Saravanan666/pypi-org huggingface dataset:
Package: aishell
AiShell 🤖A simple Python code that connects to OpenAI's ChatGPT and executes the returned results.If you are interested in these projects, please checkout AiShell's brother project:YGK-a. YGK-a is a client for the ChatGPT from your terminal, and also supports unix/linux pipelines.DemoKey Features 💡Interact with your computer using natural languageAutomatically executes the command from the response of ChatGPTGood for complex tasks like handling Git and extracting tar filesNo need to search StackOverflow for commands,AiShellhas got you coveredAiShellsimplifies the process of setting up and retrieving tokens or API keys.WithAiShell, you don't have to worry about the technical details.Simply installAiShell, execute it, and you're ready to go!Prerequisites 📚Python 3.9+ChatGPT Account (or OpenAI Account)Getting Started 🚀To begin usingAiShell, start by installing it with pip:pipinstallaishellOnce you've installedAiShell, you can start using it right away.
For example, to print "Hello World" usingAiShell, enter the following command:aishell'print Hello World'Advanced Settings 🛠By default,AiShellis configured to use the reverse-engineered ChatGPT client and retrieve login information from your browser, so you don't need to configure anything to useAiShell. However, for those who want to use different models with an OpenAI API Key, you can configure it as follows:Create an account on OpenAI.Go tohttps://platform.openai.com/account/api-keysand copy your API key.Modify or create the~/.ygka_openai_config.jsonfile as follows:{..."language_model":"official_chatgpt","openai_api_key":"<your OpenAI API key>"}Here, you can add your OpenAI API key. This will enable AiShell to use the official chatgpt api and the API key when executing commands.Contributions 💬Feel free to contribute toAiShellby adding more functionality or fixing bugs.
Package: aishield
AIShield Python Integration PackageAIShield provides the Python convenience package to allow users to seamlessly integrate AIShield Vulnerability Assessment and Defense capabilities into their AI development workflows. Users will receive assessment reports, sample attack vectors, and a threat-informed defense model with telemetry connection to SIEM/SOAR, such as Splunk and Microsoft Sentinel.RequirementsRequires Python>=3.6, and pip >= 19.0Installation$ pip install aishieldDetailsCheck out the Quick Start Examplehere.More references implementations, tutorials, samples, and documentation of AIShield can be found on ourGithub Repository.Pre-requisites:AIShield API should be white-listed, or proxy settings must be appropriately configured for the AIShield API to be called.Valid AIShield API subscription plan and authentication keys. For details regarding subscription please visit,Subscription Pageor reach out to sales atAIShield.Contact@bosch.comSupported Features:Model Extraction attack Vulnerability Analysis and Threat informed Defense Generation with relevant report artifacts for Image & Tabular ClassificationSupported for Models trained on Tensorflow (Tensorflow >=2.5.0 and <=2.9.1)Supported Input Model File formats: .h5,.pycAssessment Report formats available: PDF, XML, JSON, TXTMore about AIShieldWebsite:https://www.boschaishield.com/Email:AIShield.Contact@bosch.comVersion History0.1.5Added vulnerability analysis for image segmentation: model extraction attackUpdated to be compatible with latest AIShield API version. Also, now api_key is not required to be provided explicitly for analysis. It will be generated from org_id and policies are consumed accordingly.0.1.4Added vulnerability analysis for time series forecasting: model extraction attack0.1.3Updated to be compatible with latest AIShield API versionAdded vulnerability analysis for tabular classification: model evasion attack0.1.2Added vulnerability analysis for image classification: model evasion & model poisoning attackAdded vulnerability analysis for tabular classification: model extraction attack0.1.1Updated to be compatible with latest AIShield API version0.1.0Initial versionAdded vulnerability analysis for model extraction attack:for image_classification task*Prepare the vulnerability configs and send the model for analysis to AIShield API.This will generate vulnerability analysis reports, threat-informed defense generation with SIEM/SOAR telemetry enabled, defense reports, and sample attack data artifacts.*
Package: aisim
AISim ‒ Simulations for light-pulse atom interferometryAISim is a Python package for simulating light-pulse atom
interferometers.It uses dedicated objects to model the laser beams, the atomic ensemble
and the detection system and store experimental parameters in a neat
way. After you define these objects you can use built-in propagators to
simulate internal and external degrees of freedom of cold atoms.InstallationThe latest tagged release can installed via pip withpip install aisimor via conda withconda install -c conda-forge aisimAlternatively, if you plan to make changes to the code, usegit clone https://github.com/bleykauf/aisim.git
cd aisim
python setup.py developUsageFor basic usage and code reference, see thedocumentation.ExamplesSome examples are provided in the form ofJupyter
notebooks:Effect of wavefront aberrations in atom
interferometryRabi oscillations with a Gaussian beam and thermal
atomsMultiport atom interferometerContributingContributions are very welcome. If you want to help, check outour contributions guide.AuthorsBastian Leykauf (https://github.com/bleykauf)Sascha Vowe (https://github.com/savowe)LicenseAISim ‒ Simulations for light-pulse atom interferometryCopyright © 2023 B. Leykauf, S. VoweThis program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.You should have received a copy of the GNU General Public License along with this program. If not, seehttps://www.gnu.org/licenses/.
Package: ais-libpythonpro
libpythonproMÃ³dulo para exemplificar construÃ§Ã£o de projetos Python no curso PyToolsSuportada VersÃ£o do Python 3Para instalar:"""
console
python3 -m venv .venv
source .venv\Scripts\activate
pip install -r requirements-dev.txt
"""Para conferir qualidade do cÃ³digo:'''
console
flake8
'''
Package: aismt
aismtA command-line interface for AI Smart Task.UsingPython package: to add and install this package as a dependency of your project, runpoetry add aismt.Python CLI: to view this app's CLI commands once it's installed, runaismt --help.Python application: to serve this REST API, rundocker compose up appand openlocalhost:8000in your browser. Within the Dev Container, this is equivalent to runningpoe api.ContributingPrerequisites1. Set up Git to use SSHGenerate an SSH keyandadd the SSH key to your GitHub account.Configure SSH to automatically load your SSH keys:cat<< EOF >> ~/.ssh/configHost *AddKeysToAgent yesIgnoreUnknown UseKeychainUseKeychain yesEOF2. Install DockerInstall Docker Desktop.EnableUse Docker Compose V2in Docker Desktop's preferences window.Linux only:Configure Docker to use the BuildKit build system. On macOS and Windows, BuildKit is enabled by default in Docker Desktop.Export your user's user id and group id so thatfiles created in the Dev Container are owned by your user:cat<< EOF >> ~/.bashrcexport UID=$(id --user)export GID=$(id --group)EOF3. Install VS Code or PyCharmInstall VS CodeandVS Code's Dev Containers extension. Alternatively, installPyCharm.Optional:install aNerd Fontsuch asFiraCode Nerd Fontandconfigure VS Codeorconfigure PyCharmto use it.Development environmentsThe following development environments are supported:⭐️GitHub Codespaces: click onCodeand selectCreate codespaceto start a Dev Container withGitHub Codespaces.⭐️Dev Container (with container volume): click onOpen in Dev Containersto clone this repository in a container volume and create a Dev Container with VS Code.Dev Container: clone this repository, open it with VS Code, and runCtrl/⌘+⇧+P→Dev Containers: Reopen in Container.PyCharm: clone this repository, open it with PyCharm, andconfigure Docker Compose as a remote interpreterwith thedevservice.Terminal: clone this repository, open it with your terminal, and rundocker compose up --detach devto start a Dev Container in the background, and then rundocker compose exec dev zshto open a shell prompt in the Dev Container.DevelopingRunpoefrom within the development environment to print a list ofPoe the Poettasks available to run on this project.Runpoetry add {package}from within the development environment to install a run time dependency and add it topyproject.tomlandpoetry.lock. Add--group testor--group devto install a CI or development dependency, respectively.Runpoetry updatefrom within the development environment to upgrade all dependencies to the latest versions allowed bypyproject.toml.
Package: aisolver
我们做什么我们致力于提供优质求解器功能快速上手1、安装python库pipinstallaisolver2、调用求解>>>importaisolver>>>fromaisolverimportmix_integer_solver# 指定需要求解问题的.lp文件>>>file_name="input_problem.lp"# 确定问题类型problem，可选WPMS,FCMCNF,GISP# solution_save默认为None，表示会打印求解结果。当指定文件路径时，会将求解结果保存到指定文件>>>mix_integer_solver(file_name,problem="FCMCNF",solution_save="./solve_out.txt")目前能够求解问题1、WPMS(Weighted Independent Maximum Packing Set)WIMPs问题是NP-hard问题的一个例子，通常被用于寻找无向图中的最大权独立集。在WIMPs问题中，给定一个带权无向图，每个顶点都有一个正权值。问题的目标是找到权值之和最大的独立顶点集，其中独立顶点集是指该集合中的任何两个顶点之间都没有边相连。WIMPs问题在许多领域中都有应用，例如在社交网络分析、电路设计和资源分配等领域中。2、GISP（广义独立集问题）问题GISP问题是一类经典的优化问题，通常用于优化分配问题，例如分配资源或任务到不同的位置或机器上。在GISP问题中，有一组可用的资源和一组任务需要被完成。每个资源都有一个成本，同时可以完成一组任务，这些任务需要一定的资源。任务可以被分配给多个资源，但每个任务只能被完成一次。问题的目标是找到最小化总成本的任务分配方案。GISP问题的一个重要特点是，每个任务可以被分配给多个资源，这使得问题更加复杂。因此，GISP问题被认为是一种困难的组合优化问题。GISP问题在许多领域中都有应用，例如物流、生产调度和资源分配等领域。3、FCMCNF(Facility Capacity Multiple Choice Network Flow)该问题通常涉及在网络流模型中选择路径，以便满足一些特定的容量约束。FCMCNF问题中存在多种选择，每种选择都会影响网络流的容量限制。
具体而言，FCMCNF问题是在具有多个源和汇的带容量的有向图中找到最小费用的流，并满足以下要求:每个源必须流出特定数量的流量。每个汇必须接收特定数量的流量。每个路径可以选择多个容量和费用。每个路径的容量都受到一些限制，例如每个路径的容量不能超过某个特定的值。敬请期待多样功能
Package: aisort
README for PCSorterIntroductionPCSorter is a Python-based utility designed to intelligently organize files in a directory using the OpenAI ChatGPT API. This tool scans a specified directory (including subdirectories if desired), classifies files based on content and type, and reorganizes them into a more structured format. It leverages the advanced capabilities of OpenAI's GPT models to understand and categorize file contents, making file management more efficient and intuitive.Key Features:File sorting using AI-driven insights.Customizable directory and file type handling.Backup and restore functionality for sorted files.Cross-platform compatibility with detailed setup instructions.RequirementsPython 3.6 or higheropenaiPython packageAdditional Python libraries:os,re,shutil,time,argparse,datetime,json,sysAn active OpenAI API keyInstallation InstructionsEnsure Python 3.6+ is installed on your system.Install AiSort via pippip install aisortSet up an environment variable for your OpenAI API key (instructions in the next section).Setting Up Environment VariablesWindowsCommand Prompt:Usesetx OPENAI_API_KEY "Your-API-Key"to set the API key.PowerShell:Apply$env:OPENAI_API_KEY = "Your-API-Key"to set the key.Editing System Properties:Open System Properties -> Advanced -> Environment Variables.Add a new System variable namedOPENAI_API_KEYwith your API key as its value.macOSUsing Terminal:Addexport OPENAI_API_KEY="Your-API-Key"to your.bash_profileor.zshrc.Editing.bash_profileor.zshrc:Open these files in a text editor and add the export line as above.LinuxUsing Terminal:Similar to macOS, useexport OPENAI_API_KEY="Your-API-Key"in.bashrcor equivalent.Editing.bashrcor equivalent:Open the file in an editor and add the export command.ConfigurationBefore running PCSorter, ensure theOPENAI_API_KEYenvironment variable is set.Usage InstructionsRunning the script:ExecuteAiSortin your terminal.Use command-line arguments to specify options like--model,--dir,--include,--backup.Common use cases:Sorting files in the current directory:AiSort sort --dir ./my_directoryUsing a specific GPT model:AiSort sort --model gpt-3.5-turboTroubleshootingAPI Key Not Recognized:Ensure the environment variableOPENAI_API_KEYis correctly set.Permission Errors:Run the script with appropriate permissions or from a non-restricted directory.Invalid Model Specified:Check that the model name is correct and supported.FAQsCan PCSorter handle large directories?Yes, but performance may vary based on the number and size of files.ContributingContributions to PCSorter are welcome. Please submit issues and pull requests through GitHub, adhering to the project's coding standards and guidelines.LicensePCSorter is released under the MIT License. See the LICENSE file for more details.AcknowledgmentsThanks to the contributors and to OpenAI for the API that powers this project. Special thanks to [list any special contributors or resources].
Package: aisp
Artificial Immune Systems Package.Select the language / Selecione o Idioma:English.Português.Package documentation / Documentação do pacote:Docs.Wiki Github.EnglishSummary:Introduction.Installation.DependenciesUser installationHow to import the TechniquesExamples.IntroductionTheAISPis a python package that implements artificial immune systems techniques, distributed under the GNU Lesser General Public License v3.0 (LGPLv3).The package started in2022as a research package at the Federal Institute of Northern Minas Gerais - Salinas campus (IFNMG - Salinas).Artificial Immune Systems (AIS) are inspired by the vertebrate immune system, creating metaphors that apply the ability to detect and catalog pathogens, among other features of this system.Algorithms implemented:[x]Negative Selection.[ ]Dendritic Cells.[ ]Clonalg.[ ]Immune Network Theory.InstallationThe module requires installation ofpython 3.8.10or higher.Dependencies:PackagesVersionnumpy≥ 1.22.4scipy≥ 1.8.1tqdm≥ 4.64.1User installationThe simplest way to install AISP is usingpip:pipinstallaispHow to import the Techniquesfromaisp.NSAimportRNSAnsa=RNSA(N=300,r=0.05)Examples:Example using the negative selection technique (nsa):In the example present in thisnotebook,500random samples were generated, arranged in two groups, one for each class.Below are some examples that use a database for classification with theJupyter notebooktool.Negative Selection:RNSAApplication of negative selection techniques for classification using the Iris family flower database and Old Faithful Geyser:iris_dataBase_examplegeyser_dataBase_exampleBNSAmushrooms_dataBase_examplePortuguêsSumário:Introdução.Instalação.DependênciasInstalação do usuárioComo importar as TecnicasExemplos.IntroduçãoOAISPé um pacote python que implementa as técnicas dos sistemas imunológicos artificiais, distribuído sob a licença GNU Lesser General Public License v3.0 (LGPLv3).O pacote teve início no ano de2022como um pacote de pesquisa no instituto federal do norte de minas gerais - campus salinas (IFNMG - Salinas).Os sistemas imunológicos artificiais (SIA) inspiram-se no sistema imunológico dos vertebrados, criando metáforas que aplicam a capacidade de reconhecer e catalogar os patógenos, entre outras características desse sistema.Algoritmos implementados:[x]Seleção Negativa.[ ]Células Dendríticas.[ ]Clonalg.[ ]Teoria da Rede Imune.InstalaçãoO módulo requer a instalação dopython 3.8.10ou superior.Dependências:PacotesVersãonumpy≥ 1.22.4scipy≥ 1.8.1tqdm≥ 4.64.1Instalação do usuárioA maneira mais simples de instalação do AISP é utilizando opip:pipinstallaispComo importar as Tecnicasfromaisp.NSAimportRNSAnsa=RNSA(N=300,r=0.05)Exemplos:Exemplo utilizando a técnica de seleção negativa (nsa):No exemplo presente nessenotebook, gerando500amostras aleatórias dispostas em dois grupos um para cada classe.A seguir alguns exemplos que utiliza-se de base de dados para classificação com a ferramentaJupyter notebook.Seleção Negativa:RNSAAplicação das tecnica de seleção negativa para classificação utilizando a base de dados de flores da família Iris e Old Faithful Geyser:iris_dataBase_examplegeyser_dataBase_exampleBNSAmushrooms_dataBase_example
Package: aisploit
🤖🛡️🔍🔒🔑 aisploitAISploit is a Python package designed to support red teams and penetration testers in exploiting large language model AI solutions. It provides tools and utilities to automate tasks related to AI-based security testing.FeaturesAutomate red teaming tasks using large language model AI solutionsPerform penetration testing with AI-powered toolsSupport for various security testing scenariosEasy-to-use Python interfaceInstallationYou can install aisploit using pip:pipinstallaisploitContributingContributions are welcome! If you have any ideas for new features, improvements, or bug fixes, feel free to open an issue or submit a pull request.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: ais-service-discovery
ais-service-discovery-pythonCloud Application FrameworkDescriptionThis repository interfaces Service Discovery, in this instance CloudMap, in order to locate and communicate with different services. As opposed to storing ARN's in environment variables, this library will interface CloudMap to find a service by a user-friendly naming convention and will understand what 'type' of service you've requested and use the correct code to communicate/call that service.Services supportedLambda (call).SNS (publish).SQS (queue).TODOLambda (request).SQS (listen).Http (request|call).Fargate/ECS Task (run).Note:This library requiresPython 3.5 and above.Examples:Lambda Callfromais_service_discoveryimportcallresponse=call('namespace','service','handler',{<Payload>})print(response)Lambda Async Callfromais_service_discoveryimportcallresponse=call('namespace','service','handler',{<Payload>},{'InvocationType':'Event'})print(response)
Package: aissist
AI AssistantAIssist is a simple, but capable command-line-interface (CLI) to chat with OpenAI's cutting-edge AI models.Install me withpip install aissist.  Set theOPENAI_API_KEYenvironment variable to be your OpenAI API key, then typeai.Why AIssist?This library was written to scratch a personal itch.  I have found GPT 3.5 to be incredibly useful in my day-to-day coding activities, but also found that having to go to a web browser and interact there was tedious.  What I really wanted was a tool that would:Be accessible from a simple command line prompt (ai)That single command should open up a shell to interact with ChatGPT, with the system already prompted to respond tersely but helpfully to coding questionsHave chat history accessible with the up arrowHave syntax highlighting on ChatGPT responsesHave multi-line editing capabilities on multi-line promptsThis library is precisely that.InstallationInstall using pypi withpipinstallaissistThere is one required environment variable,OPENAI_API_KEY, which should contain an OpenAI API key.aissistwill prompt you if the key is not set or is invalid.UsageInvoke the program withai.  Various pieces of configuration can be edited with command-line parameters, list these withai --help.Once launched, simply enter a prompt, and hit ESCAPE then ENTER to submit the prompt.Inside of each session there is a history, and you can use the up arrow to revisit and edit previous prompts.Here is a simple exampleConfigurationOn first run,aissistwill write a configuration file (.aissist) to your home directory.  This file contains a number of configuration options that can be edited.  Each configuration option matches precisely to a command-line argument.  The orde of precedence for taking an option value isCommand-line argument.aissistsettingDefault settingVersioningAIssist follows semantic versioning thus you should expect that:If the patch version is increased, only bugfixes have taken placeIf the minor version is increased, only additive changes have been made to the program.  Your existing workflows will work fine.If the major version is increased, the user may notice backward incompatible changes, or feature deprecations.
Package: aist
AIST = AI Service Tools公众号：人工智能AIST这个python模块需要配合我们的微信公众号使用。目前正在测试中。请联系me@feimax.com开通功能。目前支持的功能有：团队协作的图片收集首先需要一个人开通这项功能，然后会自动产生一个加入任务的二维码，分享这个二维码，就可以让其他人加入到这个图片采集任务。收集人只需要将图片发送到公众号，就会自动上传到云端存储。任务发起者可以获取下载密钥，在aist模块中，通过设置下载密钥，然后设置下载路径，就可以将云端的图片下载到本地了。OCR扫描使用者只需要将图片发送给本公众号，就可以得到OCR的结果了。数据通道该功能用于将科研代码中的数据实时推送到微信端，通过我们的公众号实时推送到你的微信。安装方法# 升级 pip 到最新版本
python -m pip install --upgrade pip
# 从 pip 安装 aist 模块
pip install aist使用方法获取图片fromaist.picimportDownloaddn=Download('PC80000000')# 获取该图片收集任务的图片列表，包含图片在云端的名称，数据大小和贡献者的wx_openidprint(dn.ls())# 获取该图片收集任务的图片列表，包含dn.ls('1.csv')# 下载所有的图片文件到 test 文件夹下去dn.all('test')数据通道fromaist.msgImportMsg# 这里要填写从 人工智能AIST 公众号里获取的数据通道的密钥。# 您需要练习 人工智能AIST 公众号来获取该项功能。msg=Msg('MS0000000')# push 方法直接将您想得到的信息直接通过我们的微信公众号推送给您。但是这种方法有两个限制：# 1、每天的推送不能超过1500条，请仅仅推送重要的信息。# 2、微信只允许在48小时之内发过信息给公众号的用户接受到公众号的推送。#    也就是说，如果您超过48小时没有给我们的公众号发送过任何信息，我们将没有权限推送信息给您。#    所以，为了确保您的使用，请经常发信息给公众号。msg.push('这是一条推送信息')# put 方法是将数据或者信息暂存，然后您任何时间都可以到我们的 人工智能AIST 公众号里去查询。# 这种方法暂时没有条数限制，如果不是重要的信息，最好使用该方法。该方法同时会帮你记录数据暂存的时间，使用GMT标准时间。请自行换算时区msg.put('这是一条正常信息')
Package: ai-stream-interact
AI Stream Interact🧠🎞️ - LLM interaction capabilities on live USB camera video stream.This package can easily be extended to accommodate different LLMs, but for this first version interactions were implemented only forGoogle's Gemini Pro & Vision Pro ModelsNote: This is a basic Alpha version that's been written & tested in Ubuntu Linux only so it may have unexpected behavior with other operating systems.Installation:pip install ai-stream-interactNote that pip install might take a while as it will also installcoqui-aifor Text to Speech. Although TTS is partially implemented it is not turned on by default due to some glitchy behavior. (This will be fixed in future releases.)Example Usage:You need a Gemini API key. (if you don't already have one you can get onehere).Have a USB camera connected.runaisi --llm geminito enter the AI Stream Interact🧠🎞️ main menu.(note that you can always go back to the main menu from the video stream by press "m" while having the video stream focused.)Enter the API key or press enter if you've added it to .env.You will be asked to enter your camera index. Currently there is no straight forward way to identify the exact index for your camera's name due to how open-cv enumerates such indicies so you'll have to just try a few times till you get the right one if you have multiple camers connected. If you have one camera connected you can try passing "-1" as in most cases it'll just pick that one.Now you're in!. You have access to 3 types of interactions as of today.Detect Default:This fires up a window with your camera stream and whenever you press "d" will identify the object the camera is looking at. (Make sure to press "d" with the camera window focused and not your terminal).Detect with Custom Prompt:Use this to write up a custom prompt before showing the model an object for custom interactions beyond just identifying objects.Interactions:This just allows for back & forth chat with the model.Troubleshooting:Errors:google.api_core.exceptions.FailedPrecondition: 400 User location is not supported for the API use.:This is specific to Gemini as they currently do not provide general availability to all regions, so you need to make sure your region is supportedhere