Saravanan666/pypi-org huggingface dataset:
Package: tdxapi
tdxapiA Python TeamDynamix API wrapperNOTE: tdxapi is under active development. While safe to use, parts of the API may change.Dependenciespython 3.6+requestsattrspython-dateutilSupported APIsGeneralAccountsApplicationsAttachmentsAttributesGroupsLocationsAsset/Configuration ManagementAsset StatusesAssets (except bulk import)Configuration Item TypesConfiguration ItemsConfiguration Relationship TypesProduct ModelsProduct TypesVendorsReportingReportsRolesFunctional RolesResource PoolsSecurity RolesTicketsImpactsPrioritiesSourcesTicket StatusesTicket TasksTicket TypesTickets (except patch)UrgenciesInstallationpip install tdxapiQuickstartCreate a TdxClient object representing a connection to a TeamDynamix instance. Currently, only logging in via the administrative account is supported. You can connect to your sandbox withuse_sandbox=True:from tdxapi import TdxClient

tdx = TdxClient(
    "<Organization>",
    beid="<Organization BEID>",
    wskey="<Organization Web Services Key>"
)Non-Application based APIs can be accessed directly from the TdxClient instance. For example, getting a list of all Accounts:for account in tdx.accounts.all():
    print(account.name)Application based APIs require an application object to be created by using the appropriate method and application id. For example to create an asset application:app = tdx.asset_app(123)Now we can perform asset related tasks, like retrieving an asset and updating its information:kevins_pc = app.assets.get(123456)
kevins_pc.purchase_cost = 1000.00

app.assets.save(kevins_pc)If an API supports adding new objects there will be anew()method to return a new object instance. Usingnew()will set the object'sapp_id, if applicable, and populateattributeswith the applicable custom attributes based on object type:new_asset = app.assets.new()

new_asset.name = "Kevin's Office Printer"
new_asset.serial_number = "123XYZ"
new_asset.status_id = 123               # In Use
new_asset.manufacturer_id = 456         # HP
new_asset.product_model_id = 789        # LaserJet Pro M118dw

app.assets.save(new_asset)Alternatively, you can use keyword arguments when creating new objects:new_asset = app.assets.new(
    name="kevins office printer",
    serial_number="123XYZ",
    status_id=123,
    manufacturer_id=456,
    product_model_id=789
)

app.assets.save(new_asset)Custom attributes are stored in a list-like container with added functionality to make working with them easier. Updating attributes only requires the attribute id and the value:kevins_pc = app.assets.get(123456)

kevins_pc.attributes.update(21212, datetime.now())      # date/time attribute
kevins_pc.attributes.update(31313, "PO# 1234567")       # text attribute
kevins_pc.attributes.update(41414, 11223)               # single choice attribute
kevins_pc.attributes.update(51515, [13579, 24680])      # multiple choice attributeTo search by attributes set thecustom_attributesargument to a list of tuples containing attribute id and value:# Find assets with a specific PO Number
assets = app.assets.search(attributes=[(31313, "PO# 1234567")])That's it for now. Better documentation and more API support coming soon.
Package: tdxpy
TdxPy - Python TDX 数据接口如果喜欢本项目可以在右上角给颗⭐！你的支持是我最大的动力😎！开源协议: MIT license在线文档:https://tdxpy.readthedocs.io国内镜像:https://gitee.com/ibopo/tdxpy项目仓库:https://github.com/mootdx/tdxpy项目概述本项目是 pytdx 衍生项目, 原作者因不再维护了, 以至于很多问题不能得到修复。项目特点纯 python 实现，无须引入动态连接库.dll / .so文件支持全平台Windows / MacOS / Linux移除python2的支持去除命令行功能修复若干 bug重新整理项目结构修复基金价格问题快速安装pipinstalltdxpy郑重声明本项目只作学习交流, 不得用于任何商业目的.此代码用于个人对网络协议的研究和习作，不对外提供服务，任何人使用本代码遇到问题请自行解决，也可以在 github 提 issue 给我，但是我不保证能即时处理。由于我们连接的是既有的行情软件兼容行情服务器，机构请不要使用此代码，对此造成的任何问题本人概不负责。
Package: tdxwiyqvef
Tdxwiyqvef=================This API SDK was automatically generated by [APIMATIC Code Generator](https://apimatic.io/).This SDK uses the Requests library and will work for Python ```2 >=2.7.9``` and Python ```3 >=3.4```.How to configure:=================The generated code might need to be configured with your API credentials.To do that, open the file "Configuration.py" and edit its contents.How to resolve dependencies:===========================The generated code uses Python packages named requests, jsonpickle and dateutil.You can resolve these dependencies using pip ( https://pip.pypa.io/en/stable/ ).1. From terminal/cmd navigate to the root directory of the SDK.2. Invoke ```pip install -r requirements.txt```Note: You will need internet access for this step.How  to test:=============You can test the generated SDK and the server with automatically generated testcases. unittest is used as the testing framework and nose is used as the testrunner. You can run the tests as follows:1. From terminal/cmd navigate to the root directory of the SDK.2. Invoke ```pip install -r test-requirements.txt```3. Invoke ```nosetests```How to use:===========After having resolved the dependencies, you can easily use the SDK following these steps.1. Create a "tdxwiyqvef_test.py" file in the root directory.2. Use any controller as follows:```pythonfrom __future__ import print_functionfrom tdxwiyqvef.tdxwiyqvef_client import TdxwiyqvefClientapi_client = TdxwiyqvefClient()controller = api_client.clientresponse = controller.get_basic_auth_test(<required parameters if any>)print(response)```
Package: te
Toggl Entry (te)Toggle Entry (te) is a Toggl REST API command line interface (CLI) client. This tool allows quick entries insertion from your terminal - no more tedious mouse-clicking!For any power user, this will be probably much faster way than inserting entries with the official web/desktop/mobile version of the app. Moreover, it can nicely speed-up bulk editing via shell script edited in your favorite editor. Actually, this is how I am catching up with my timesheets ;-)RequirementsWindows, macOS, Linux, or any Unix-like operating systemPython 3 + pip (tested with Python 3.7)Toggl account + Toggl API tokenYou can get the Toggl API token in its web interface fromProfile Settings(scroll to the bottom).InstallationInstalltefrom PyPI (Python Package Index):$ pip3 install teCreate a configuration file.te.iniin your home directory:$ touch ~/.te.iniSet permissions to your configuration file, so nobody but you can read it:$ chmod 0600 ~/.te.iniOpen the configuration file:$ nano  ~/.te.iniInsert this template to the configuration file:[general]

; Set your timezone here
timezone = Europe/Prague

; Set your API token here
api_token = ...Adjust thetimezoneandapi_tokenin the configuration file and save it.UsageOpen your terminal application and try this command:$ te 20-01-30 08:00 1h My first task entryIf no error message is provided and the process gracefully exits, the entry has been successfully inserted. You can check it in the Toggl web app.You can also use this format to automatically insert the current date:$ te - 08:00 120m My first task entryAlso, the separators can be avoided:$ te 200130 0800 3h My first task entryThat's it for now and happy Toggl-ing!ContributingPlease readCONTRIBUTING.mdfor details on our code of conduct, and the process for submitting pull requests to us.VersioningWe useSemVerfor versioning. For the versions available, see thetags on this repository.AuthorsPavel Hübner- Initial workLicenseThis project is licensed under theMIT License- see theLICENSEfile for details.
Package: te2-sdk
Copyright (c) 2017 Rory ChattertonPermission is hereby granted, free of charge, to any person obtaining a copyof this software and associated documentation files (the "Software"), to dealin the Software without restriction, including without limitation the rightsto use, copy, modify, merge, publish, distribute, sublicense, and/or sellcopies of the Software, and to permit persons to whom the Software isfurnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in allcopies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS ORIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THEAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHERLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THESOFTWARE.Description: # Terraform 2 SDK [![Build Status](https://travis-ci.org/westpac-cloud-engineering/Terraform-Enterprise-2-Python-SDK.svg?branch=master)](https://travis-ci.org/westpac-cloud-engineering/Terraform-Enterprise-2-Python-SDK) [![codecov](https://codecov.io/gh/westpac-cloud-engineering/Terraform-Enterprise-2-Python-SDK/branch/master/graph/badge.svg)](https://codecov.io/gh/westpac-cloud-engineering/Terraform-Enterprise-2-Python-SDK)SDK to call Terraform Enterprise 2 using Python.This SDK was developed to wrap Restful API calls to Terraform Enterprise 2, in order to trigger Plans, Applys and Variable updates using Jenkins.## How to install```pip install te2_sdk```## Triggering a RunBelow is an example of how you trigger a plan```pythonfrom te2_sdk import te2client = te2.TE2Client(organisation="MY_TERRAFORM_ENTERPRISE_ORG",atlas_token="SECRET_TOKEN_HERE",base_url="https://atlas.hashicorp.com/api/v2" # Change this if you are using a private install)ws_runs = te2.TE2WorkspaceRuns(client=client, workspace_name="My Workspace Name" )run = ws_runs.request_run(request_type="plan", destroy=False)```And to do a run:```pythonrun = ws_runs.request_run(request_type="apply", destroy=False, run_id = "RUN_ID_FROM_PLAN")```### Completed Functionality- [x] Runs- [x] Plan- [x] Run- [x] Discard- [x] Get Log- [x] Variables- [x] Create- [x] Read- [x] Update- [x] Delete### Incomplete FunctionalityThe following items are not in development, as these Day 0 operations will eventually be wrapped up in an official Terraform Provider.- [ ] Workspaces- [ ] Sentinel- [ ] TeamsPlatform: UNKNOWNClassifier: Development Status :: 4 - BetaClassifier: Intended Audience :: DevelopersClassifier: License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)Classifier: Operating System :: OS IndependentClassifier: Programming Language :: Python :: 3Classifier: Programming Language :: Python :: 3.5Classifier: Programming Language :: Python :: 3.6Classifier: Topic :: Text Processing
Package: tea
teateais a set of libraries developed for common tasks like executing and
manipulating processes, sending emails, file system manipulation, and a great
number of utility functions that I caught myself writing over and over again.Also parts of thetealibrary can be viewed as a sand-box where I start
a module, which eventually, if it grows and become large enough to become a
library itself, gets extracted as a separate project.Modules likedsa(data structures and algorithms),logger,msg,process,shell,utils... will always remain a part oftea.
Package: teaairdrop
一、安装（python版本建议3.7以上）pipinstalldubborequests二、升级包pipinstall--upgradedubborequests三、示例获取dubbo服务详情# 导入importdubborequests# 获取dubbo服务详情data=dubborequests.search('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService')获取服务下的所有方法# 导入importdubborequests# 获取dubbo服务下的所有方法data=dubborequests.list('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService')# 获取dubbo服务指定的方法data=dubborequests.list('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService','login')通过zookeeper获取服务的ip和端口, Telnet命令测试dubbo接口importdubborequestsfromdubborequestsimportConfig# 先配置zookeeper中心地址Config.zookeeper_url_list=['192.168.240.15:2181','192.168.240.15:2182','192.168.240.15:2183']invoke_data={"service_name":"cn.com.xxxxx.sso.ehr.api.dubbo.SsoEmpInfoService","method_name":"login","data":{"account":"xxxx","password":"xxxx"}}# 通过zookeeper获取服务的ip和端口, Telnet命令测试dubbo接口data=dubborequests.zk_invoke(*invoke_data)Telnet命令测试dubbo接口importdubborequestsinvoke_data={"ip":'xxxx',"port":7777,"service_name":"cn.com.xxxxx.sso.ehr.api.dubbo.SsoEmpInfoService","method_name":"login","data":{"account":"xxxx","password":"xxxx"}}# Telnet命令测试dubbo接口data=dubborequests.telnet_invoke(*invoke_data)
Package: teaairdroptest
一、安装（python版本建议3.7以上）pipinstalldubborequests二、升级包pipinstall--upgradedubborequests三、示例获取dubbo服务详情# 导入importdubborequests# 获取dubbo服务详情data=dubborequests.search('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService')获取服务下的所有方法# 导入importdubborequests# 获取dubbo服务下的所有方法data=dubborequests.list('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService')# 获取dubbo服务指定的方法data=dubborequests.list('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService','login')通过zookeeper获取服务的ip和端口, Telnet命令测试dubbo接口importdubborequestsfromdubborequestsimportConfig# 先配置zookeeper中心地址Config.zookeeper_url_list=['192.168.240.15:2181','192.168.240.15:2182','192.168.240.15:2183']invoke_data={"service_name":"cn.com.xxxxx.sso.ehr.api.dubbo.SsoEmpInfoService","method_name":"login","data":{"account":"xxxx","password":"xxxx"}}# 通过zookeeper获取服务的ip和端口, Telnet命令测试dubbo接口data=dubborequests.zk_invoke(*invoke_data)Telnet命令测试dubbo接口importdubborequestsinvoke_data={"ip":'xxxx',"port":7777,"service_name":"cn.com.xxxxx.sso.ehr.api.dubbo.SsoEmpInfoService","method_name":"login","data":{"account":"xxxx","password":"xxxx"}}# Telnet命令测试dubbo接口data=dubborequests.telnet_invoke(*invoke_data)
Package: tea-and-sympathy
This is a technical demonstration ofBalladeer.Balladeer helps you build parser-based Interactive Fiction for the Web.
Balladeer is a Python library.InstallationWindowsLinuxWindowsTea and Sympathyis a command line program.
You use it from the Windows command interpreter.To launch a new command window:Tap the Windows key so that the Start Menu pops up.Type the wordcmd.When you see theCommand Promptapp highlighted, tap the Enter key.You should see a prompt like this (your user name will differ):Microsoft Windows [Version 10.0.18362.1139]
(c) 2019 Microsoft Corporation. All rights reserved.

C:\Users\author>PrerequisitesDownload and install Python fromhttps://www.python.org/. You need Python version 3.8 or higher.
Make sure to check the option to addpythonto your environment path.
This makes command line operation more easy.After you’ve installed Python, open a command window and typepython.
You should see something like this:C:\Users\author>python
Python 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 22:45:29) [MSC v.1916 32 bit (Intel)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>>Typequit()and press Return.Virtual EnvironmentFirst make a fresh Python virtual environment:python -m venv C:\Users\author\balladeer-appUpdate the package manager within it:C:\Users\author\balladeer-app\Scripts\pip install -U pip wheelInstall dependencies:C:\Users\author\balladeer-app\Scripts\pip install aiohttpInstall (or update) Balladeer:C:\Users\author\balladeer-app\Scripts\pip install -U balladeerDownloadDownload therepository as a zip file.
Unzip it to a local directory.cdtotea_and_sympathy.RunYou can run the demo in two modes.Launch a local web server to play in a browser (http://localhost:8080):C:\Users\author\balladeer-app\Scripts\python -m tas.serverOr text-only in the terminal:C:\Users\author\balladeer-app\Scripts\python -m tas.storyLinuxThe Linux command line is generally more easy to work with than the Windows command prompt.
If you’re finding the Windows command prompt tricky, you can installGit Bashwhich behaves in a
Linux-like way.I encourage you to move to a Linux operating system when you are able.
You can try one out at little cost on aRaspberry Pior similar device.Here are the install instructions for Linux. You need Python version 3.8 or higher.Virtual EnvironmentFirst make a fresh Python virtual environment:python3 -m venv ~/balladeer-appUpdate the package manager within it:~/balladeer-app/bin/pip install -U pip wheelInstall dependencies:~/balladeer-app/bin/pip install aiohttpInstall (or update) Balladeer:~/balladeer-app/bin/pip install -U balladeerDownloadDownload therepository as a zip file.
Unzip it to a local directory.cdtotea_and_sympathy.RunYou can run the demo in two modes.Launch a local web server to play the web app (http://localhost:8080):~/balladeer-app/bin/python -m tas.serverOr text-only in the terminal:~/balladeer-app/bin/python -m tas.storyThe freedom to copyYou are free to use this project as a teaching example, or as the basis of your own work.
Please read the licence and make sure youunderstand the Affero GPL.
Package: teabugmaker
一、安装（python版本建议3.7以上）pipinstalldubborequests二、升级包pipinstall--upgradedubborequests三、示例获取dubbo服务详情# 导入importdubborequests# 获取dubbo服务详情data=dubborequests.search('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService')获取服务下的所有方法# 导入importdubborequests# 获取dubbo服务下的所有方法data=dubborequests.list('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService')# 获取dubbo服务指定的方法data=dubborequests.list('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService','login')通过zookeeper获取服务的ip和端口, Telnet命令测试dubbo接口importdubborequestsfromdubborequestsimportConfig# 先配置zookeeper中心地址Config.zookeeper_url_list=['192.168.240.15:2181','192.168.240.15:2182','192.168.240.15:2183']invoke_data={"service_name":"cn.com.xxxxx.sso.ehr.api.dubbo.SsoEmpInfoService","method_name":"login","data":{"account":"xxxx","password":"xxxx"}}# 通过zookeeper获取服务的ip和端口, Telnet命令测试dubbo接口data=dubborequests.zk_invoke(*invoke_data)Telnet命令测试dubbo接口importdubborequestsinvoke_data={"ip":'xxxx',"port":7777,"service_name":"cn.com.xxxxx.sso.ehr.api.dubbo.SsoEmpInfoService","method_name":"login","data":{"account":"xxxx","password":"xxxx"}}# Telnet命令测试dubbo接口data=dubborequests.telnet_invoke(*invoke_data)
Package: teachablehub
AboutDeployment, Prediction, and Management API interactions of TeachableHub Machine-Learning Deployment Platform via Python.UsageDeploy a modelYou can integrate this SDK in your training logic, Jupyter notebook during experimentation, or your CI/CD system during the production environment. This SDK is also applicable for deploying existing models from your model registry or storage.fromteachablehub.deployments.sklearnimportTeachableDeployment# ... training logic here ...deployment=TeachableDeployment(teachable="user/teachable",environment="production",deploy_key="your-deploy-key-here",)deployment.model(clf)deployment.deploy(summary="Automatic deployment from our CI via sklearn-deploy.py",activate=True)Other deployment examplesWe have prepared a couple of simple and advanced examples to show you some standard ways to integrate the TeachableHub platform very easily inside your training process. Also, there are ideas on how you can deploy your already trained and stored models to TeachableHub.In the advanced examples, you will find some excellent tips and tricks on sharing knowledge between the training environment and the deployments via the Context API or creating and enforcing Features Schema with Validation to make your Model Serving API more understandable and secure.Take a look at the examples here:Ludwig exampleDeploy existing model exampleDeploy advanced exampleSklearn Regression Advanced ExampleMake predictionsSimple ndarray predictionsfromteachablehub.clientsimportTeachableHubPredictAPIteachable=TeachableHubPredictAPI(teachable="user/teachable",environment="production",serving_key="your-serving-key-here")predictions=teachable.predict([[0.03,0.05,-0.002,-0.01,0.04,0.01,0.08,-0.04,0.005,-0.1]])print(predictions)Advanced predictions with Features Validationfromteachablehub.clientsimportTeachableHubPredictAPIteachable=TeachableHubPredictAPI(teachable="user/teachable",environment="production",serving_key="your-serving-key-here")features={"age":0.03,"sex":0.05,"bmi":-0.002,"bp":-0.01,"s1":0.04,"s2":0.01,"s3":0.08,"s4":-0.04,"s5":0.005,"s6":-0.1}predictions=teachable.predict(features,order='desc',limit=10,threshold=0.5)print(predictions)Supported Tools & FrameworksUber's Luwdig- Ludwig is a toolbox on top of TensorFlow that allows to train and evaluate deep learning models without the need to write code.scikit-learn- Machine Learning in PythonGoogle's TeachablemachineImage ClassificationRequirementsPython 3.7.7+ (probably could work with Python 3.4+ as well.)Create a teachable here:https://app.teachablehub.com/createCreate a deploy key here:https://app.teachablehub.com/<user>/<teachable>/settings/deploy-keysCreate a Serving key here:https://app.teachablehub.com/<user>/<teachable>/settings/serving-keysInstallationfrom sourcegitclonehttps://github.com/teachablehub/python-sdk.gitcdpython-sdk
pythonsetup.pyinstallwith pippipinstallteachablehubContributingThanks for looking at this section. We're open to any cool ideas, so if you have one and are willing to share - fork the repo, apply changes and open a pull request. :)CopyrightCopyright (c) 2021 CloudStrap AD. See LICENSE for further details.
Package: teachable-machine
Teachable MachineA Python package to simplify the deployment process of exportedTeachable Machinemodels into Python projects.Developed by@MeqdadDevSupported ClassifiersImage Classification: Use exported keras model from Teachable Machine platfrom.RequirementsPython >= 3.7How to install packagepipinstallteachable-machineDependenciesnumpy
Pillow
tensorflowExampleAn example for teachable machine package with OpenCV:fromteachable_machineimportTeachableMachineimportcv2ascvcap=cv.VideoCapture(0)model=TeachableMachine(model_path="keras_model.h5",labels_file_path="labels.txt")image_path="screenshot.jpg"whileTrue:_,img=cap.read()cv.imwrite(image_path,img)result=model.classify_image(image_path)print("class_index",result["class_index"])print("class_name:::",result["class_name"])print("class_confidence:",result["class_confidence"])print("predictions:",result["predictions"])cv.imshow("Video Stream",img)cv.waitKey(1)class_indexandclass_nameare assigned based on the content of labels.txt file.Links:PyPISource Code
Package: teachable-machine-easy
Teachable Machine Easy LibraryMany people know of Teachable Machine, by Google. This certain library/module only supports the image part of it.Website where you can create it a h5 file/model.Checklist:h5 File Support, without user opening file themselves.Other Teachable Machine Options, like audio.Cross-platform testing.Simple Syntax:fromteachable-machine-easyimportmainmain(keras_model.h5,messages=["You have Pepsi.","You have Coke.","You have Fanta.","You have Red Bull."])This will open up the camera, and constantly tell whch type of soda there is.Note: This library is in no way associated with these drinks. They are here for demostartion purposes only.
Package: teachable-machine-lite
Teachable Machine LiteDescriptionA Python package to simplify the deployment process of exportedTeachable Machinemodels into different Robotics, AI and IoT controllers such as: Raspberry Pi, Jetson Nano and any other SBCs using TensorFlowLite framework.Developed by@MeqdadDevSupported ClassifiersImage Classification: use exported and quantized TensorFlow Lite model fromTeachable Machine platfrom(a model file withtfliteextension).RequirementsPython >= 3.7How to install packagepipinstallteachable-machine-liteDependenciesnumpy
tflite-runtime
Pillow(PIL)How to Use Teachable Machine Lite Packagefromteachable_machine_liteimportTeachableMachineLiteimportcv2ascvcap=cv.VideoCapture(0)model_path='model.tflite'image_file_name="frame.jpg"labels_path="labels.txt"tm_model=TeachableMachineLite(model_path=model_path,labels_file_path=labels_path)whileTrue:ret,frame=cap.read()cv.imshow('Cam',frame)cv.imwrite(image_file_name,frame)results=tm_model.classify_frame(image_file_name)print("results:",results)k=cv.waitKey(1)ifk%255==27:# press ESC to close camera view.breakLinks:PyPISource CodeDeveloper
Package: teachable-pi-websocket
Picamera Websocket for Teachable MachineHow to useplug in camerasetup cameraofficalDocumentationclone this repo to your home directoryinstall python dependenciespip3 install -r requirements.txtopen SSH tunnel to your raspberryssh pi@<raspberry-ip> -L 8080:<raspberry-ip>:8080run python scriptpython3 server.pyvisit Teachable Machineto collect images via the network append the following code to thelink?network=truenow three options for uploading images show up.WecamUploadNetwork <--that is what you wantuse the configured websocket addresslocalhostand the port8080pressconnectnow you cann add images to the class by pressing therecordbutton