Saravanan666/pypi-org huggingface dataset:
Package: aioyoutube
AioyoutubeAboutUsageAboutAioyoutube is a Python async wrapper for youtube.com REST API.UsageNote:For use aioyoutube package, you must create youtube application 
and generate API application key. You can do it 
on https://console.developers.google.comInstall packagepip install aioyoutubeUse apiAll description of youtube api methods you can find onyoutube api page.
All api method names in package match with youtube api method names.Currently implemented methods:searchcommentscommentThreadschannelsplaylistItemsplaylistsvideosExapmple using youtube api method "search":importasynciofromaioyoutubeimportApiapi=Api()loop=asyncio.get_event_loop()search=api.search(key='your application key',text='search text')task=loop.create_task(search)result=loop.run_until_complete(task)
Package: aioytmdesktopapi
YouTube Music Desktop Remote Control APIAsync IO API package forYouTube Music Desktop app.Installationpython3-mpipinstallaioytmdesktopapiContentsThis package contains theYtmDesktopclass which represents the API.
Check theAPI documentationto see what functionality is available and how to use it.Example usageCheckexample.pyfor a runnable example.asyncwithaiohttp.ClientSession()assession:asyncwithYtmDesktop(session,"192.168.1.123",password="PASSWORD")asytmdesktop:# Initialize first before using any of the functionalityawaitytmdesktop.initialize()# Print status of some attributesprint(f"{ytmdesktop.player.has_song=}")print(f"{ytmdesktop.player.is_paused=}")print(f"{ytmdesktop.track.author=}")print(f"{ytmdesktop.track.title=}")print(f"{ytmdesktop.track.album=}")# Pause the current trackawaitytmdesktop.send_command.track_pause()# Call `.update()` to update the internal state of the API with the state of the actual player instanceawaitytmdesktop.update()# Print updated stateprint(f"{ytmdesktop.player.is_paused=}")time.sleep(2)# Play the current trackawaitytmdesktop.send_command.track_play()# Call `.update()` to update the internal state of the API with the state of the actual player instanceawaitytmdesktop.update()# Print updated stateprint(f"{ytmdesktop.player.is_paused=}")
Package: aiozabbix
aiozabbixaiozabbixis a Python package that provides an asynchronous
interface to theZabbix API,
using aiohttp. It is based onPyZabbix.Example usageThe interface mimics PyZabbix as closely as possible:importasynciofromaiozabbiximportZabbixAPIasyncdefmain():zapi=ZabbixAPI('https://zabbixserver.example.com/zabbix')awaitzapi.login('zabbix user')hosts=awaitzapi.host.get(output=['host','hostid','name','status'])print(hosts)if__name__=='__main__':loop=asyncio.get_event_loop()loop.run_until_complete(main())loop.close()To customize the HTTP requests, for example to perform HTTP basic
authentication, you need to provide your ownaiohttp.ClientSession:importasyncioimportaiohttpfromaiozabbiximportZabbixAPIasyncdefmain():auth=aiohttp.BasicAuth('zabbix user',password='zabbix password')asyncwithaiohttp.ClientSession(auth=auth)assession:zapi=ZabbixAPI('https://zabbixserver.example.com/zabbix',client_session=session)awaitzapi.login('zabbix user')hosts=awaitzapi.host.get(output=['host','hostid','name','status'])print(hosts)if__name__=='__main__':loop=asyncio.get_event_loop()loop.run_until_complete(main())loop.close()
Package: aiozaneapi
Made for Python 3.6+.Website LinkExample:importaiozaneapi# Import the package so it can be used.client=aiozaneapi.Client('Token Here')# Instantiate the Client.try:image=awaitclient.magic('Image URL Here')# This will return a BytesIO object.exceptaiozaneapi.GatewayErroraserr:print(f'Error has occurred on the server-side.{err}')exceptaiozaneapi.UnauthorizedErroraserr:print(f'Error has occurred on the client-side.{err}')awaitclient.close()# Close the client unless you want aiohttp screaming at you.
Package: aiozoom
AIOZOOMAiozoom is an async python library for interaction with Zoom APIRequirementsPython 3.9pipInstallationUnder console using pipIn the console, run the following command:pipinstall--upgradeaiozoomQuick startImport modulefromaiozoomimportZoomConfigure a ClientfromaiozoomimportZoomZoom.configure('JWT_TOKEN')Create a meetingimportasynciofromaiozoomimportZoomZoom.configure('JWT_TOKEN')asyncdefmain():zoom=Zoom()awaitzoom.create_meeting('example@example.com',{'title':'test'})loop=asyncio.get_event_loop()task=loop.create_task(main())loop.run_until_complete(task)loop.close()Available methods:create_meeting(email, body)get_meeting(meeting_id)stop_meeting(meeting_id)delete_meeting(meeting_id)update_meeting(meeting_id, body)get_meeting_participants(meeting_id)get_meeting_info(meeting_id)and more...Docs will be available soon...
Package: aiozyre
aiozyreasyncio-friendly Python bindings forZyre, an open-source framework for proximity-based peer-to-peer applications.InstallationpipinstallaiozyreTests run on both Linux and macOS for the following Python versions:CPython: 3.6.4, 3.7.0, 3.8.0PyPy: 7.2.0 (3.6.9)CPython 3.6.3 and lower are not supported due tothis bug.UsageSee theexample peer-to-peer chat client.ContributingPull requests are welcome, please file any issues you encounter.Changelogv1.1.5 (2020-07-22)Fix memory leak where zlist items were not being freedFix egg installation issue by passing zip_safe=Falsev1.1.4 (2020-05-23)HandleSILENTmessage
Package: ai-param-validation
ProjectParameter validation utility for Python methods whether they are standalone or in a class.Install: pip install .Test: pytest testsSee src/Readme.md for more details.See test_class.py for using the utility with a class.
See test_standalone for using with standalone methods.ContributingThis project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visithttps://cla.opensource.microsoft.com.When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.This project has adopted theMicrosoft Open Source Code of Conduct.
For more information see theCode of Conduct FAQor
contactopencode@microsoft.comwith any additional questions or comments.TrademarksThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must followMicrosoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
Package: aipkgs-notifications
APNS Push NotificationThis Python script allows you to send push notifications to Apple devices manually. It uses the Apple Push Notification service (APNS) to deliver notifications to your application users.Why use initialize_apns?Theinitialize_apnsfunction is used to set up the APNS with your application's specific details. This includes your key ID, team ID, bundle ID, and the path to your p8 or pem file. You can also specify whether you want to use the production or sandbox environment (default is sandbox).How to use the script?First, import the necessary modules:importapnsThen, initialize the APNS with your application's details:KEY_ID=''TEAM_ID=''BUNDLE_ID='com.test.app'IS_PROD=FalseP8_KEY_PATH='path/to/p8/key'PEM_FILE_PATH='path/to/pem/file'APNS_PRIORITY=10APNS_EXPIRATION=0apns.initialize_apns(key_id=KEY_ID,team_id=TEAM_ID,bundle_id=BUNDLE_ID,is_prod=IS_PROD,p8_key_path=P8_KEY_PATH,pem_file_path=PEM_FILE_PATH,apns_priority=APNS_PRIORITY,apns_expiration=APNS_EXPIRATION)apns.apns_config().verbose=TrueNow, you can send a push notification:device_token=""data={}title="Hello World!"response:APNSResponse=apns.push(device_token=device_token,title=title,data=data,badge=1,push_type=apns.PushType.alert,collapse_id=None)Using p8 or pem fileDepending on your preference or requirements, you can use either a p8 or pem file for authentication. If you want to use a p8 file, pass the path to thep8_key_pathparameter in theinitialize_apnsfunction. If you want to use a pem file, pass the path to thepem_file_pathparameter.Getting a p8 file or pem fileTo get the p8 or pem file for APNS push notification, you can follow these steps:For p8 file:Go to the Apple Developer Portal.Navigate to the "Certificates, Identifiers & Profiles" section.Under the "Keys" section, click on the "+" button to create a new key. (make sure to selectApple Push Notifications service (APNs)from the checkbox)Make sure to write down theKey IDas you will need it later.Download the generated p8 file and save it to a secure location.For pem file:Convert the exported .p12 file to a .pem file using the following command in the terminal:openssl pkcs12 -clcerts -legacy -nodes -in Certificates.p12 -out AuthKey.pemReplace "Certificates.p12" with the path to your .p12 file or keep it if you're in the same directory as your .p12 file.Enter the password for the .p12 file when prompted (hit Enter if blank).The converted pem file will be saved as "AuthKey.pem" in the current directory.Once you have obtained the p8 or pem file, you can use it in theinitialize_apnsfunction by providing the path to the file in thep8_key_pathorpem_file_pathparameter, respectively.
Package: aiplugins
🎸 Plug and PlaiPlug and Plai is an open source library aiming to simplify the integration of AI plugins into open-source language models (LLMs).It provides utility functions to get a list of active plugins from plugnplai.com directory, get plugin manifests, and extract OpenAPI specifications and load plugins.InstallationYou can install Plug and PlAI using pip:pip install plugnplaiUsageUtility FunctionsThe following utility functions are available in the library:get_plugins(endpoint): Get a list of available plugins from aplugins repository.get_plugin_manifest(url): Get the AI plugin manifest from the specified plugin URL.get_openapi_url(url, manifest): Get the OpenAPI URL from the plugin manifest.get_openapi_spec(openapi_url): Get the OpenAPI specification from the specified OpenAPI URL.from_url(url): Returns the Manifest and OpenAPI specification from the plugin URL.ExampleHere is an example of how to use the utility functions:importplugnplai# Get all plugins from plugnplai.comurls=plugnplai.get_plugins()#  Get ChatGPT plugins - only ChatGPT verified pluginsurls=plugnplai.get_plugins(filter='ChatGPT')#  Get working plugins - only tested plugins (in progress)urls=plugnplai.get_plugins(filter='working')# Get the Manifest and the OpenAPI specification from the plugin URLmanifest,openapi_spec=spec_from_url(plugins[0])ContributingPlug and Plai is an open source library, and we welcome contributions from the entire community. If you're interested in contributing to the project, please feel free to fork, submit pull requests, report issues, or suggest new features.LinksPlugins directory:https://plugplai.com/API reference:https://plugnplai.github.io/
Package: aipo
Version:0.1.0Download:http://pypi.org/project/aipoSource:http://github.com/CaiqueReinhold/aipoKeywords:asyncio, task, job, queueWhat is Aipo?Aipo is a minimal framework for dispatching and running background tasks using asynchronous python.Using the task decorator you can turn your python functions into tasks that can be dispatched to a queue and executed in the aipo server.Setting up your aipo app:app = Aipo(config='aipo.yaml')

@app.task
async def my_task():
    await get_stuff()
    await comunicate_stuff()Once you execute you task it will be dispatched to the queue and executed in the aipo server:await my_task()Run your Aipo server using the command line::aipo run –config aipo.yamlInstallationYou can install Aipo from the Python Package Index (PyPI).To install usingpip:$ pip install aipoCurrently supported Aipo backendsRedisCurrently supported event loopsasynciouvloop
Package: aipod
AIPODAIPOD 是一个 AI 模型服务框架：约定 AI 模型提供的接口（初始化/训练/预测/日志等）基于模型定义，启动一个 RPC 服务，并提供 RPC client模型接入步骤通过 AIPOD 接入模型的过程大致如下服务端：fromaipod.modelimportAIModelBasefromaipod.rpc.serverimportserveclassUserModel(AIModelBase):deftrain(self,**kwargs):# 在此实现训练过程passdefpredict(self,**kwargs):# 在此实现预测过程pass# 启动一个加载了 UserModel 模型的 rpc 服务serve(UserModel)客户端：fromaipod.rpc.clientimportAIClientmodel=AIClient(address="{rpc_server_address}",version="{model_version}")# 初始化(仅模型训练前需要初始化)model.initialize(**model_configs)# 训练model.train(**trainning_options)# 查看训练日志（训练进度、评估结果等，非必要）logs=model.log()# 预测result=model.predict(**input_data)类方法关系如下（以 predict 为例）class AIClient(AIModelBase) -> AIClient.predict(**input_data)
   ↓
RPC Server
   ↓
class UserModel(AIModelBase) -> UserModel.predict(**input_data)因此自定义模型，即是基于aipod.model.AIModelBase实现一个模型类，其中主要是训练和预测方法模型封装AIPOD 使用aipod.model.AIModelBase类对模型进行了定义，主要包括以下方法：AIModelBase.initialize()模型初始化AIModelBase.train()模型训练AIModelBase.predict()模型预测AIModelBase.log()查看日志以上方法使用**kwargs传递任意所需参数，由于需要通过网络传递，输入/输出均需要是可序列化的数据类型其中有一个保留关键字binary_data用于传递二进制数据如需传递海量的训练数据，建议使用接入共享/网络存储的方式，参数部分只传路径在模型训练前需调用AIModelBase.initialize()初始化模型数据文件夹，同时会将 initialize 所有的参数作为模型参数保存下来，之后通过AIModelBase.model_info进行访问AIModelBase.model_dir()方法会返回此模型实例对应的文件夹位置，在训练/预测过程中所有相关数据应存放在此文件夹下无需在线训练的模型项目，也可以只实现AIModelBase.predict()方法，加载自有模型进行预测模型服务经 AIModelBase 封装好的模型类可以通过serve(UserAIModel)启动一个 rpc 服务，客户端即可使用 AIClient 进行调用模型服务会负载某一个模型类，且支持多个模型实例，由AIClient的version参数进行区分为了减少模型加载时间带来的影响，模型服务会缓存加载好的模型实例，但由于显存的限制，需要通过AIPOD_MODEL_POOL_SIZE环境变量配置最大同时加载的模型实例数量（超出会进行 LRU 淘汰）模型服务可由环境变量进行控制，列表如下：AIPOD_LISTEN_PORT：rpc 服务监听端口，默认为50051AIPOD_DATA_PATH：模型数据存放路径，默认为appdata/AIPOD_MODEL_POOL_SIZE：服务内模型缓存池大小，默认为1AIPOD_RPC_MAX_WORKERS：RPC 服务线程池大小，默认为12
Package: ai-privacy-toolkit
ai-privacy-toolkitA toolkit for tools and techniques related to the privacy and compliance of AI models.Theanonymizationmodule contains methods for anonymizing ML model
training data, so that when a model is retrained on the anonymized data, the model itself will also be
considered anonymous. This may help exempt the model from different obligations and restrictions
set out in data protection regulations such as GDPR, CCPA, etc.Theminimizationmodule contains methods to help adhere to the data
minimization principle in GDPR for ML models. It enables to reduce the amount of
personal data needed to perform predictions with a machine learning model, while still enabling the model
to make accurate predictions. This is done by by removing or generalizing some of the input features.Thedataset assessmentmodule implements a tool for privacy assessment of
synthetic datasets that are to be used in AI model training.Official ai-privacy-toolkit documentation:https://ai-privacy-toolkit.readthedocs.io/en/latest/Installation: pip install ai-privacy-toolkitFor more information or help using or improving the toolkit, please contact Abigail Goldsteen atabigailt@il.ibm.com,
or join our Slack channel:https://aip360.mybluemix.net/community.We welcome new contributors! If you're interested, take a look at ourcontribution guidelines.Related toolkits:ai-minimization-toolkit - has been migrated into this toolkit.differential-privacy-library: A
general-purpose library for experimenting with, investigating and developing applications in,
differential privacy.adversarial-robustness-toolbox:
A Python library for Machine Learning Security. Includes an attack module calledinferencethat contains privacy attacks on ML models
(membership inference, attribute inference, model inversion and database reconstruction) as well as aprivacymetrics module that contains
membership leakage metrics for ML models.CitationAbigail Goldsteen, Ola Saadi, Ron Shmelkin, Shlomit Shachor, Natalia Razinkov,
"AI privacy toolkit", SoftwareX, Volume 22, 2023, 101352, ISSN 2352-7110,https://doi.org/10.1016/j.softx.2023.101352.
Package: aipsetup
Software For Building and Maintaining Own GNU/Linux distributionaipsetupFeatures:building software packages from tarrballs
* multihost package buildings supported
* multilib package buildings supportedinstalling and uninstalling packagessearching for and deletion of garbage among system filessome sort of tools for creating distributionsRequirements:some packages from my GitHub page are required.
also You’ll need BottlePy, CherryPy, Mako, SQLAlchemy, lxml
and some other stuff. GUI elements requires Gtk+3 and PyGObject.aipsetup is written (and oriented) on system with Python 3.5GitHub page is here:https://github.com/AnimusPEXUS/wayround_org_aipsetupSome slack distribution packages are here:rsync://wayround.org/Lailalo_snapshots/
Package: aipy
This package collects together tools for radio astronomical interferometry. In addition to pure-python phasing, calibration, imaging, and deconvolution code, this package includes interfaces to MIRIAD (a Fortran interferometry package) and HEALPix (a package for representing spherical data sets). Instructions, documentation, and a FAQ may be found at [the aipy GitHub page](http://github.com/HERA-Team/aipy).
Package: ai-python
# AI Python by MicrosoftThis is a library of Python packages scanned using various open source and internal tools to provide up-to-date, and secury dependencies.## Installation
Currently Python3.7 is supported.### Ubuntu 18.04
The aiubuntu library is provided to simplify installation of required Ubuntu dependancies.shelladd-apt-repository -y ppa:deadsnakes/ppa && apt-get update && apt-get install -y python3.7 python3.7-devcurl -fSsLOhttps://bootstrap.pypa.io/get-pip.py&& /usr/bin/python3.7 get-pip.py ‘pip==20.3.3’add-apt-repository -y ppa:dciborow/ppa && apt-get update && apt-get install -y aiubuntu# For Core Libaries (this will install nearly everything)
python3.7 -m pip install ai-python[core]# For Libraries needed for testing
python3.7 -m pip install ai-python[tests]# For Libraries needed for a single package
python3.7 -m pip install ai-python[retail]## ContributingThis project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visithttps://cla.opensource.microsoft.com.When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.## TrademarksThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must follow
[Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party’s policies.
Package: aiqc
📚DocumentationTechnical OverviewWhat is it?AIQC is an open source Python package that provides adeclarative API for end-to-end MLOps(dataset registration, preprocessing, experiment tracking, model evaluation, inference, post-processing, etc) in order to make deep learning more accessible to researchers.How does it work?The backend is aSQLite object-relational model (ORM)for machine learning objects (Dataset, Feature, Label, Splits, Algorithm, Job, etc). The high-level API stacks these building blocks intostandardized workflowsfor various: analyses (classify, regress, generate), data types (tabular, sequence, image), and libraries (TensorFlow, PyTorch). The benefits of this approach are:⏱️90% reduction in data wranglingvia automation of highly conditional and repetitive tasks that vary for each type of dataset and analysis (e.g. model evaluation, metrics, and charts for every split of every model).💾Reproducibility, not only because the workflow is persisted (e.g. encoder metadata) but also because it provides standardized classes as opposed to open-ended scripting (e.g. 'X_train, y_test').🎛️No need to install and maintainapplication and database servers for experiment tracking. SQLite is just a highly-performant and portable file that is automatically configured by `aiqc.setup()`. AIQC is just a pip-installable Python package that works great in Jupyter (or any IDE/shell), and provides a Dash-Plotly user interface (UI) for areal-time experiment tracking.What's on the roadmap?🖥️   Expand the UI (e.g. dataset registration and model design) to make it even more approachable for less technical users.☁️  Schedule parallel training of models in the cloud.📚DocumentationExperiment TrackerCompare ModelsWhat if?Usage# Built on Python 3.7.12 to mirror Google Colab$pipinstall--upgradepip$pipinstall--upgradewheel$pipinstall--upgradeaiqc# Monitor and evaluate models (from CLI)$python-maiqc.ui.app# High-level APIfromaiqcimportmlops# Declare preprocessing stepsmlops.Pipeline()# Define, train, & evaluate modelsmlops.Experiment().run_jobs()# Infer using original Pipelinemlops.Inference()Official Installation Documentation:https://aiqc.readthedocs.io/en/latest/notebooks/installation.html📚Documentation
Package: aiqDocTests
aiqDocTestsAiqDocTests A framework to validate request/response's json and create documentation for applications maintained by the devs of themost greedy-gut app on the internet!Install with pip3pip3 install aiqDocTestsInit in the project folderaiqdoctests --initWill be created the folderdata_scructures_ioandstatic.In thedata_scructures_io, is the json files to test the request.ExampleWe usingCerberusto validate the structure. So if any valid in the json response is a name, type or don't send. Will occurs a exception in tests.In thestaticfolder, will be the json file toSwagger, with this file you can any package in any language what you want to read.
This swagger.json is generate, so every time that you run the commandaiqdoctests -gin the folder this file will be update.The.aiqdoctests.configfileThis json file is for configuration, so the name folders and other things can be personalizable.docs_urlwith the commandaiqdoctests --docswill be up un flask server in the port 3000 and read the swagger file, this value is for which url will run.default: localhost:3000/docssave_file_swaggerThe name file generate to swagger.default: swagger.jsondata_structures_folderThe name folder that are the data scructures for requests.default: data_structures_iotests_folderThe name folder that will be the tests.default: teststests_before_cmdSometimes in the project, we wanna run command/script before start the tests, example create the tables in the bd. Will run this command before start the tests.tests_between_cmdThis command is for run between tests, in thetearDown, soafterrun a test, this is for a script or migration that you want to run for clean the bd for example.swaggerThe header for swagger file.For more informationFor more you can see in the example (https://github.com/aiqfome/aiqDocTests-example)Relax, this documentation is still in construction :construction_worker:Any doubt create a issue.Made with :pizza: & :hearts:! Enjoy it!
Package: aiqpy
aiqpy is a wrapper library that allows you to interact with anAppearIQ 8platform using Python.The library connects to the REST APIs of the platform and takes care of authentication and session management so you can focus on funnier things.Example: Changing the password of a user>>>platform=aiqpy.Connection(profile='dev')>>>user=platform.get(['admin','users'],username='stephen.falken')[0]>>>user['password']='joshua'>>>updated=platform.put(['admin','users'],user)Dependenciesaiqpy usesRequestsfor performing HTTP requests.Licenseaiqpy is licensed under GNU GPL v3.0.