Saravanan666/pypi-org huggingface dataset:
Package: aityz-chess
# Aityz Chess
## Features
Aityz Chess, at it’s core is a chess.com API wrapper. The main parts of the program are the analysis and stockfish sub-modules that can be used to analyse games using a customPGNGeneratorclass.
## Documentation
Aityz Chess uses PDoc3 auto generated documentation. It is hosted [here](https://chess.aityz.repl.co).
## Installation
You can install Aityz Chess using Pip! Just typepip install aityz_chessorpip3 install aityz_chessdepending on your operating system to install!
## Contributing
More analysis functions, and scraping methods would be appreciated, and definitely better documentation and docstrings. I am currently migrating some of the code into sub-modules, and still isn’t complete (using right types etc).
Package: aiutil
AI/ML Utils  |@GitHub|@PyPIThis is a Python pacakage that contains misc utils for AI/ML.Misc enhancement of Python's built-in functionalities.stringcollectionspandas DataFramedatetimeMisc other toolsaiutil.filesystem: misc tools for querying and manipulating filesystems; convenient tools for manipulating text files.aiutil.url: URL formatting for HTML, Excel, etc.aiutil.sql: SQL formattingaiutil.cv: some more tools (in addition to OpenCV) for image processingaiutil.shell: parse command-line output to a pandas DataFrameaiutil.shebang: auto correct SheBang of scriptsaiutil.poetry: tools for making it even easier to manage Python project using Poetryaiutil.pdf: easy and flexible extracting of PDF pagesaiutil.memory: query and consume memory to a specified rangeaiutil.notebook: Jupyter/Lab notebook related toolsaiutil.dockerhub: managing Docker images on DockerHub in batch mode using Pythonaiutil.hadoop:A Spark application log analyzing tool for identify root causes of failed Spark applications.Pythonic wrappers to thehdfscommand.A auto authentication tool for Kerberos.An improved version ofspark_submit.Other misc PySpark functions.Supported Operating Systems and Python VersionsPython 3.10.x on Linux and macOS.
It might work on Windows but is not tested on Windows.Installationpip3install--user-UaiutilUse the following commands if you want to install all components of aiutil.
Available additional components arecv,docker,pdf,jupyter,adminandall.pip3install--user-Uaiutil[all]
Package: aiv
AIV: Annotation of Identified VariantsAnnotation of Identified Variants to Create Reports for Clinicians to Assist Therapeutic DecisionsPrerequisitesIt requires three main modules: pandas, myvariant and reportlabpip install pandas
pip install myvariant
pip install reportlabInstallationpip install aivUpgradepip install aiv --upgradeUsageimportaiv#Getvariantinfoaiv.getvariant('chr1',69635,'G','C')#Annotatevariants,referencegenome:hg38aiv.annotate_mutations('variant_calls.tsv',assembly='hg38')#Annotatevariants,referencegenome:hg19aiv.annotate_mutations('bwa_mutect2_nb09_50_lines.txt',assembly='hg19')TestsYou can test your installation with sample variant call files. Input test files can be found at:./tests/test_annotate_variants.tsv
./tests/bwa_mutect2_nb09_50_lines.txt
./tests/my_data.txtInput File FormatReport PreviewFuture WorkPerformance can be determined by calculating the running time for a given input file with 6000+ mutations.
Package: ai-voice-sdk
AI VOICE SDK目錄簡介需求開始使用安裝教學課程簡介AI Voice是宏正自動科技的語音合成服務優聲學，使用本SDK是必須租用優聲學服務。租用服務請至https://www.aivoice.com.tw上留下聯絡資料。宏正優聲學，推出限量企業體驗版之語音合成服務，提供六個優質美聲，大量語音合成，歡迎企業用戶填寫表格連繫, 了解更多企業體驗版方案細節!需求Pythonpython >= 3.7支援版本API == v1.x開始使用安裝透過pip安裝SDKpipinstallai-voice-sdk手動安裝SDKgitclonehttps://github.com/ATEN-International/ai-voice-sdk-python.gitcdai-voice-sdk-python
python-mpipinstallwheel
pythonsetup.pysdistbdist_wheel# 建立SDK安裝檔pipinstalldist/ai_voice_sdk-x.x.x-py3-none-any.whl# 安裝SDK，其中 'x.x.x' 填入現在的版本號教學課程tutorial
Package: aivoifu
AIwaifu Vocal Pipelinean implementation of AIwaifu Vocal Pipeline to make it easier to create a fast and easy-to-use Cute Waifu Voice
Text -> TTS -> Voice Conversion -> DoneUsage# pip install aivoifupoetryinstallaivoifu# Recommended using poetryfromAIvoifuimportclient_pipelinemodel=client_pipeline.tts_pipeline(tts_model_selection='gtts',vc_model_selection='ayaka-jp',hubert_model='zomehwh-hubert-base',language='en')model.tts('Hello This Is A Test Text Anyway',save_path='./test.wav')How to add your own TTS/Voice Conversion pipelineFirst of all we need to understand how AIwaifu Vocal Pipeline was design
There's two componentsTTS: This is a typical text to speech model which you can add your ownVoice_Conversion: This is our Heroine on making TTS sound like a cute girl by converting TTS speech using Voice Conversion which you can train easilyFirst of all we get TTS voice from TTS pipeline, adn the we do voice conversion on itCustom TTSin the TTS folder you'll foundtts_base_model: foldertts.pyif you wish to add your own TTS model please create a class in tts.py
and in the function constructor please download the weight and cache it
under the tts_base_model along with other cache file# Example codeclassCustomTTS:def__init__(self)->None:fromsomethingimporttts_libraryself.model_link='Huggingface_link'root=os.path.dirname(os.path.abspath(__file__))self.model_root_path=f'{root}/base_tts_model/'self.model_name='custom_tts'model_path=f'{self.model_root_path}/{self.model_name}'ifnotos.path.exist(model_path)os.mkdir(model_path)weigh_path=f'{model_path}/{self.model_name}.pth'ifnotos.path.exist(weigh_path):wget.download(self.model_link,weigh_path)self.model=tts_library.load(weigh_path)print(f'model{self.model_name}initialized')deftts(self,text:str,save:boolean=True,your_args:any):# some preprocessingoutput=self.model.tts(text)ifsave:output.save('save_path')returnoutputCustom Voice Conversionif you're not looking to add new language but just want to custom the model voice easilyOur recommendation is to just Train Voice Conversion pipeline on your own samples and added it to the zoo
Package: aix
aixArtificial Intelligence eXtensionsFast access to your favorite A.I. tools.To install:pip install aixExamplesWant all your faves at your fingertips?Never remember where to import that learner from?SayLinearDiscriminantAnalysis?... was itfrom sklearn?... was itfrom sklearn.linear_model?... ah no! It wasfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis.Sure, you can do that. Or you can simply typefrom aix.Lin...click tab, and; there it is!
Select, enter, and moving on with real work.Note: This is meant to get off the ground quickly
-- once your code is stable, you should probably import your stuff directly from it's originComing upNow that the AI revolution is on its way, we'll add the ability to find, and one day,
use the right AI tool -- until the day that AI will do even that for us...
Package: aixapi
AIx GPT APIFree tier uses a CPU. GPU access costs just $8 / month. This speeds up responses by 10 seconds or more. Upgradeherefor faster AI responses.Submit issues and feature requests for our API here.Seehttps://apps.aixsolutionsgroup.comfor more info.Python Quick Startpip install aixapipip install requestsGet an API key for free athttps://apps.aixsolutionsgroup.com.from aixapi import AIxResourceresult = AIxResource("MY_API_KEY").compose("hello!")print(result)Python DocumentationTo get documentation in python, runhelp(AIxResource)after importingAIxResourceas shown above.DocumentationFor full documentation go tohttps://apps.aixsolutionsgroup.comand click on the Documentation tab.
Package: aixd
AI-eXtended Design (AIXD)IntroductionIn the current repository we collect the code for the general methodology for AI-augmented generative design. This methodology allows to invert the standard paradigm of parametric modelling, where the designer needs to tweak and tune the input parameters, iteratively or through trial and error, for achieving some desired performance values.Instead, this method allows to, by just specifying the requirements' values, obtain a range of designs that closely approximate those. Besides, the present methodology allows the user to explore the design space, understand how different parameters relate to each other, areas of feasible and unfeasible designs, etc.DocumentationA detailed documentation of theaixdlibrary is providedhere. The documentation includes detailed installation instructions, API references, a user guide, application examples and more.InstallationInstall usingconda:conda env create -f environment.ymlThis creates a conda environment calledaixdwith python 3.9 and all the dependencies defined inrequirements.txtas well as installing theaixdpackage itself in editable mode.DevelopmentIf you are going to develop on this repository, also install the development requirements:pip install -e ".[examples, dev]"Check thecontribution guidelinesfor more details.Folders and structureThe structure we follow on the current repo is as follows:examples: all example applications of theaixdtoolboxsrc: for all source code. It can be structure following the next structuressrc/aixd: source code ofaixdtoolboxKnown issuesPlotly image export can cause a hang of the system. This is due to a bug in Kaleido (the library
used by Plotly for image export) reported inhere. A workaround is to
downgrade Kaleido to version0.1.0.post1, which can be done by runningpip install kaleido==0.1.0.post1.
Package: aiXplain
aiXplainaixplain is a software development kit (SDK) for theaiXplainplatform. With aixplain, developers can quickly and easily:DiscoveraiXplain’s ever-expanding catalog of 35,000+ ready-to-use AI models and utilize them.BenchmarkAI systems by choosing models, datasets and metrics.Designtheir own custom pipelines and run them.FineTunepre-trained models by tuning them using your data, enhancing their performance.🔎Findmodels,datasets,metricson the platform.💛 Our repository is constantly evolving. With the help of the scientific community, we plan to add even more datasets, models, and metrics across domains and tasks.Getting StartedInstallationTo install the base package, simply,pipinstallaixplainTo  install aiXplain with additional model building support:pipinstallaixplain[model-builder]API Key SetupBefore you can use the aixplain SDK, you'll need to obtain an API key from our platform. For details refer thisTeam API Key Guide.Once you get the API key, you'll  need to add this API key as an environment variable on your system.Linux or macOSexportTEAM_API_KEY=YOUR_API_KEYWindowssetTEAM_API_KEY=YOUR_API_KEYJupyter Notebook%env TEAM_API_KEY=YOUR_API_KEYUsageLet’s see how we can use aixplain to run a machine translation model. The following example shows anEnglish to French translation model.fromaixplain.factoriesimportModelFactorymodel=ModelFactory.get("61dc52976eb5634cf06e97cc")# Get the ID of a model from our platform.translation=model.run("This is a sample text")# Alternatively, you can input a public URL or provide a file path on your local machine.Check out theexplore sectionof our guide on Models to get the ID of your desired modelQuick LinksTeam API Key GuideUser DocumentationDeveloper GuideAPI ReferenceRelease notesSupportRaise issues for support in this repository.Pull requests are welcome!NoteTheaiXtendpython package was renamed toaiXplainfrom the release v0.1.1.
Package: aixplain-pipelines
aiXplain PipelinesaiXplain Pipelines enables python programmers to add AI functions
to their software.An aiXplain pipeline is a directed acyclic graph (DAG) of AI functions built using aiXplain's designer UI. An AI function is a data processing step that relies on a machine learning model to execute. An example of an AI function is speech recognition or machine translation. It helps you process your data by calling a series of functions as defined in the DAG, abstracting the orchestration by providing a simple python function call.aiXplain has a collection of AI models for each AI function. You can explore the collection of our AI models by using the discover feature of ourplatform's website.aiXplain Pipeline Designer DAGThe image below shows a sample aiXplain pipeline built for subtitling video files. The description of the pipeline can be found in thedocumentation.Installationpip install aixplain-pipelinesUser GuideIn order to use aiXplain pipelines, you need to create an account inaiXplain platform. Follow the code samples listed below to get started.Code Samples and Demosaixplain-pipelines provides python APIs to call AI workflows you can build with aiXplain designer.Generic Snippetfrom aixplain_pipelines import Pipeline

api_key=<API_KEY>

pipe = Pipeline(api_key=api_key)

path=<DATA_URL>
response = pipe.run(data=path)API_KEY can be obtained by creating a pipeline in pipeline designer through the aiXplain platform UI.For DATA_URL generate a http(s) link to your image or video file to process, though text input can be directly supplied to data parameter in the run function without needing a URL.Information on how to generate the API_KEY can be found in thesubtitle generation pipeline sample video.Subtitle GenerationThis demo creates a .srt file for the supplied video using aixplain-pipelines. Follow the instructions in thedocumentation.Developer GuideFollow the developer guidedocumentation.SupportRaise issues for support in this repository.Pull requests are welcome!
Package: aixtend
aiXtendaiXtend is a software development kit (SDK) for theaiXplainplatform. With aiXtend, developers can quickly and easily:DiscoveraiXplain’s ever-expanding catalog of 35,000+ ready-to-use AI models and utilize them.BenchmarkAI systems by choosing models, datasets and metrics.Designtheir own custom pipelines and run them.🎓Documentation🔎Findmodels,datasets,metricson the platform.:yellow_heart: Our repository is constantly evolving. With the help of the scientific community, we plan to add even more datasets, models, and metrics across domains and tasks.Getting StartedInstallationTo install simply,pipinstallaixtendAPI Key SetupBefore you can use the aiXtend SDK, you'll need to obtain an API key from our platform. For details refer thisTeam API Key Guide.Once you get the API key, you'll  need to add this API key as an environment variable on your system.Linux or macOSexportTEAM_API_KEY=YOUR_API_KEYWindowssetTEAM_API_KEY=YOUR_API_KEYUsageLet’s see how we can use aiXtend to run a machine translation model.fromaixtend.factories.model_factoryimportModelFactorymodel=ModelFactory.create_asset_from_id("61b27086c45ecd3c10d0608c")# Got the ID of an MT model from on our platformtranslation=model.run("This is a sample text")Check out theexplore sectionof our guide on Models to get the ID of your desired modelDeveloper GuideFollow theDeveloper Guide.SupportRaise issues for support in this repository.Pull requests are welcome!
Package: aiyc1v1
📦 Bornforthis Library Python aiyc1v11. What?带编程一对一学员学习 Python 所开发的库，便于零基础小白学习。和部分实用功能，目前拥有：简单的 NLP类游戏NoteSearch：https://github.com/AndersonHJB/aiyc1v1/tree/main/aiyc1v1/NoteSearch爬虫延迟插件「DelayWait」：https://github.com/AndersonHJB/aiyc1v1/tree/main/aiyc1v1/DelayWait如果，你有想要实现的功能，迟迟未实现，可以提交 issue 给我。https://github.com/AndersonHJB/aiyc1v1/issues2. Install如果你换源了，请用下面的命令获取最新版本：pipinstallaiyc1v1-ihttps://pypi.org/simple国内镜像源同步，比较缓慢，一般需要一天左右才会同步。pipinstallaiy1v13. Upgrade版本在快速的迭代中，所以如果需要最新版本的话，请用下面的命令。pip3install--upgradeaiyc1v1-ihttps://pypi.org/simplepip3install--upgradeaiyc1v14. 项目文档本部分是对已有的项目进行详细介绍，自行点入进行阅读。NoteSearch：README爬虫延迟插件：5. Github本项目的 GitHub 地址：https://github.com/AndersonHJB/aiyc1v16. 已有功能代码SimpleNLP:简单的自然语言处理代码「仅仅支持英文」——词频分析、词云生成。文档：SimpleNLP.mdGameBase:基础文字对话游戏NoteSearch:文档DelayWait: 智能爬虫插件「2023年01月07日」7. 使用示例NoteSearchfromaiyc1v1importDataManger,Search_Engineabs_path="your/project/path"d=DataManger(path=abs_path)s=Search_Engine(language="zh_CN")s.search("专栏")DelayWait更智能的延迟请求插件，使你摆脱单纯使用time.sleep()。具体实现逻辑，我也会在该插件文档中为你解答。fromaiyc1v1importDelayWaitimportrequestsif__name__=='__main__':urls=["https://bornforthis.cn"]*10d=DelayWait()forurlinurls:html=requests.get(url)d.wait(url)print(html.status_code)Error2022年08月20日numpy.core._exceptions.UFuncTypeError:ufunc'add'didnotcontainaloopwithsignaturematchingtypes(dtype('float64'),dtype('<U1'))->None预测是 dataframe to str 的错误，直接把读取到的结果，强制转换成 strself.content=str(pd.read_csv(self.path))self.content=str(pd.read_excel(self.path))ChangelogBug Fixes：bug 修复⚠ BREAKING CHANGES：⚠ 重大变化Features：特征Reverts：还原Performance Improvements：性能改进2023年01月12日Bug文件读取，过程中，编码问题。Features多语言支持询问用户：查询到 xx 条数据，请问要返回几条？、全部「all」2023年01月06日正式发布可用的 NoteSearch初步解决编写代码，文档中搜索问题2022年08月20日修改用户输入提示template="您选择使用默认路径:%s,\n如果确认请输入回车或者 yes(否则:no):"totemplate="您选择使用路径:%s,\n如果确认请输入回车或者 yes(否则:no):"公众号：AI悦创【二维码】欢迎关注我公众号：AI悦创，有更多更好玩的等你发现！info AI悦创·编程一对一AI悦创·推出辅导班啦，包括「Python 语言辅导班、C++ 辅导班、java 辅导班、算法/数据结构辅导班、少儿编程、pygame 游戏开发」，全部都是一对一教学：一对一辅导 + 一对一答疑 + 布置作业 + 项目实践等。当然，还有线下线上摄影课程、Photoshop、Premiere 一对一教学、QQ、微信在线，随时响应！微信：JiabcdefhC++ 信息奥赛题解，长期更新！长期招收一对一中小学信息奥赛集训，莆田、厦门地区有机会线下上门，其他地区线上。微信：Jiabcdefh方法一：QQ方法二：微信：Jiabcdefh
Package: aiycsnlp
SimpleNLP-Bornforthis LibraryWhat?带编程一对一学员类基础练习所创建的代码，功能主要有：传入英文文本「.txt」，并调用，生成词云。Installpipinstallaiycsnlp-ihttps://pypi.org/simpleGithubhttps://github.com/AndersonHJB/aiyc-snlp公众号：AI悦创【二维码】欢迎关注我公众号：AI悦创，有更多更好玩的等你发现！info AI悦创·编程一对一AI悦创·推出辅导班啦，包括「Python 语言辅导班、C++ 辅导班、java 辅导班、算法/数据结构辅导班、少儿编程、pygame 游戏开发」，全部都是一对一教学：一对一辅导 + 一对一答疑 + 布置作业 + 项目实践等。当然，还有线下线上摄影课程、Photoshop、Premiere 一对一教学、QQ、微信在线，随时响应！微信：JiabcdefhC++ 信息奥赛题解，长期更新！长期招收一对一中小学信息奥赛集训，莆田、厦门地区有机会线下上门，其他地区线上。微信：Jiabcdefh方法一：QQ方法二：微信：Jiabcdefh
Package: aiymakerkit
AIY Maker Kit Python API and examplesThe aiymakerkit API greatly simplifies the amount of code needed to
perform common operations with TensorFlow Lite models, such as performing image
classification, object detection, pose estimation, and speech recognition
(usually in combination with the Coral Edge TPU).This repo also includes
scripts to collect training images and perform transfer learning with an image
classification model, directly on your device (such as a Raspberry Pi).This project was designed specifically for theAIY Maker Kit, which uses a
Raspberry Pi with a Coral USB Accelerator, camera, and microphone.Learn moreTo get started, see theAIY Maker Kit documentation.
It includes complete setup instructions with a Raspberry Pi, project tutorials,
and theaiymakerkit API reference.Install on Raspberry Pi OSIf you're on a Raspberry Pi, we recommend you flash our custom Raspberry Pi OS
system image before installing this library, as documented athttps://aiyprojects.withgoogle.com/maker/. That way, you're sure to have
all the required software installed and there should be no trouble.But if you want to do things differently and can tolerate some extra steps and risk troubleshooting,
you can build our system image yourself and/or install the required libraries
on an existing RPI OS system as documented athttps://github.com/google-coral/aiy-maker-kit-tools(but we
do not recommend it).Install manuallyFor other situations where you want to install only theaiymakerkitlibrary,you must manually install thelibedgetpuandpycorallibraries first.
Assuming that you are also using the Coral USB
Accelerator, you can get these libraries by following theCoral USB Accelerator
setup guide at coral.ai.Then you can clone this repo and install the library as follows:git clone https://github.com/google-coral/aiy-maker-kit.git

cd aiymakerkit

python3 -m pip install .
Package: aiy-prayer
Google-AIY-prayer_assistantAIY optimized voice assistant Python library that tells when the next Islamic prayer will occur.This application is based on the open-sourceprayer-toollibrary.How to get it ?The aiy prayer assistant is a python package available via pip.pip install aiy_prayerHow to use it ?To use the tool you must simply type this command. When it's done, the assistant will wait for you to push the button. That's it !python -m aiy_prayer -c <city> -l <language> -v <volume>Examplepython -m aiy_prayer -c Brussels -l fr -v 60
Package: ai-z
Install and usagegit clone https://github.com/ai-z/ai-z.git
cd ai-z
pip3 install -r requirements.txt
python3 -m aiz.aizAlternatively Using pip:pip3 install ai-z
ai-zKnown IssuesAMD GPUs name will be displayed as ‘cardX’PCIE bandwith usage graph on NVDIA cards is not correct
Package: aizoo
🔥aizoo🔥Installpip install aizoo -UDocsUsagesOOF modelsfromaizoo.tab.modelsimportTabNetClassifierfromsklearn.datasetsimportmake_regression,make_classificationfromsklearn.metricsimportr2_score,roc_auc_scoreX,y=make_classification(n_samples=10000)TabNetClassifier().run(X,y,feval=roc_auc_score)Hyperparameter optimization:search_spacefromaizoo.tuner.optimizersimportLGBOptimizer,F1Optimizerfromsklearn.datasetsimportmake_regression,make_classificationX,y=make_classification(n_samples=1000)opt=LGBOptimizer('search_space.yaml',X,y)best_params=opt.optimize(100)opt.plot()TODO=======
History0.0.0 (2021-09-04)First release on PyPI.
Package: ajallaan
AjallaanIn due time (Finnish: ajallaan) - reporting on worklog entries of some ticket system.License: MITThird party dependencies are documented in the folderthird-party.DocumentationUser and developerdocumentation of ajallaan.Bug TrackerAny feature requests or bug reports shall go to thetodos of ajallaan.Primary Source repositoryThe main source ofajallaanis on a mountain in central Switzerland.
We use distributed version control (git).
There is no central hub.
Every clone can become a new source for the benefit of all.
The preferred public clones ofajallaanare:on codeberg- a democratic community-driven, non-profit software development platform operated by Codeberg e.V.at sourcehut- a collection of tools useful for software development.ContributionsPlease do not submit "pull requests" (I found no way to disable that "feature" on GitHub).
If you like to share small changes under the repositories license please kindly do so by sending a patchset.
You can either send such a patchset per email usinggit send-emailor
if you are a sourcehut user by selecting "Prepare a patchset" on the summary page of your fork atsourcehut.StatusExperimental.Note: The default branch isdefault.
Package: ajax-views
ajax-viewsA simple Django application to easily use AJAX views with JavaScript.Compatibilitydjango>= 3.2python>= 3.7FeaturesAbility to expose your AJAX URLs to JavaScriptSupported Function-Based and Class-Based ViewsOne URL patternto rule them allfor all AJAX viewsJinja2 supportInstallationInstall the package via Pip:pip install ajax-viewsAdd it to yourINSTALLED_APPSlist:INSTALLED_APPS=(# ..."ajax_views",# ...)Addajax_views.urlsto your URLconf:fromdjango.urlsimportinclude,pathurlpatterns=[path("ajax/",include("ajax_views.urls")),]Usage@ajax_view("name")Use this decorator to register your views (Function-Based or Class-Based).fromajax_views.decoratorsimportajax_view@ajax_view("myapp.form")defform_view(request):...@ajax_view("myapp.form_cbv")classAjaxFormView(FormView):...NOTE: The specified name has to be unique.You can combineajax_viewwith other decorators:@csrf_exempt@require_POST@ajax_view("myapp.contact_form")defcsrf_exempt_view(request):# ...{% ajax_views_json %}Template tag to output registered URLs as JSON.{% load ajax_views %}

<script>
    window.ajax_views = {% ajax_views_json %};
</script>Now you can use the declared object to refer to the corresponding urls like this:$.ajax({url:window.ajax_views.myapp.form,...});{% ajax_url 'name' %}This tag is used to add AJAX URLs in the template files:{% load ajax_views %}

<form action="{% ajax_url 'myapp.form' %}" method="post">
    ...
</form>Multiple namesYou can have multiple names for the same view:fromajax_views.decoratorsimportajax_view@ajax_view(["myapp.form","myapp.fallback"])defexample_view(request):...Jinja2 supportEnable Jinja2 extensionTEMPLATES=[{"BACKEND":"django.template.backends.jinja2.Jinja2","OPTIONS":{"extensions":[# ..."ajax_views.templatetags.ajax_views.AjaxViewsExtension",]}}]NOTE: If you are usingdjango-jinja, you don't need to do this.The usage is similar to Django, except thatajax_urlis a global function:<form action="{{ ajax_url('myapp.form') }}" method="post">
    ...
</form>Development and TestingAfter cloning the Git repository, you should install this
in a virtualenv and set up for development:virtualenv.venvsource.venv/bin/activate
pipinstall-r./requirements.txt
pre-commitinstall