Saravanan666/pypi-org huggingface dataset:
Package: ahui
库的介绍及用法：简单介绍：库(ahui)由作者阿辉开发并维护，不定期发布更新版本查看最新版本>>该库的模块及函数覆盖实际工作中的多种场景的应用，函数的开发基于原标准库中的模块、调用第三方接口实现、原始函数的二次开发应用等本文档列举了库中所有模块下的函数及要点注解，更多详细教程可访问作者(阿辉)博客：CSDN知乎基本用法：安装库 (以下方法默认安装最新版本)：pip install ahui调用库或模块：import ahuifrom ahui import pyemail,pywechat注：因部分依赖库没有加入到默认安装列表，调用时可根据报错提示安装所缺少的<module>调用库(ahui)时默认导入各模块下的所有函数，故可以直接调用库下的函数，示例如下：importahuiahui.Email_send(username='your_email',password='email_password',subject='test',contents='test...',receivers=['receiver_email'],accs=[],links={},df=None,df_links={},file_pathname=None,smtp='exmail',ssl=True)一、Python 发送邮件的多种场景的应用0.邮箱-SMTP服务器地址/端口号(SSL加密)/端口号(非加密)：注：qq(腾讯QQ邮箱)、exmail(腾讯企业邮箱)、126和163(网易邮箱)smtp_servers(smtp='exmail',ssl=True)1.发送邮件信息：参数：[file_pathname]类型(str/list) 支持添加多个附件-文件格式不限、附件总大小不超过50M；[contents]类型(str/list) 支持正文内容换行(列表中一个元素代表一行)；[links]设置超链接-支持添加多个；[df_links]支持设置多个链接字段，默认为空。设置：[QQ邮箱发送]用于发送邮件的qq邮箱需要开启SMTP服务，进入邮箱-设置-账户-开启(POP3/SMTP)验证-获取授信码(授信码用于邮箱登录密码)Email_send(username='',password='',subject='',contents='',receivers=[],accs=[],links={},df=None,df_links={},file_pathname=None,smtp='exmail',ssl=True)2.发送邮件报告：功能：支持正文插入1张图片(png/jpg/gif动图); 支持添加多个附件(文件格式不限)参数：[image_name]支持类型(本地图片/变量Image/变量bytes)；[file_pathname]类型(str/list) 支持添加多个附件-文件格式不限、附件总大小不超过50M；[contents]类型(str/list) 支持正文内容换行(列表中一个元素代表一行)说明：插入动图浏览器中首次打开正常，但手机端需要二次打开邮件才显示动图(刷新邮件)Email_image(username='',password='',title='',contents='',image_name=None,receivers=[],accs=[],file_pathname=None,smtp='exmail',ssl=True,sign=False)二、企业微信群机器人-推送多类型消息的应用0.配置企业微信群机器人key：wechat_key(key='key1')1.1.企业微信群机器人-推送消息类型-markdown：参数：[content]支持传入字符串(str)/列表(list)/数据框(DataFrame)；[df_links]支持多个链接字段，默认参数空；说明：只支持@1个人/不支持@all；@对象为企业微信英文名，英文名不区分大小写；若@对象无此人仍正常推送消息内容；系统：适用于 Windows、Linux 系统环境下的Python3版本。wechat_markdown(top='',title='',content='',user='',links={},df_links={},show_cols=False,key=None)1.2.企业微信群机器人-推送消息类型-markdown：简介：基于 wechat_markdown() 函数的二次封装开发；功能上支持@多人、支持推送多个群；参数：[user]支持传入数据类型str/list； [key]支持数据类型str/list，当传入多个元素则用列表形式。wechat_markdowns(top='',title='',content='',user='',links={},df_links={},show_cols=False,key=None)2.企业微信群机器人-推送消息类型-text：功能：@对象支持使用企业微信英文名或手机号、支持 '@all' 或 @多个人；参数：[content]支持数据类型str/list，列表中的每个元素文本代表一行。wechat_text(top='',title='',content='',users_name=[],users_phone=[],key=None)3.企业微信群机器人-推送消息类型-@群成员：功能：@对象支持使用企业微信英文名或手机号，英文名不区分大小写；支持 '@all' 或 @多个人，若无此人则不@且不影响执行推送。wechat_at(users_name=[],users_phone=[],key=None,content='')4.企业微信群机器人-推送消息类型-图像：参数：[image]支持传入的数据类型-本地图片路径/变量Image/变量bytes说明：图片最大不能超过2M，支持JPG、PNG格式。安装：PIL(pip install pillow)wechat_image(image,key=None,users=[],content='')5.企业微信群机器人-推送消息类型-本地文件：参数：[pathfile]传参要求，图片:最大10MB 支持JPG、PNG格式；语音:最大2MB 播放长度不超过60s 支持AMR格式；视频:最大10MB 支持MP4格式；普通文件:最大20MBwechat_file(pathfile,key=None,users=[],content='')
Package: ahui_aiohttp_server
Ahui-aiohttp-serverThis is a simple http async server which extendspython -m http.server.(WARN: It's not recommended  for production environments, just use it  in development or testing environments only!):Support print to http response directly (same as php's echo)Support async-awaitSupport php, pythonInstallpip install ahui-aiohttp-server
pip3 install ahui-aiohttp-serverStart server$ tree .
./
    app/
        echo1.py
        echo2.py
        echo.php
        return.py
    js/
        test.js
$ python -m ahui_aiohttp_server
$ python -m ahui_aiohttp_server --host 127.0.0.1 --port 5000Access serverAccess via echo server(php-like):$ cat app/echo1.py
print('Hello World!')

$ curl http://127.0.0.1:5000/app/echo1.py
Hello World!
$ curl http://127.0.0.1:5000/js/test.js
<js content>
$ curl http://127.0.0.1:5000/app/echo.php
<js content>If you want to get request data(such as:get, post, cookie, ..., useaiohttp_handler(request)instead:$ cat app/echo2.py
def aiohttp_handler(request):
    print(request.query)    # use print 

$ curl http://127.0.0.1:5000/app/echo2.py?var=value
{'var':'value'}Access via normal aiohttp server:$ cat app/return.py
from aiohttp import web
async def aiohttp_handler(request):
    data = await request.post()
    return web.Response(body=str(data)) # use return

$ curl http://127.0.0.1:5000/app/return.py?var=value - 'k1=v2'
{'k1':'v2'}Access static file:$ curl http://127.0.0.1:5000/js/test.js
<js content>Requiredaiohttppython>=3.6
Package: ahuiwechat
企业微信群机器人-推送消息类型的多种应用企业微信群机器人key：可以通过函数 wechat_key() 定义默认的群机器人key1.1.企业微信群机器人-推送消息类型-markdown：wechat_markdown(top='', title='', content='', user='', links={}, df_links={}, show_cols=False, key=None)参数：[content]支持传入字符串(str)/列表(list)/数据框(DataFrame)；[df_links]支持多个链接字段，默认参数空；说明：只支持@1个人/不支持@all；@对象为企业微信英文名，英文名不区分大小写；若@对象无此人仍正常推送消息内容；系统：适用于 Windows、Linux 系统环境下的Python3版本。1.2.企业微信群机器人-推送消息类型-markdown：wechat_markdowns(top='', title='', content='', user='', links={}, df_links={}, show_cols=False, key=None)简介：基于 wechat_markdown() 函数的二次封装开发；功能上支持@多人、支持推送多个群；参数：[user]支持传入数据类型str/list； [key]支持数据类型str/list，当传入多个元素则用列表形式。2.1.企业微信群机器人-推送消息类型-text：wechat_text(top='', title='', content='', users_name=[], users_phone=[], key=None)功能：@对象支持使用企业微信英文名或手机号、支持 '@all' 或 @多个人；参数：[content]支持数据类型str/list，列表中的每个元素文本代表一行。3.1.企业微信群机器人-推送消息类型-@群成员：wechat_at(users_name=[], users_phone=[], key=None, content='👆')功能：@对象支持使用企业微信英文名或手机号，英文名不区分大小写；支持 '@all' 或 @多个人，若无此人则不@且不影响执行推送。4.1.企业微信群机器人-推送消息类型-图像：wechat_image(image, key=None, users=[], content='👆')参数：[image]支持传入的数据类型-本地图片路径/变量Image/变量bytes；说明：图片最大不能超过2M，支持JPG、PNG格式。5.1.企业微信群机器人-推送消息类型-本地文件：wechat_file(pathfile, key=None, users=[], content='👆')参数：[pathfile]传参要求，图片:最大10MB 支持JPG、PNG格式；语音:最大2MB 播放长度不超过60s 支持AMR格式；视频:最大10MB 支持MP4格式；普通文件:最大20MB5.2.企业微信群机器人-推送消息类型-本地文件：wechat_files(pathfiles, key=None, users=[], content='👆')简介：基于 wechat_file() 函数的二次封装开发；功能上支持批量推送多个文件。
Package: ahura
a God like Serializer for God like Developers.Ahura is an Automatic easy to use Django serializer. it comes With diverse features designed to fit in all different occasions. Feel free to contribute in case of detecting issues or if any new ideas came to your mind.PrerequisitesAhura is a Django model serializer for now, so you must have Django installed and use Django ORM.InstallingFor using Ahura you just need to install it using pip.$pipinstallahuraor clone the project. then import the Serializer and use it like the example
below.>>>fromahuraimportSerializer>>>frommyapp.modelsimportMyModel>>>my_objects=MyModel.objects.all()>>>s=Serializer(exclude=["password"],date_format="%Y-%m-%d")>>>data=s.serialize(my_objects)Getting Startedusing ahura is easy as you can see above while it gives you many different options
to modify your model’s json. let’s go through some of these options.this  part is not complete but we will add the documention very soonContributingPlease readCONTRIBUTING.mdfor details on our code of conduct, and the process for submitting pull requests to us.AuthorsMahdi Sorkhemiri-Initial work-SorkhemiriMohammad Rabetian-Initial work-RabetianSee also the list ofcontributorswho participated in this project.LicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details
Package: ahuri-cli
ahuri-cli v1.1.2Use Ahuri from the command line!This repository contains the source code forahuri-cli.View thePyPI repositoryfor this CLI app.How to use this app?RequirementsYou needPython 3.8+with pip on path.InstallationOn Linux / MacOSOpen a terminal and type:$pipinstallahuri-cliOn WindowsOpen a terminal and type:>pipinstallahuri-cli[windows]UsageYou can use the commandahurito use the app.Useahuri -hto view the main help page.NOTE:If the commandahuriis not found, it's probably not on path. If python or python3 is on path, you can use the commandpython -m ahuriorpython3 -m ahurito use the app.
Package: ai1wm
Pack/Unpack All-in-One WP Migration PackagesThis library provides helper classes for packing/unpacking WordPressAll-in-One WP Migrationpackages.ExamplesUnpack a Filefromai1wmimportAi1wmPackagepackage=Ai1wmPackage('/path/to/the/destination/dir')package.unpack_from('/path/to/the/source/wpress/file')Pack a Directoryfromai1wmimportAi1wmPackagepackage=Ai1wmPackage('/path/to/the/source/dir')package.pack_to('/path/to/the/destination/wpress/file')
Package: ai21-tokenizer
AI21 Labs TokenizerA SentencePiece based tokenizer for production usesInstallationpippipinstallai21-tokenizerpoetrypoetryaddai21-tokenizerUsageTokenizer Creationfromai21_tokenizerimportTokenizertokenizer=Tokenizer.get_tokenizer()# Your code hereAnother way would be to use our Jurassic model directly:fromai21_tokenizerimportJurassicTokenizermodel_path="<Path to your vocabs file. This is usually a binary file that end with .model>"config={}# "dictionary object of your config.json file"tokenizer=JurassicTokenizer(model_path=model_path,config=config)FunctionsEncode and DecodeThese functions allow you to encode your text to a list of token ids and back to plaintexttext_to_encode="apple orange banana"encoded_text=tokenizer.encode(text_to_encode)print(f"Encoded text:{encoded_text}")decoded_text=tokenizer.decode(encoded_text)print(f"Decoded text:{decoded_text}")What if you had wanted to convert your tokens to ids or vice versa?tokens=tokenizer.convert_ids_to_tokens(encoded_text)print(f"IDs corresponds to Tokens:{tokens}")ids=tokenizer.convert_tokens_to_ids(tokens)For more examples, please see ourexamplesfolder.
Package: ai2d
企联AI DevConnectAI定位为一个帮助公司更快地建立AI内部工具的平台产品背景由于各个公司的内部管理流程不同，会有很多无法标准化的长尾AI需求企业内部技术团队需要将1/3的时间用来开发维护这些内部AI工具如果能将低代码用于AI内部工具的开发，无疑将极大地提升企业技术团队的效率既然AI需求是无法标准化的，那么就标准化这些长尾AI需求的开发工具以低代码的方式提高内部AI工具开发效率
Package: ai2e
企联AI DevConnectAI定位为一个帮助公司更快地建立AI内部工具的平台产品背景由于各个公司的内部管理流程不同，会有很多无法标准化的长尾AI需求企业内部技术团队需要将1/3的时间用来开发维护这些内部AI工具如果能将低代码用于AI内部工具的开发，无疑将极大地提升企业技术团队的效率既然AI需求是无法标准化的，那么就标准化这些长尾AI需求的开发工具以低代码的方式提高内部AI工具开发效率
Package: ai2-kit
ai2-kitA toolkit featuredartificialintelligence ×abinitiofor computational chemistry research.Please be advised thatai2-kitis still under heavy development and you should expect things to change often. We encourage people to play and explore withai2-kit, and stay tuned with us for more features to come.Feature HighlightsCollection of tools to facilitate the development of automated workflows for computational chemistry research.Utilities to execute and manage jobs in local or remote HPC job scheduler.Utilities to simplified automated workflows development with reusable components.InstallationYou can use the following command to installai2-kit:# for users who just use most common featurespipinstallai2-kit# for users who want to use all featurespipinstallai2-kit[all]If you want to runai2-kitfrom source, you can run the following commands in the project folder:pipinstallpoetry
poetryinstall

./ai2-kit--helpNote that instead of running globalai2-kitcommand, you should run./ai2-kitto run the command from source on Linux/MacOS or.\ai2-kit.ps1on Windows.ManualsFeaturing Toolsai2-cat: A toolkit for dynamic catalysis researching.WorkflowsCLL MLP Training Workflow(zh)Domain Specific ToolsProton Transfer Analysis ToolkitAmorphous Oxides Structure Analysis ToolkitGeneral ToolsTips: useful tips for usingai2-kitBatch Toolkit: a toolkit to generate batch scripts from files or directoriesASE Toolkit: commands to process trajectory files withASEdpdata Toolkit: commands to process system data withdpdataNotebooksai2catMiscellaneousHPC Executor Introduction(zh): a simple HPC executor for job submission and managementCheckpoint MechanismAcknowledgementThis project is inspired by and built upon the following projects:dpgen: A concurrent learning platform for the generation of reliable deep learning based potential energy models.ase: Atomic Simulation Environment.
Package: ai2py
#AI2PYAI2PY is an OpenAI-powered Python module created to help developers easily integrate artificial intelligence into their Python projects. AI2PY provides a comprehensive set of tools for creating AI-generated text, images, and image variations. An OpenAI API Key is required.The module contains functions to create text, create text commands, create images, and create image variations. The init function initializes the class with an api_key. The create_text function takes in data and a word count and uses the OpenAI API to generate text based on the data. The create_text_command function takes in a command, data, and a word count and uses the OpenAI API to generate text based on the command and data. The create_image function takes in prompt text and an image size and uses the OpenAI API to generate an image based on the prompt text. The create_image_variation function takes in data and an image size and uses the OpenAI API to generate an image variation based on the data. The options class contains two functions: display_imagesizes and image_resize. The display_imagesizes function prints out the required image sizes for the OpenAI API. The image_resize function takes in an image and a size and resizes the image to the specified size.In addition, AI2PY also provides a set of pre-trained models that can be used for various tasks such as natural language processing, computer vision, and more. These models can be used to quickly build AI applications without having to train your own models from scratch.If you have any questions, feel free to ask them in the GitHub Discussion tab. If you need help with a project or want to show off a project made with AI2PY, you can also do so in the Discussion tab. If you find a bug, please report it in the GitHub Issues tab and provide the complete error message and a minimal example code that reproduces the error. If you have ideas for improvements or requests for new functions, you can also report these in the GitHub Issues tab, but please check for previous requests to prevent duplicates.
Package: ai2thor-colab
Run AI2-THOR on the Cloud using Google Colab💡 Templates💪 Full Starter TemplateTo get started, we recommend saving a copy of theAI2-THOR Colab Full Starter Templateto your drive. It goes over many helper functions that are often useful.https://user-images.githubusercontent.com/28768645/119420726-06d8bc80-bcb2-11eb-9acf-e9b151121506.mp4👑 Minimal Starter TemplateWe also provide aMinimal Starter Templatethat does not showcase any helper functions. This is often useful as a starting point to minimally reproduce issues, highlight, or test functionality.🐱‍💻 Setup Overview💻 InstallationUsing Python's packaging manager,ai2thor_colabcan be installed withpipinstallai2thor_colab🔥 Start X ServerAI2-THOR requires an X Server to run on a Linux machine. It allows us to open a Unity window where we can render scenes and observe images. Colab runs Linux, but it does not start an X Server by default. Usingai2thor_colab.start_xserver(), we can install all required X Server dependencies and start it up:importai2thor_colabai2thor_colab.start_xserver()💬 SupportQuestions.If you have any questions on AI2-THOR, please ask them onAI2-THOR's GitHub Discussions Page.Issues.If you encounter any issues while using AI2-THOR, please open anIssue on AI2-THOR's GitHub. If you encounter an issue with AI2-THOR Colab, please open anIssue on our GitHub🏫 Learn moreSectionDescriptionAI2-THOR WebsiteThe AI2-THOR website, which contains extensive documentation on using the API.AI2-THOR GitHubContains the source code and development of AI2-THOR.AI2-THOR DemoInteract and play with AI2-THOR live in the browser.👋 Our TeamAI2-THOR and AI2-THOR Colab are open-source projects built by thePRIOR teamat theAllen Institute for AI(AI2).
AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.
Package: ai2xl
ai2xlis a freePythonlibrary that allows usingExcelforML data preparation, zero dependencyML model deployment,model debuggingandmodel explainability.Data Scientistscan use ai2xl to make their own lives easier, and also tocollaboratewithData Analysts,Business Analystsand more. There are an estimated1 billion+Excel, Google Sheets, LibreOffice or similar Spreadsheet users in the World. ai2xl aims to bridge the gap between Data Scientist / Python world and the billion+ Business / Excel users.You can read more about ai2xl here:https://ai2xl.github.ioFor getting started and other information:https://ai2xl.readthedocs.ioSample Jupyter notebooks:https://ai2xl.github.io/samplesEnjoy!
Package: ai2xlcore
ai2xlis a freePythonlibrary that allows usingExcelforML data preparation, zero dependencyML model deployment,model debuggingandmodel explainability.Data Scientistscan use ai2xl to make their own lives easier, and also tocollaboratewithData Analysts,Business Analystsand more. There are an estimated1 billion+Excel, Google Sheets, LibreOffice or similar Spreadsheet users in the World. ai2xl aims to bridge the gap between Data Scientist / Python world and the billion+ Business / Excel users.You can read more about ai2xl here:https://ai2xl.github.ioFor getting started and other information:https://ai2xl.readthedocs.ioSample Jupyter notebooks:https://ai2xl.github.io/samplesEnjoy!
Package: ai4ao
AI for Anomaly and Outlier detection (AI4AO)AI4AO is a Python package that allows to build any of thescikit-learnsupported Clustering and Classification algorithms
based machine learning models in batches. This means that one can useyamldeclarative
syntax in order to write a configuration file, and based on the instructions in the configuration file, and the
machine learning models will be constructed sequentially. This way many models can be built with a single configuration
file with the results being arranged in an extremely modular way. AI4AO can be considered as a convenient wrapper for
scikit-learn models.Developed by:Selvakumar UlaganathanWebsite:www.selvai.comPyPI Project:https://pypi.org/project/ai4aoGitLab Source:https://gitlab.com/selvai/ai4aoOfficial Documentation:https://selvai.gitlab.io/ai4ao/UsageDefine a configuration inconfig.yaml# config.yamlIsolationForest_0.01:project_name:timeseries_anomalyrun_this_project:Truemulti_variate_model:Truemodel:IsolationForestdata:path:'path-to-train-data.csv'test_data_path:'path-to-train-data.csv'features_to_avoid:['feat-to-avoid']hyperparams:contamination:0.01results:path:'results/isolation_forest_001/'remote_run:FalseRun the model defined inconfig.yaml# example_script.pyimportai4ao# import packagefromai4ao.modelsimportSKLearnModelasModel# scikit-learn wrapper# fit and evaluate modelmodel=Model(plot_results=True)model.batch_fit(path_config='configs.yaml')# print models and metricsprint(model.models)print(model.metrics())
Package: ai4flwr
ai4-flwrThis repository contains theAI4OSextensions for
theFlowerframework.AuthenticationAuthentication for Flower is implemented directly via GRPC: interceptors
(server side) and authentication medatata plugins (client side).In order to use it, the server must be initialized with any object of theai4flwr.authpackage as interceptor. See the examples below for more
details.Bearer token authenticationIn your server, start it as follows:import ai4flwr.auth.bearer

fl.server.start_server(
    server_address="0.0.0.0:5000",
    certificates=(...),
    interceptors=[ai4flwr.auth.bearer.BearerTokenInterceptor()]
)In your client, start it as follows:import ai4flwr.auth.bearer

token = "Your token as configured in the server"

fl.client.start_numpy_client(
    server_address=f"localhost:5000",
    client=...,
    root_certificates=...
    call_credentials=grpc.metadata_call_credentials(
        ai4flwr.auth.bearer.BearerTokenAuthPlugin(token)
    ),
)
Package: ai4-metadata-validator
AI4 Metadata validatorMetadata validator for the AI4OS hub data science applications.Motivationai4-metadata-validatorvalidates the metadata used by the AI4OS hub models
and applications.ImplementationTheschemahas been implemented according toJSON
schema specification(Draft 7), usingPython's
jsonschemamodule.Once installed, theai4-metadata-validatorCLI tool is provided, which
accepts schema instance files as input parameters.Installation$ pip install ai4-metadata-validatorUsage$ ai4-metadata-validator instances/sample.mods.json
Package: ai4scr-athena
ATHENA is an open-source computational framework written in Python that facilitates the visualization, processing and analysis of (spatial) heterogeneity from spatial omics data. ATHENA supports any spatially resolved dataset that contains spatial transcriptomic or proteomic measurements, including Imaging Mass Cytometry (IMC), Multiplexed Ion Beam Imaging (MIBI), multiplexed Immunohistochemisty (mIHC) or Immunofluorescence (mIF), seqFISH, MERFISH, Visium.
Package: ai4vl-video-tools
ai4vl video toolsVideo handling tools for ai4vl.Install it from PyPIpipinstallai4vl-video-toolsUsageThe tools expects a directory with video files as input and outputs a directory with the converted videos.
The source directory must have the following structure:source_dir
└──some_study_name├──1-1# these are patient number - case number│├──snipped01.mp4│├──snipped02.mp4│└──snipped03.mp4└──2-2└──snipped04.mp4Run the help command to see the usage. A basic example is:$python-mvideo_tools-vaggregate-itests/data/-otests/data/output-s1
Package: ai69
ai69is a Python package to dynamically generate and execute Python functions for any occation. It's designed to simplify the process of integrating AI-powered functions for anything you want.InstallationTo installai69, run the following command in your terminal:pipinstallai69Ensure you have Python 3.6 or later installed on your system.UsageSetting UpFirst, importai69and set your OpenAI API key:fromai69importaiai.set_key("your-openai-api-key")[!IMPORTANT]
 You must obtain an API key from OpenAI. You can get it fromhere.Alternatively, If you have an environment variableOPENAI_API_KEYset, you need not useai.set()to set up the key. ai69 will auto import the key for youCalling FunctionsWithai69, you can call functions dynamically. The package will attempt to generate the appropriate Python code using OpenAI's Codex:fromai69importaiawaitai.getWeather('Chennai')# 'sunny'awaitai.randomNumberBetween(1,10)#6awaitai.slugify('My Article')# 'my-article'awaitai.hasProfanityRegex('f*ck this lol')# Falseawaitai.extractHashtags('this is #really cool! #ai #code')# ['really', 'ai', 'code']awaitai.getProgrammerJoke()# 'What do you call a programmer from Finland? Nerdic.'FeaturesDynamic Function Generation:Create functions on the fly based on method names and arguments.AI-Powered Code Generation:Utilizes OpenAI's GPT-3.5 Turbo model for generating Python code.Flexible and Easy to Use:Designed to be intuitive and straightforward, requiring minimal setup.Important NotesSecurity:Executing dynamically generated code can be risky. Always validate and sanitize inputs and useai69in a secure environment.API Key:Your OpenAI API key should be kept confidential. Do not expose it in publicly accessible areas like GitHub repositories.[!NOTE]
The responses from the AI can vary, and the generated code's quality depends on the model's current capabilities and understanding.Disclaimerai69is an experimental tool that relies on AI to generate code. The developers ofai69are not responsible for any consequences arising from the use of this package, including but not limited to generated code quality, security vulnerabilities, or AI model inaccuracies.Currently only OpenAI's GPT-3.5 Turbo model is supported. Support for other models may be added in the future. For this reason, we use openai's api rather than openai's python package.UwU wetotally* recommend you to use in productionlmaowContributingContributions toai69are welcome! Please feel free to open a pull request or issue on GitHub. All contributions must be released under theMIT.ai69is released under theMIT License.