Package: gptdeploy
GPT Deploy: One line to generate them all 🧙🚀Turn your natural language descriptions into fully functional, deployed AI-powered microservices with a single command!
Your imagination is the limit!This project streamlines the creation and deployment of AI-powered microservices. 
Simply describe your task using natural language, and the system will automatically build and deploy your microservice. 
To ensure the microservice accurately aligns with your intended task a test scenario is required.QuickstartRequirementsOpenAI key with access to GPT-3.5 or GPT-4Installationpipinstallgptdeploy
gptdeployconfigure--key<youropenaiapikey>If you set the environment variableOPENAI_API_KEY, the configuration step can be skipped.
Your api key must have access to gpt-4 to use this tool.
We are working on a way to use gpt-3.5-turbo as well.Generate Microservicegptdeploygenerate\--description"<description of the microservice>"\--test"<specification of a test scenario>"\--model<gpt-3.5orgpt-4>\--path</path/to/local/folder>To generate your personal microservice two things are required:Adescriptionof the task you want to accomplish.Atestscenario that ensures the microservice works as expected.Themodelyou want to use - eithergpt-3.5orgpt-4.gpt-3.5is ~10x cheaper,
but will not be able to generate as complex microservices.Apathon the local drive where the microservice will be generated.The creation process should take between 5 and 15 minutes.
During this time, GPT iteratively builds your microservice until it finds a strategy that make your test scenario pass.Be aware that the costs you have to pay for openai vary between $0.50 and $3.00 per microservice (using GPT-4).Run MicroserviceRun the microservice locally in docker. In case docker is not running on your machine, it will try to run it without docker.
With this command a playground opens in your browser where you can test the microservice.gptdeployrun--path<pathtomicroservice>Deploy MicroserviceIf you want to deploy your microservice to the cloud aJina accountis required.
When creating a Jina account, you get some free credits, which you can use to deploy your microservice ($0.025/hour).
If you run out of credits, you can purchase more.gptdeploydeploy--microservice_path<pathtomicroservice>Delete MicroserviceTo save credits you can delete your microservice via the following commands:jclist# get the microservice idjcdelete<microserviceid>ExamplesIn this section you can get a feeling for the kind of microservices that can be generated with GPT Deploy.Compliment Generatorgptdeploygenerate\--description"The user writes something and gets a related deep compliment."\--test"Given the word test a deep compliment is generated"\--modelgpt-4\--pathmicroserviceExtract and summarize news articles given a URLgptdeploygenerate\--description"Extract text from a news article URL using Newspaper3k library and generate a summary using gpt."\--test"input: 'http://fox13now.com/2013/12/30/new-year-new-laws-obamacare-pot-guns-and-drones/' output: assert a summarized version of the article exists"\--modelgpt-4\--pathmicroserviceChemical Formula Visualizationgptdeploygenerate\--description"Convert a chemical formula into a 2D chemical structure diagram"\--test"C=C, CN=C=O, CCC(=O)O"\--modelgpt-4\--pathmicroservice2d rendering of 3d modelgptdeploygenerate\--description"create a 2d rendering of a whole 3d object and x,y,z object rotation using trimesh and pyrender.OffscreenRenderer with os.environ['PYOPENGL_PLATFORM'] = 'egl' and freeglut3-dev library"\--test"input: https://graphics.stanford.edu/courses/cs148-10-summer/as3/code/as3/teapot.obj output: assert the image is not completely white or black"\--modelgpt-4\--pathmicroserviceProduct Recommendationgptdeploygenerate\--description"Generate personalized product recommendations based on user product browsing history and the product categories fashion, electronics and sport"\--test"Test that a user how visited p1(electronics),p2(fashion),p3(fashion) is more likely to buy p4(fashion) than p5(sports)"\--modelgpt-4\--pathmicroserviceHacker News Searchgptdeploygenerate\--description"Given a search query, find articles on hacker news using the hacker news api and return a list of (title, author, website_link, first_image_on_the_website)"\--test"searching for GPT gives results"\--modelgpt-4\--pathmicroserviceAnimal Detectorgptdeploygenerate\--description"Given an image, return the image with bounding boxes of all animals (https://pjreddie.com/media/files/yolov3.weights, https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg)"\--test"https://images.unsplash.com/photo-1444212477490-ca407925329e contains animals"\--modelgpt-4\--pathmicroserviceMeme Generatorgptdeploygenerate\--description"Generate a meme from an image and a caption"\--test"Surprised Pikachu: https://media.wired.com/photos/5f87340d114b38fa1f8339f9/master/w_1600%2Cc_limit/Ideas_Surprised_Pikachu_HD.jpg, TOP:When you discovered GPTDeploy"\--modelgpt-4\--pathmicroserviceRhyme Generatorgptdeploygenerate\--description"Given a word, return a list of rhyming words using the datamuse api"\--test"hello"\--modelgpt-4\--pathmicroserviceWord Cloud Generatorgptdeploygenerate\--description"Generate a word cloud from a given text"\--test"Lorem ipsum dolor sit amet, consectetur adipiscing elit."\--modelgpt-4\--pathmicroservice3d model infogptdeploygenerate\--description"Given a 3d object, return vertex count and face count"\--test"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/models/wolf.obj"\--modelgpt-4\--pathmicroserviceTable extractiongptdeploygenerate\--description"Given a URL, extract all tables as csv"\--test"http://www.ins.tn/statistiques/90"\--modelgpt-4\--pathmicroserviceAudio to mel spectrogramgptdeploygenerate\--description"Create mel spectrograms from audio file"\--test"https://cdn.pixabay.com/download/audio/2023/02/28/audio_550d815fa5.mp3"\--modelgpt-4\--pathmicroserviceText to speechgptdeploygenerate\--description"Convert text to speech"\--test"Hello, welcome to GPT Deploy!"\--modelgpt-4\--pathmicroserviceYour browser does not support the audio element.Heatmap Generatorgptdeploygenerate\--description"Create a heatmap from an image and a list of relative coordinates"\--test"https://images.unsplash.com/photo-1574786198875-49f5d09fe2d2, [[0.1, 0.2], [0.3, 0.4], [0.5, 0.6], [0.2, 0.1], [0.7, 0.2], [0.4, 0.2]]"\--modelgpt-4\--pathmicroserviceQR Code Generatorgptdeploygenerate\--description"Generate QR code from URL"\--test"https://www.example.com"\--modelgpt-4\--pathmicroserviceMandelbrot Set Visualizergptdeploygenerate\--description"Visualize the Mandelbrot set with custom parameters"\--test"center=-0+1i, zoom=1.0, size=800x800, iterations=1000"\--modelgpt-4\--pathmicroserviceMarkdown to HTML Convertergptdeploygenerate--description"Convert markdown to HTML"--test"# Hello, welcome to GPT Deploy!"Technical InsightsThe graphic below illustrates the process of creating a microservice and deploying it to the cloud elaboration two different implementation strategies.graph TB

    description[description: generate QR code from URL] --> make_strat{think a}

    test[test: https://www.example.com] --> make_strat[generate strategies]

    make_strat --> implement1[implement strategy 1]

    implement1 --> build1{build image}

    build1 -->|error message| implement1

    build1 -->|failed 10 times| implement2[implement strategy 2]

    build1 -->|success| registry[push docker image to registry]

    implement2 --> build2{build image}

    build2 -->|error message| implement2

    build2 -->|failed 10 times| all_failed[all strategies failed]

    build2 -->|success| registry[push docker image to registry]

    registry --> deploy[deploy microservice]

    deploy --> streamlit[generate streamlit playground]

    streamlit --> user_run[user tests microservice]GPT Deploy identifies several strategies to implement your task.It tests each strategy until it finds one that works.For each strategy, it generates the following files:microservice.py: This is the main implementation of the microservice.test_microservice.py: These are test cases to ensure the microservice works as expected.requirements.txt: This file lists the packages needed by the microservice and its tests.Dockerfile: This file is used to run the microservice in a container and also runs the tests when building the image.GPT Deploy attempts to build the image. If the build fails, it uses the error message to apply a fix and tries again to build the image.Once it finds a successful strategy, it:Pushes the Docker image to the registry.Deploys the microservice.Generates a Streamlit playground where you can test the microservice.If it fails 10 times in a row, it moves on to the next approach.🔮 visionUse natural language interface to generate, deploy and update your microservice infrastructure.✨ ContributorsIf you want to contribute to this project, feel free to open a PR or an issue.
In the following, you can find a list of things that need to be done.next steps:check if windows and linux support worksadd video to README.mdbug: it can happen that the code generation is hanging forever - in this case aboard and redo the generationnew user has free credits but should be told to verify accountNice to have:smooth rendering animation of  the responsesif the user runs gptdeploy without any arguments, show the help messagedon't show this message:
🔐 You are logged in to Jina AI as florian.hoenicke (username:auth0-unified-448f11965ce142b6).
To log out, use jina auth logout.put the playground into the custom gateway (without rebuilding the custom gateway)hide prompts in normal mode and show them in verbose modetestsclean up duplicate codesupport popular cloud providers - lambda, cloud run, cloud functions, ...support local docker buildsautoscaling enabled for cost savingadd more examples to README.mdsupport multiple endpoints - example: todolist microservice with endpoints for adding, deleting, and listing todossupport stateful microservicesThe playground is currently printed twice even if it did not change.
Make sure it is only printed twice in case it changed.allow to update your microservice by providing feedbacksupport for other large language models like Open Assistentfor cost savings, it should be possible to insert less context during the code generation of the main functionality - no jina knowledge is requireduse gptdeploy list to show all deploymentsgptdeploy delete to delete a deploymentgptdeploy update to update a deploymenttest param optional - in case the test param is not there first ask gpt if more information is required to write a test - like access to pdf datasection for microservices built by the communitytest feedback for playground generation (could be part of the debugging)should we send everything via json in the text attribute for simplicity?fix release workflowafter the user specified the task, ask them questions back if the task is not clear enough or something is missingProposal:just generate the non-jina related code and insert it into an executor templatethink about strategies after the first approach failed?