPip Installation Procedure:From PIP:pip install wikifacesFrom Repo:git clone git@github.com:tford9/Wiki-Faces-Downloader.git
cd Wiki-Faces-Downloader
python setup.py
pip install wikifacesUsageCommand-Line Examplepython downloader -i "indonesian engineers" -o ../data/ -dPackage Examplefromwikifaces.downloaderimportWikiFacewikiface_obj=WikiFace()wikiface_obj.download(categories=['facebook'],depth=2,output_location='../data/')The following structure is output:facebookcached_1_people_pages_d2.pklcached_pages_d2.pklalan_rushbridgerAlan_Rusbridger_01.jpg-p0.jpg...mark_zuckerbergMarkZuckerbergcrop.jpg-p1.jpg...The process is carried out as follows:Given a category from a Wiki, collectnpages that contain the same category as well as at least one category
containing "people" in the title.With those pages, crawl across their included categories and collectypages that contain those categories as well
as at least one "people" category.Given the collected Wiki pages, download the primary image from the page and determine if it is a human face using
light facial detection.We capture all images from the wiki that contain the name of the page (if it's a person then the filename contains
their name),Using the captured name and images, we create a dataset for that face.TODOs:Currently, a part of this process uses a recursive call structure to get all related pages; there may be a way to
linearize, or parallelize this.Currently, we are only pulling images contain the person's name in the title and only have one visible face in the
image. All other images are not considered. A voting system should be added to get the most represented faces across
multiple images.
Package: wikifeet
WikiFeetWikiFeet (The collaborative celebrity feet website) crawler.ExamplefromwikifeetimportWikiFeetfeet=WikiFeet("Karla Souza")print("Karla Souza feet pic: "+feet.image())print("Karla Souza shoe size: "+feet.shoe_size())print("Karla Souza feet rating: "+feet.rating())# Output:# Karla Souza feet pic: https://pics.wikifeet.com/Karla-Souza-Feet-6104875.jpg# Karla Souza shoe size: 6.5 US# Karla Souza feet rating: beautifulDocumentationpdoc
Package: wiki-fetch
wiki-fetchInstallationPyPIpipinstallwiki-fetchSourcegitclonegit@github.com:d3z-the-dev/wiki-fetch.gitcdwiki-fetch&&poetrybuild
pipinstall./dist/*.whlUsageCLIOptions for use in consoleOptionFlagLongDefaultExampleWiki's page link-u--urlNonehttps://en.wikipedia.org/wiki/The_DoorsSearch query-q--queryNoneThe Doors (band)Page language-l--langEnglishEnglishPart of the page-p--partallinfoboxParts by order-i--itemallfirstOutput format-o--outputtexttextwiki-fetch-q'The Doors (band)'-pinfobox-ifirstoutputInfobox:The Doors:The Doors:Image:https://upload.wikimedia.org/wikipedia/commons/thumb/6/69/The_Doors_1968.JPG/250px-The_Doors_1968.JPGCaption: The Doors in 1966:Morrison (left), Densmore (centre), Krieger (right) and Manzarek (seated)Background information:Origin:Los Angeles, California, U.S.Genres:Psychedelic RockBlues RockAcid RockYears active:1965-19731978Labels:ElektraRhinoSpinoffs:The Psychedelic RangersButts BandNite CityManzarek-KriegerSpinoff of:Rick & the RavensPast members:Jim MorrisonRay ManzarekRobby KriegerJohn DensmoreWebsite:thedoors.comURL:https://en.wikipedia.org/?search=The Doors (Band)PythonArguments of function and classArgumentValuesDescriptionurlstrAny Wiki's page URLquerystrAny query stringlangstrAny of available languagespartinfobox,paragraph,table,list,thumb,toc,allSpecify page partitemfirst,last,allSpecify the order of the partfromwiki_fetch.driverimportWikioutput=Wiki(lang='English').search(query='The Doors (band)',part='infobox',item='first')print(output.json)output{"Infobox":[{"The Doors":{"The Doors":{"Image":"https://upload.wikimedia.org/wikipedia/commons/thumb/6/69/The_Doors_1968.JPG/250px-The_Doors_1968.JPG","Caption":"The Doors in 1966: Morrison (left), Densmore (centre), Krieger (right) and Manzarek (seated)"},"Background information":{"Origin":"Los Angeles, California, U.S.","Genres":["Psychedelic Rock","Blues Rock","Acid Rock"],"Years active":["1965-1973","1978"],"Labels":["Elektra","Rhino"],"Spinoffs":["The Psychedelic Rangers","Butts Band","Nite City","Manzarek-Krieger"],"Spinoff of":"Rick & the Ravens","Past members":["Jim Morrison","Ray Manzarek","Robby Krieger","John Densmore"],"Website":"thedoors.com"}}}],"URL":"https://en.wikipedia.org/?search=The Doors (Band)"}SpecificationAvailable optionsParts of pageOutput formatsLanguageinfoboxtextEnglishparagraphjsonUkrainiantabledictRussianlistPolishthumbGermantocNederlandsSwedishSpanishFrenchItalianJapaneseChaineseCebuano
Package: wikiframe
WikiFrameConvert all csv files in a folder to a diccionary of dataframe and more!.Explore the docs Â»Table of ContentsAbout The ProjectInstallationUsageContributingLicenseAbout The ProjectWikiFrame scan all the .csv files in a folder and convert into a diccionary of dataframe, This package accept different csv delimiter and encodings. Also if you want to add transform dataframe-functions, you can add it!.(back to top)InstallationInstall fromPyPIpipinstallwikiframe(back to top)UsageExtractor:This function will extract all the csv files in the folder and convert them to a dictionary of dataframe.Parameters:
    -----------

    sep : Especifies the delimiter of the csv file. (default [',',';', ':', '|', '\t'])
    encoding : Especifies the encoding of the csv file. (if not especified, it will be detected automatically)
    func : A list of functions that will be applied to the dataframe. (e.g. [func1, func2, func3])
    func_custom : A dictionary of functions that will be applied to the especific dataframe. (e.g. {'dataframe_1':[func1,func2], 'dataframe2':func3})
    verbose : If True, will print the name of the dataframe that is transformed. (default False)

    Returns:
    --------
    data_dict : A dictionary of dataframe.

    Notes:
    ------
    The order of the dataframe in the dictionary is the same as the order of the csv files in the folder.
    func could be a list of functions or a single function.Example:fromwiki_toolsimportExtractor# Import the Extractor functionextractor=Extractor(path='path/to/folder')# path to folder with csv filesdata_dictonary=extractor.extract_from_csv()# extract all csv files on a folder and convert into a dictionary of dataframeSay:A cow helper function to say something
Parameters:
-----------
something:  A string to say in consoleExample:fromwiki_tools.Sayimportcow_says_good,cow_says_good# Import the Say functioncow_says_good('Hola')# Say something in cow speakcow_says_error('Hola) # Say something in cow speakOutput:< Hello World >
 -------------
        \   ^__^
         \  (oo)\_______
            (__)\ goodðŸ™ˆ )\/\
                ||----w |
                ||     ||
 _____________
< Hello World >
 -------------
        \   ^__^
         \  (oo)\_______
            (__)\ goodðŸ™ˆ )\/\
                ||----w |
                ||     ||(back to top)ContributingContributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make aregreatly appreciated.If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull Request(back to top)LicenseDistributed under the MIT License. SeeLICENSE.txtfor more information.(back to top)
Package: wikiglot
wikiglotOverviewwikiglotparses entries in Wiktionary pages. It is designed to be simple and flexible.Installingwikiglotcan be installed from source via pip:pipinstallgit+https://github.com/jolars/wikiglotUsageUsingwikiglotis simple. Here's an example of how to parse the entry from the Swedish Wiktionary page for "katt" (cat):fromwikiglotimportWiktionaryparser=Wiktionary("sv")parser.lookup("katt")You can also parse entries for words in other languages that are available on the Swedish Wiktionary page:parser.lookup("funicular","en")Limitationswikiglotparses Wiktionary pages one-by-one, which can be slow if you need to look up a large number of words. If that is the case, you might prefer a solution that relies on a downloaded version of Wiktionary.ContributingWhen writing commit messages, please use theconventional commits format.Versioningwikiglot usessemantic versioning.
Package: wikihappy
Wikipedia with sectionsis a Python library that makes it easy to access and parse
data from Wikipedia.This is a fork from Jonathan Goldsmith’sWikipedia package>>>importwikipedia>>>ny=wikipedia.page("New York")>>>ny.sections[:5]# ['Etymology', 'History', 'Early history', 'Dutch rule', 'English rule']InstallationTo install Wikipedia, simply run:$ pip install wikipedia_sectionsWikipedia is compatible with Python 2.6+ (2.7+ to run unittest discover) and Python 3.3+.DocumentationSeehttps://github.com/goldsmith/Wikipediafor full dcumentation.LicenseMIT licensed. See theLICENSE
filefor
full details.CreditsWikipediaby Jonathan Goldsmith for the original repositorywiki-apiby
@richardasaurus for inspiration@nmoroze and @themichaelyang for feedback and suggestionsTheWikimedia
Foundationfor giving
the world free access to data
Package: wikihow2zim
wikiHowwikihow2zimis anOpenZIMscraper to create offline versions ofwikiHowwebsites, in all its supported languages.:zap:        Scraper is known to have a very significant issue linked to throttling (https://github.com/openzim/wikihow/issues/150)Usagewikihow2zimworks off alanguage versionthat you must provide via the--languageargument. The list of supported languages is visible in the--helpmessage.Dockerdockerrun-vmy_dir:/outputghcr.io/openzim/wikihowwikihow2zim--helpPythonwikihow2zimis a Python3 (3.6+) software. If you are not using theDockerimage, you are advised to use it in a virtual environment to avoid installing software dependencies on your system.python3-mvenvenvsourceenv/bin/activate# using published versionpip3installwikihow2zim
wikihow2zim--help# running from sourcepythonwikihow2zim/--helpCalldeactivateto quit the virtual environment.Seerequirements.txtfor the list of python dependencies.ContributingAll contributions are welcome!Please open an issue on Github and/or submit a Pull-request.GuidelinesDon't take assigned issues. Comment if those get staled.If your contribution is far from trivial, open an issue to discuss it first.Ensure your code passedblack formatting,isortandflake8(88 chars)We have apre-commithook ready for you. Install it withpip install pre-commit && pre-commit install
Package: wikihowunofficialapi
wikiHowUnofficialAPIwikiHowUnofficilAPI aims to provide a Python-based interface to extract useful information from wikiHow articles such as article summary, steps, images, etc. in an easy-to-use way.InstallationUsageRandom HowToArticle DetailsImagesSearchInstallationpipinstallwikiHowUnofficialAPIUsageRandom HowToLearn random stuff! Retuns a random WikiHow article URL.importwikihowunofficialapiaswhara=wha.random_article()print(ra)Article DetailsUses the article URL to return various details about an article. In addition, it returns whether an article is written by an expert or not.importwikihowunofficialapiaswhaarticle=wha.Article('https://www.wikihow.com/Train-a-Dog')print(article.url)# Print Article's URLprint(article.title)# Print Article's Titleprint(article.intro)# Print Article's Introductionprint(article.n_methods)# Print number of methods in an Articleprint(article.methods)# Print a list of methods in an Articleprint(article.num_votes)# Print number of votes given to an Articleprint(article.percent_helpful)# Print percentage of helpful votes given to an Articleprint(article.is_expert)# Print True if the Article is written by an expertprint(article.last_updated)# Print date when the Article was last updatedprint(article.views)# Print the number of views recieved by Articleprint(article.co_authors)# Print the number of co-authors of an Articleprint(article.references)# Print the number of references in an Articleprint(article.summary)# Print Article's summaryprint(article.warnings)# Print Article's warningsprint(article.tips)# Print Article's tipsfirst_method=article.methods[0]first_step=first_method.steps[0]print(first_step)# Print Article's first step of the first methodprint(first_step.title)# Print the title of Article's first step of the first methodprint(first_step.description)# Print the description of Article's first step of the first methodImagesRetrieves a list of image included in a step as URLs.importwikihowunofficialapiaswhaarticle=wha.Article('https://www.wikihow.com/Train-a-Dog')print(article.methods[0].steps[0].picture)# Print the URL of the image of Article's first step of the first methodSearchSearches WikiHow for the string and returns a list containing the title of the articles. The default max results is 10, but this can be changed.importwikihowunofficialapiaswhamax_results=1how_tos=wha.search_wikihow("sleep",max_results)print(how_tos[0])
Package: wikijs
Wiki.Js Python client ( ⚠ WIP ⚠ )ExamplesMove all pages in a folder to a new folderfromwikijsimportWikiJsclient=WikiJs(<endpoint>,<api_key>)OLD_FOLDER='OldFolder/'NEW_FOLDER='NewFolder/'forpageinclient.search_pages('',OLD_FOLDER):new_path=page['path'].replace(OLD_FOLDER,NEW_FOLDER,1)client.move_page(page['id'],new_path,page['locale'])
Package: wikijs-pyclient
Wikijs PyClientA python client for an easy interaction with the WikiJS graphql API, through automating the graphql queries.DocumentationSource CodePypi PackageBug ReportsInstallationpython 3.10+pip install wikijs-pyclientWikiJS client setupfromwikijsimportWikiJSwiki=WikiJS(url="<protocol>://<wiki_host>:<wiki_port>",token="<wiki_api_key>")
Package: wikilabels
# Wiki LabelsThis package provides the components of a generalized labeling service forMediaWiki.  There are two primary components, a user script to be used onMediaWiki and a flask server for the gadget to converse with.## ServerThe flask server is intended to be hosted by a web server, but a dev server canbe started locally withwikilabels dev_server### Installation#### DependenciesInstallation will require some additional packages to be available.`sudo apt-get install postgresql-server-dev-all postgresql libffi-dev npm g++ python3-dev libmemcached-dev`#### Database setupYou'll need to create a `wikilabels` user and database. Here's a sequence ofcommands that works on a fresh install of postgres (note that `sudo` rightswill be required).Create a wikilabels user$ sudo useradd wikilabelsSwitch to `postgres` user to run commands$ sudo su postgres$ psqlpostgres=# CREATE USER wikilabels WITH PASSWORD 'something secure';postgres=# CREATE DATABASE wikilabels;postgres=# GRANT ALL PRIVILEGES ON DATABASE wikilabels to wikilabels;postgres=# \q$ exitSwitch to `wikilabels` user to load schema$ sudo su wikilabels$ psqlCopy-paste config/schema.sql into the command prompt.Optionally, you can also load sample data into the database by copy-pasting config/schema-testdata.sql into the command prompt### Starting the dev server$ wikilabels dev_server --config config-localdev.yaml## Gadget1. Go to one of the js subpages of your user page. You can choose a page such as these:* [meta:User:`<Name>`/global.js](https://meta.wikimedia.org/wiki/Special:MyPage/global.js), which will be loaded in all wikis, in all skins* [meta:User:`<Name>`/common.js](https://meta.wikimedia.org/wiki/Special:MyPage/common.js), which will be loaded only on Meta-wiki, in all skins* [meta:User:`<Name>`/vector.js](https://meta.wikimedia.org/wiki/Special:MyPage/vector.js), which will be loaded only on Meta-wiki, in the vector skin2. Copy the following to the page you have chosen:```javascript// [[File:User:EpochFail/WikiLabels.js]] (workaround for [[phab:T35355]])mw.loader.load( '//labels.wmflabs.org/gadget/loader.js' );```3. Clear the cache of your browser.This will import a live copy of the javascript.## See also* [meta:Wiki labels](https://meta.wikimedia.org/wiki/Wiki labels)
Package: wikilink
wikilink is a multiprocessing web-scraping application to scrape the wiki pages, extract urls and find the minimum number of links between 2 given wiki pages.I discussed brief the motivation and an overview of the project inmy blog.The project is currently at versionv0.3.0.post1, also seechange logfor more details on release history.If you like this project, feel fee to leave a few words of appreciation hereBuildQualitySupportPlatformTable of contentsUsageContributionLicenseUsageInstall with pip$ pip install wikilinkDatabase supportwikilink currently supportsMysqlandPostgreSQLAPIsetup_db(db, username, password, ip="127.0.0.1", port=3306): set up database

Args:
	db(str): Database engine, currently support "mysql" and "postgresql"
	name(str): database username
	password(str): database password
	ip(str): IP address of database (Default = "127.0.0.1")
	port(str): port that databse is running on (default=3306)

Returns:
	Nonemin_link(source, destination, limit=6, multiprocessing=False): find minimum number of link from source url to destination url within limit 

Args:
	source(str): source wiki url, i.e. "https://en.wikipedia.org/wiki/Cristiano_Ronaldo"
	destination(str): Destination wiki url, i.e. "https://en.wikipedia.org/wiki/Cristiano_Ronaldo"
	limit(int): max number of links from the source that will be considered (default=6)
	multiprocessing(boolean): enable/disable multiprocessing mode (default=False)

Returns:
	(int) minimum number of sepration between source and destination urls
	return None and print messages if exceeding limits or no path found

Raises:
	DisconnectionError: error connecting to DBExamples>>> from wikilink import WikiLink
>>> app = WikiLink()
>>> app.setup_db("mysql", "root", "12345", "127.0.0.1", "3306")
>>> source = "https://en.wikipedia.org/wiki/Cristiano_Ronaldo"
>>> destination = "https://en.wikipedia.org/wiki/Lionel_Messi"
>>> app.min_link(source, destination, 6)
1ContributionHow to contributePlease follow our contribution convention atcontribution instructionsandcode of conduct.To set up development environment, simply run:$ pip install -r requirements.txtPlease check out theissue filefor list of issues that required helps.AppreciationFeel free to add your name into thelist of contributors. You will automatically be inducted into Hall of Fame as a way to show my appreciation for your contributions.Hall of FameLicenseSee theLICENSEfile for license rights and limitations (Apache License 2.0).
Package: wikimedia-cli
wikimedia-cliA minimally dependent Wikimedia CLI written in PythonIn early developementCurrently supported Wikimedia projectsWikipedia- The free encyclopediaWiktionary- The free dictionaryDocumentationDocumentation is available onReadTheDocsInstallationTo build the documentation locally, make sure you are in the root project directory and run:pipinstallSphinxfuromyst-parsercddocs
makehtmlDocumentation will be built in thedocs/_build/htmldirectoryIdeas and TODOWiktionary and Wikipedia article formatting (e.g. bold, italics, colors, links, images, etc.)Wiktionary ImprovementsSummaryBreakdown of phrase properties (e.g. definition, usage, synonyms, entomology, etc.)Other wikimedia wikisWikimedia commonsWikidataWikinewsWikibooksRevision historyMain page and ITN/OngoingPackagingAUR
Package: wiki-music
Wikipegia music tags parserPython application which parses wikipedia for music tags and writes them to
files. Has also the ability to search for lyrics and album cover art. The app
was created to complement tagging tools that use other datatabases.
I often found that these databases provide only some basic information. For
instance there are no lyrics or composers information, or the cover art is in
low resolution. The Wikipedia is often the most complete source of information
but extracting the information by hand is a slow and boring process.wiki_music aims to automate this, paired with a powerful image and lyrics
search, tagging often takes only a few clicks. It is not perfect however.
Wikipedia data is not saved in standardized form so it has to be extracted from
html which can at times be unreliable. But the app should always give you at
least some good starting point from which you can continuee editing by hand.Getting StartedPackage can be installed with pip:pip install wiki_musicFor more details refer to Documentation which can be found at:readthedocs.Installation intructionsUsage intructionsContributing guideAnyone is welcome to use it or contribute. All of the dependencies are fairly
common so you shouldn't encounter any problems. Curentlly supported versions of
python are3.6-3.8.Bugs & FeaturesIf there are some features missing or you found a bug please create an issue
at:Git IssuesPrerequisitesappdirs>=1.4.3
beautifulsoup4>=4.6.0
colorama>=0.4.0
datefinder>=0.6.1
fuzzywuzzy>=0.17.0
lazy-import>=0.2.2
lxml>=4.2.1
nltk>=3.3
mutagen>=1.42.0
numpy>=1.14.3
Pillow>=6.1.0
pyyaml>=5.1.2
PyQt5>=5.11.3
python-Levenshtein>=0.12.0
QtPy>=1.7.0
requests>=2.18.4
wikipedia>=1.4.0AuthorsMariĂˇn Rynik-Initial work-marian-codeSee also the list ofcontributorswho participated in this project.LicenseMITAcknowledgmentsStackoverflowGiesela Inc.- LyricsFinderhttps://github.com/hardikvasa- google_images_download