Saravanan666/pypi-org huggingface dataset:
Package: questionary
Questionary✨ Questionary is a Python library for effortlessly building pretty command line interfaces ✨FeaturesInstallationUsageDocumentationSupportimportquestionaryquestionary.text("What's your first name").ask()questionary.password("What's your secret?").ask()questionary.confirm("Are you amazed?").ask()questionary.select("What do you want to do?",choices=["Order a pizza","Make a reservation","Ask for opening hours"],).ask()questionary.rawselect("What do you want to do?",choices=["Order a pizza","Make a reservation","Ask for opening hours"],).ask()questionary.checkbox("Select toppings",choices=["foo","bar","bazz"]).ask()questionary.path("Path to the projects version file").ask()Used and supported byFeaturesQuestionary supports the following input prompts:TextPasswordFile PathConfirmationSelectRaw selectCheckboxAutocompleteThere is also a helper toprint formatted textfor when you want to spice up your printed messages a bit.InstallationUse the package managerpipto install Questionary:$pipinstallquestionary
✨🎂✨Usageimportquestionaryquestionary.select("What do you want to do?",choices=['Order a pizza','Make a reservation','Ask for opening hours']).ask()# returns value of selectionThat's all it takes to create a prompt! Have alook at the documentationfor some more examples.DocumentationDocumentation for Questionary is availablehere.SupportPleaseopen an issuewith enough information for us to reproduce your problem.
Aminimal, reproducible examplewould be very helpful.ContributingContributions are very much welcomed and appreciated. Head over to the documentation onhow to contribute.Authors and AcknowledgmentQuestionary is written and maintained by Tom Bocklisch and Kian Cross.It is based on the great work byOyetoke TobyandMark Fink.LicenseLicensed under theMIT License. Copyright 2021 Tom Bocklisch.
Package: questioner
QuestionerA human-friendly Python library for asking people questions on the command-line.MotivationData often needs a human eye. I found myself writing over and over the code to help me investigate data manually on the command-line;questioneris my attempt to make this tool the right way once.It’s suitable for:Labelling data manuallyfaster than you can in excel spreadsheets or similar setupsActive learning loopswhere you and a machine-learnining agent collaborate, reducing the need for training dataShort question-based UIson the command-line where you need a user’s input to continueUsagePython APIYou write a simple Python api that asks you things.importquestionerwithquestioner.Cli()asc:is_hurt=c.yes_or_no('Are you hurt')symptom_set=c.choose_many('What symptoms do you have?',['pain','nausea','anxiety'],)rating=c.give_an_int('How would you rate this experience (1-5)',1,5)choice=c.choose_one('Which do you like best',['dogs','cats','horses'])On the terminalWhen run, the experience on the terminal looks like the following:$ python -m questioner.demo
Are you hurt? (y/n) n

What symptoms do you have?
  pain? (y/n) y
  nausea? (y/n) n
  anxiety? (y/n) n

How would you rate this experience (1-5)
3

Which do you like best
  0. dogs
  1. cats
  2. horses
1The user can by default skip a question (raisingQuestionSkipped) by pressing enter, or quit the entire cli by pressingq(raisingQuitCli).FeaturesSupport for boolean, numeric, single-choice and multiple-choice questionsUses single-keystroke input where possibleLicenseMIT licensed.History0.1.0 (2019-02-15)First release on PyPI.
Package: question-framework
Question Framework helps you to ask questions and get answers in a declarative way!Question FrameworkBasic Usagefromquestion_framework.questionimportQuestion,RepeatedQuestion,BranchedQuestionfromquestion_framework.user_inputimportaskquestions=[Question("Name","Your name:")]answers=ask(questions)print(answers)Output:Yourname:
foobar{'Name':'foobar'}Question TypesQuestionQuestionis basically a question with an answer.questions=[Question("Name","Your name:")]answers=ask(questions)print(answers)Output:Yourname:
JohnDoe{'Name':'John Doe'}Repeated QuestionRepeatedQuestioncan be used to ask same question consecutively.questions=[RepeatedQuestion("Password","Your password:",2)]answers=ask(questions)print(answers)Output:Yourpassword:123Yourpassword:321Yourpassword:765{'Password':['123','321','765']}Branched QuestionBranchedQuestioncan be used to create one way adventures.game=[BranchedQuestion("Main","Where to go? [N | E | S | W]",[Question("N","North is cold. You died! (type anything to exit)"),Question("E","You trigerred the trap. (type anything to exit)"),BranchedQuestion("S","You found a tresure chest! [open | leave]",[Question("open","It was a trap! (type anything to exit)"),Question("leave","You leave the cave.. (type anything to exit)"),]),Question("W","West is wild, you died! (type anything to exit)"),])]answers=ask(game)Static Answers"StaticAnswer" can be used to provide a default value.fromquestion_framework.questionimportBranchedQuestion,StaticAnswer,Questionquestions=[BranchedQuestion("password","Do you want to enter a password? [y|n]",[Question("y","What is your password?"),StaticAnswer("n","No password.")])]answers=ask(questions)Output:Doyouwanttoenterapassword?[y|n]n{'password':{'n':'No password.','__answer':'n'}}ValidationsA validation function can be specified to validate answers. If validation fails, user will be asked to enter the input again.Question("Password","Enter password:",validation=lambdax:len(x)>5)Validation Error MessagesWhen a user provides input that does not satify a validation function, it may be desireable to give them a message.  The ValidationError exception allows this.To use, raise the ValidationError exception from your validation function with your desired message.fromquestion_framework.questionimportQuestionfromquestion_framework.user_inputimportaskfromquestion_framework.validationimportValidationErrordefis_not_blank(x):ifnotx:raiseValidationError("Your answer may not be blank.")returnTruequestions=[Question("Name","Your name:",validation=is_not_blank)]answers=ask(questions)Output:Yourname:
Youranswermaynotbeblank.
Yourname:
David{'Name':'David'}Post processA post process can be specified to transform answer.Question("Firstname","Enter firstname:",post_process=lambdax:x.upper())
Package: question-intimacy
Question-IntimacyIntroquestion-intimacy is a package used to estimate the intimacy of questions. It is released with
EMNLP 2020 paperQuantifying Intimacy in Language.InstallUse pipIfpipis installed, question-intimacy could be installed directly from it:pip3 install question-intimacyDependenciespython>=3.6.0
torch>=1.6.0
transformers >= 3.1.0
numpy
math
tqdmUsage and ExampleNotes: During your first usage, the package will download a model file automatically, which is about 500MB.Construct the Predictor Object>>> from question_intimacy.predict_intimacy import IntimacyEstimator
>>> inti = IntimacyEstimator()Cuda is disabled by default, to allow GPU calculation, please use>>> from question_intimacy.predict_intimacy import IntimacyEstimator
>>> inti = IntimacyEstimator(cuda=True)predictpredictis the core method of this package,
which takes a single text of a list of texts, and returns a list of raw values in[-1,1](higher means more intimate, while lower means less).# Predict intimacy for one question
>>> text = 'What is this movie ?''
>>> inti.predict(text,type='list')
-0.2737383

# Predict intimacy for a list of questions (less than a batch)
>>> text = ['What is this movie ?','Why do you hate me ?']
>>> inti.predict(text,type='list')
[-0.2737383, 0.3481976]

# Predict intimacy for a long list of questions
>>> text = [a long list of questions]
>>> inti.predict(text,type='long_list',tqdm=tqdm)
[-0.2737383, 0.3481976, ... ,-0.2737383, 0.3481976]ContactJiaxin Pei (pedropei@umich.edu)
Package: question-score
question-scoreRepository for KDA(Knowledge-dependent Answerability), EMNLP 2022 workHow to usepip install --upgrade pip
pip install question-scorefromquestion_scoreimportKDAkda=KDA()print(kda.kda_small("passage","question",["option1","option2","option3","option4"],1))What does the score mean?You can check the explanation of KDA onEMNLP 2022 papernow.
The official link from EMNLP 2022 will soon be released.You can use $KDA_{small}$ or $KDA_{large}$ instead of heavy metric using all model.
Below is the performance of the submetrics, which mentioned on the appendix of the paper.Sub MetricModel Count ( Total Size )KDA (Valid)Likert (Test)KDA_small4 (3.5GB)0.7400.377KDA_large10 (19.2GB)0.7840.421Citation@inproceedings{moon-etal-2022-evaluating,
    title = "Evaluating the Knowledge Dependency of Questions",
    author = "Moon, Hyeongdon  and
      Yang, Yoonseok  and
      Yu, Hangyeol  and
      Lee, Seunghyun  and
      Jeong, Myeongho  and
      Park, Juneyoung  and
      Shin, Jamin  and
      Kim, Minsam  and
      Choi, Seungtaek",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-main.718",
    pages = "10512--10526",
    abstract = "The automatic generation of Multiple Choice Questions (MCQ) has the potential to reduce the time educators spend on student assessment significantly. However, existing evaluation metrics for MCQ generation, such as BLEU, ROUGE, and METEOR, focus on the n-gram based similarity of the generated MCQ to the gold sample in the dataset and disregard their educational value.They fail to evaluate the MCQ{'}s ability to assess the student{'}s knowledge of the corresponding target fact. To tackle this issue, we propose a novel automatic evaluation metric, coined Knowledge Dependent Answerability (KDA), which measures the MCQ{'}s answerability given knowledge of the target fact. Specifically, we first show how to measure KDA based on student responses from a human survey.Then, we propose two automatic evaluation metrics, KDA{\_}disc and KDA{\_}cont, that approximate KDA by leveraging pre-trained language models to imitate students{'} problem-solving behavior.Through our human studies, we show that KDA{\_}disc and KDA{\_}soft have strong correlations with both (1) KDA and (2) usability in an actual classroom setting, labeled by experts. Furthermore, when combined with n-gram based similarity metrics, KDA{\_}disc and KDA{\_}cont are shown to have a strong predictive power for various expert-labeled MCQ quality measures.",
}
Package: questions-pkg-joanclopezm
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  / ___ \ V  V /  ___) | | |___| | (_) | |_| | (_| |\__, |
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Package: questions-three-aws
Amazon Web Services Integrations for Questions ThreeThis is an optional package for theQuestions Three testing library.S3 Artifact SaverAs the name implies, the S3 Artifact Saver saves artifacts to an AWS S3 bucket.  It subscribes toARTIFACT_CREATEDandREPORT_CREATEDevents and saves everything it sees. Among other things, this will include JUnit XML test reports, HTTP transcripts from the built-in HTTP Client, and screenshots and DOM dumps from theoptional Selenium integrations.ActivationCreate (or append) a custom reporters file:echo questions_three_aws.s3_artifact_saver.S3ArtifactSaver >> /var/custom_reportersInform Questions Three about the custom reporters file:export CUSTOM_REPORTERS_FILE=/var/custom_reportersTell the S3 Artifact Saver which bucket to use:export S3_BUCKET_FOR_ARTIFACTS=spam-eggs-sausage-spam(Optional) Specify an object(folder) prefix to which test runs will be saved within the specified bucket:export S3_PREFIX_OBJECT_NAME=some_test_runnerSet appropriate environment variables for AWS:Seethe fine AWS documentation.  At a minimum, you will need credentials and a region.
Package: questo
QuestoQuestois not your ordinary library of CLI elements; It's a framework. It's modular, extensible, Pythonic and brain friendly. You can build your own CLI elements, with custom key-press handling, validation and rendering, all in plain old Python; No magic.Basic Usagefromquestoimportselect,promptfromyakhimportget_keyselector=select.Select()selector.state=select.SelectState(title="Who's your favorite Teletubbie",options=["Tinky Winky","Dipsy","Laa-Laa","Po","The Weird Vacuum Thing"],selected=4)withselector.displayed():whileTrue:selector.state=select.key_handler(selector.state,get_key())ifselector.state.exitorselector.state.abort:breakprompter=prompt.Prompt()prompter.state=prompt.PromptState(title=f"Why{selector.result}?",)withprompter.displayed():whileTrue:prompter.state=prompt.key_handler(prompter.state,get_key())ifprompter.state.exitorprompter.state.abort:breakprint(f"Ah, of course:{prompter.result}")
Package: questradeapi
Questrade API WrapperThis package is a Python wrapper for the [Questrade API][https://www.questrade.com/api].InstallationTo install QuestradeAPI, you can usepipenv(orpip, of course):pip install questradeapiDocumentationAll the documentation is available athttps://questradeapi.readthedocs.io/en/latest/.Issue ReportingI you find a bug, have a feature request, or have design suggestions, please do not hesitate to report it in the issues section of this repository. For any security related issues, please do not report them publicly on the public GitHub issue tracker but contact me direcly by email.AuthorAntoine Viscardi
Package: quetz-client
quetz-clientA Python client to interact with a Quetz server. This client is compatible with allquetzversions listedhere.InstallationFrom conda-forgemambainstallquetz-clientFrom this repoYou can install the package in development mode using:gitclonegit@github.com:mamba-org/quetz-client.gitcdquetz-client# create and activate a fresh environment named quetz-client# see environment.yml for detailsmambaenvcreate
condaactivatequetz-client

pre-commitinstall
pipinstall--no-build-isolation-e.UsagePython Clientfromquetz_clientimportQuetzClienturl=""# URL to your Quetz servertoken=""# API token for your Quetz serverclient=QuetzClient.from_token(url,token)forchannelinclient.yield_channels():print(channel)CLI ClientexportQUETZ_SERVER_URL=""# URL to your Quetz serverexportQUETZ_API_KEY=""# API token for your Quetz serverquetz-client--help
Package: quetz-frontend
Quetz-frontendThe Open-Source Server for Conda Packagespart of mamba-orgPackage ManagermambaPackage ServerquetzPackage BuilderboaDevelopmentFirst of all, clone quetz and quetz-frontend, create a conda environment using theenvironment.ymlin quetz, run quetz and modify its config file.# Create an environmentmambaenvcreate-fquetz/environment.yml
mambaactivatequetz
mambainstall-cconda-forgenodejs=16yarn=1.22Install Quetz in dev modecdquetz
pipinstall-e.# Run quetzquetzruntest_quetz--delete--copy-conf./dev_config.toml--dev--reloadModify thequetz/test_quetz/config.tomlfile to add the client_id, client_secret, github username and the front-end paths.[github]# Register the app here: https://github.com/settings/applications/newclient_id="id"client_secret="secret"[users]admins=["github:username"]Install Quetz-Frontend in dev mode# build the apppipinstall-e.# Create a link to the quetz folderquetz-frontendlink-frontend--developmentUseful commands# Start an already configured quetz deployment in dev mode:quetzstarttest_quetz--reload# Build the Quetz-frontendyarnrunbuild# Build the Quetz-Frontend in watch modeyarnrunwatchDisabling extensions"quetz":{"extension":true,"outputDir":"quetz_light_theme/quetzextension","themePath":"style/index.css","disabledExtensions":["quetz-theme"]},Command line toolQuetz fronted also comes with a cli to manage extensionsUsage:quetz-frontend[OPTIONS]COMMAND[ARGS]...

Options:--install-completionInstallcompletionforthecurrentshell.--show-completionShowcompletionforthecurrentshell,tocopyitorcustomizetheinstallation.--helpShowthismessageandexit.

Commands:buildBuildanextensioncleanCleantheextensionsdirectoryclean-frontendCleantheQuetz-FrontenddevelopBuildandinstallanextensionindevmodeinstallBuildandinstallanextensionlink-frontendIntalltheQuetz-FrontendlistListofextensionspathswatchWatchanextension
Package: quetzpy
Quetzalcoatl :snake: :bird: :eyeglasses:Understand how people are talking of a topic based on a simple queryQuetzalcoatl :snake: :bird: :eyeglasses:1. WHAT IS IT?2. WHAT'S INSIDE THIS REPO ?2.1 root folder2.2 src folder2.3 __config__ folder2.4 media folder3. HOW TO USE IT ?4. ABOUT THE PROJECT5. CONTACT THE AUTHOR6. CONTRIBUTE1. WHAT IS IT?The Queztzalcoatl project is the ability to:eyes: excavate the :bird: Twitter API to extract insights about any topic that people talk about.From a topic, you should be able to understand how people react using the power of text mining.:arrow_up:Go to top page2. WHAT'S INSIDE THIS REPO ?Quetzalcoatl
├── /__config__
│   ├── DEMO_configuration_extractor.json
│   └── DEMO_configuration_runner.json
├── /media
├── /src
│   ├── /modules
│   │   ├── data_cleaning.py  
│   │   ├── data_mining.py  
│   │   ├── data_stats.py  
│   │   ├── decorators.py  
│   │   └── tweeter_connection.py  
│   └── Quetzalcoatl.py
├── requirements.txt
├── demo.py
├── .gitignore  
├── README.md
└── LICENCE2.1 root folderYou will find:FileDescriptionrequirements.txtUsed by pip to install dependanciesdemo.pyA demo script.gitignore-README.md-LICENCE-2.2 src folderYou will find:FileDescription/src/modulesContains modules used by the project/src/modules/data_cleaning.pymodule used to clean the tweets/src/modules/data_mining.pymodule used to extract insights from each tweets/src/modules/data_stats.pymodule used to run some statistics from the mined data or tracked words/src/modules/decorators.pymodule that contains all the decorators/src/modules/tweeter_connection.pymodule used to connect to the Twitter API/src/Quetzalcoatl.pyContains all the classes of the project2.3 __config__ folderYou will find the two configuration files used by the demo script2.4 media folderAll the stuff that are used to produce documents:arrow_up:Go to top page3. HOW TO USE IT ?Want to try the project? Please, follow the instructions from the Wiki page!:arrow_up:Go to top page4. ABOUT THE PROJECTI started this project a long time ago as my very first Python project. At first, I used some webscraping with Selenium as I didn't know how to use an API. As my experience with Python grew, I switched to the Twitter REST API and bash scripting and started think automation. Now, I am willing to create a package, or even an API inside which it would be possible to track an information inside a topic.:arrow_up:Go to top page5. CONTACT THE AUTHORYou can reach me on Twitter {LINK}I am also on LinkedIn {LINK}6. CONTRIBUTEThe Quetzalcoatl is a open source project. As so, if you feel interested in building the project with me, it would be amazing!Feel free to contact me on Twitter so I can tell you how you can help.We also have an issue page,link here!:arrow_up:Go to top page
Package: quetz-sql-authenticator
SQL AuthenticatorAn authenticator that stores credentials in the Quetz SQL database using passlib. It ships with REST routes for CRUD operations on the credentials table.InstallationLocally after cloning:pip install -e .Once uploaded to conda-forge:mamba install -c conda-forge quetz-sql-authenticatorUsageThe authenticator should be active now. You can login by navigating to<HOST>/auth/sql/login.CRUD operationsThe authenticator provides REST routes to create, update, and delete credentials and to reset the entire table.GET /api/sqlauth/credentials/: List all users.GET /api/sqlauth/credentials/{username}: Verify that a user exists.POST /api/sqlauth/credentials/{username}?password={password}: Create a new user.PUT /api/sqlauth/credentials/{username}?password={password}: Update a user's password.DELETE /api/sqlauth/credentials/{username}: Delete a user.
Package: quetz-theme
quetz-themeA dark-yellow theme for Quetz.RequirementsJupyterLab >= 3.0Installpipinstallquetz-themeContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the quetz-theme directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmrunbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmrunwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm run buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseUninstallpipuninstallquetz-theme
jupyterlabextensionuninstallquetz-theme
Package: queueCrypt
queueCrypt."queueCrypt" is a library that is library that creates a queue and secures it better.
Basically this library encrypts the data that is been added to the queue and saves the data
securely and safely.Libraries Needed:pip install cryptographythis library is needed to encrypt the data. Without this
library the library won't work!How To Call The Library?fromqueueCryptimportQueueTo Call The Errors Exceptions Use:fromqueueCryptimportErrorQueueSizeNotValid,ErrorRequestedHigherThanExpected#----OR----#fromqueueCrypt.errorsimport*To Call The Encryption Handler Use:fromqueueCrypt.extimportEncryption#----OR----#fromqueueCrypt.extimport*First Thing That Needed Before Using...fromqueueCryptimport*q=Queue(5)#----OR----#q=Queue()Every time that you create the Queue function you can setQueue(number)or useQueue()which will leave the queue with no limit. By setting a number,
you set the queue length, that can be changed if you need by usingq.change_queue_length(number).Examples:Let's create a secret input from the user that takes passwords
and encrypts them. The user will type the passwords until he will type"quit". After adding them
to the queue, print the encrypted list, and the decrypted list.fromqueueCryptimport*q=Queue()whileTrue:user=input("Type passwords: ")ifuser=="quit":break# When putting the user data you need to convert it to bytes!q.put(bytes(user,'utf8'))# returns in bytes form.print(q.get_queue_encrypted())print(q.get_queue_decrypted())Now let's say that you want to encrypt the data again
but after printing the inputted data, you return the encrypted data and delete it.fromqueueCryptimport*q=Queue()whileTrue:user=input("Type passwords: ")ifuser=="quit":break# When putting the user data you need to convert it to bytes!q.put(bytes(user,'utf8'))print(q.get_queue_encrypted()[0].decode())# destroys the first element in the queue list with timeout of 0.1.q.next_and_destroy(0.1)