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!

[](https://user-images.githubusercontent.com/11627845/231530421-272a66aa-4260-4e17-ab7a-ba66adca754c.mp4)

``` If you set the environment variable `OPENAI_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 Microservice ```bash gptdeploy generate \ --description "" \ --test "" \ --model \ --path ``` To generate your personal microservice two things are required: - A `description` of the task you want to accomplish. - A `test` scenario that ensures the microservice works as expected. - The `model` you want to use - either `gpt-3.5` or `gpt-4`. `gpt-3.5` is ~10x cheaper, but will not be able to generate as complex microservices. - A `path` on 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 Microservice Run 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. ```bash gptdeploy run --path ``` ### Deploy Microservice If you want to deploy your microservice to the cloud a [Jina account](https://cloud.jina.ai/) is 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. ```bash gptdeploy deploy --microservice_path ``` ### Delete Microservice To save credits you can delete your microservice via the following commands: ```bash jc list # get the microservice id jc delete ``` ## Examples In this section you can get a feeling for the kind of microservices that can be generated with GPT Deploy. ### Compliment Generator ```bash gptdeploy generate \ --description "The user writes something and gets a related deep compliment." \ --test "Given the word test a deep compliment is generated" \ --model gpt-4 \ --path microservice ``` ### Extract and summarize news articles given a URL ```bash gptdeploy generate \ --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" \ --model gpt-4 \ --path microservice ``` ### Chemical Formula Visualization ```bash gptdeploy generate \ --description "Convert a chemical formula into a 2D chemical structure diagram" \ --test "C=C, CN=C=O, CCC(=O)O" \ --model gpt-4 \ --path microservice ``` ### 2d rendering of 3d model ```bash gptdeploy generate \ --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" \ --model gpt-4 \ --path microservice ``` ### Product Recommendation ```bash gptdeploy generate \ --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)" \ --model gpt-4 \ --path microservice ``` ### Hacker News Search ```bash gptdeploy generate \ --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" \ --model gpt-4 \ --path microservice ```` ### Animal Detector ```bash gptdeploy generate \ --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" \ --model gpt-4 \ --path microservice ``` ### Meme Generator ```bash gptdeploy generate \ --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" \ --model gpt-4 \ --path microservice ``` ### Rhyme Generator ```bash gptdeploy generate \ --description "Given a word, return a list of rhyming words using the datamuse api" \ --test "hello" \ --model gpt-4 \ --path microservice ``` ### Word Cloud Generator ```bash gptdeploy generate \ --description "Generate a word cloud from a given text" \ --test "Lorem ipsum dolor sit amet, consectetur adipiscing elit." \ --model gpt-4 \ --path microservice ``` ### 3d model info ```bash gptdeploy generate \ --description "Given a 3d object, return vertex count and face count" \ --test "https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/models/wolf.obj" \ --model gpt-4 \ --path microservice ``` ### Table extraction ```bash gptdeploy generate \ --description "Given a URL, extract all tables as csv" \ --test "http://www.ins.tn/statistiques/90" \ --model gpt-4 \ --path microservice ``` ### Audio to mel spectrogram ```bash gptdeploy generate \ --description "Create mel spectrograms from audio file" \ --test "https://cdn.pixabay.com/download/audio/2023/02/28/audio_550d815fa5.mp3" \ --model gpt-4 \ --path microservice ``` ### Text to speech ```bash gptdeploy generate \ --description "Convert text to speech" \ --test "Hello, welcome to GPT Deploy!" \ --model gpt-4 \ --path microservice ``` ### Heatmap Generator ```bash gptdeploy generate \ --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]]" \ --model gpt-4 \ --path microservice ``` ### QR Code Generator ```bash gptdeploy generate \ --description "Generate QR code from URL" \ --test "https://www.example.com" \ --model gpt-4 \ --path microservice ``` ### Mandelbrot Set Visualizer ```bash gptdeploy generate \ --description "Visualize the Mandelbrot set with custom parameters" \ --test "center=-0+1i, zoom=1.0, size=800x800, iterations=1000" \ --model gpt-4 \ --path microservice ``` ### Markdown to HTML Converter ```bash gptdeploy generate --description "Convert markdown to HTML" --test "# Hello, welcome to GPT Deploy!" ``` [//]: # (## TO BE TESTED) [//]: # (### Password Strength Checker) [//]: # (```bash) [//]: # (gptdeploy generate --description "Given a password, return a score from 1 to 10 indicating the strength of the password" --test "Pa$$w0rd => 1/5, !Akfdh%.ytRadf => 5/5") [//]: # (```) [//]: # (### Morse Code Translator) [//]: # (```bash) [//]: # (gptdeploy generate --description "Convert text to morse code" --test "Hello, welcome to GPT Deploy!") [//]: # (```) [//]: # (### IP Geolocation) [//]: # (```bash) [//]: # (gptdeploy generate --description "Given an IP address, return the geolocation information" --test "142.251.46.174") [//]: # (```) [//]: # (### Currency Converter) [//]: # (```bash) [//]: # (gptdeploy generate --description "Converts any currency into any other" --test "1 usd to eur") [//]: # (```) [//]: # (### Image Resizer) [//]: # (```bash) [//]: # (gptdeploy generate --description "Given an image, resize it to a specified width and height" --test "https://images.unsplash.com/photo-1602738328654-51ab2ae6c4ff") [//]: # (```) [//]: # (### Weather API) [//]: # (```bash) [//]: # (gptdeploy generate --description "Given a city, return the current weather" --test "Berlin") [//]: # (```) [//]: # () [//]: # (### Sudoku Solver) [//]: # (```bash) [//]: # (gptdeploy generate --description "Given a sudoku puzzle, return the solution" --test "[[2, 5, 0, 0, 3, 0, 9, 0, 1], [0, 1, 0, 0, 0, 4, 0, 0, 0], [4, 0, 7, 0, 0, 0, 2, 0, 8], [0, 0, 5, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 9, 8, 1, 0, 0], [0, 4, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 3, 6, 0, 0, 7, 2], [0, 7, 0, 0, 0, 0, 0, 0, 3], [9, 0, 3, 0, 0, 0, 6, 0, 4]]") [//]: # (```) [//]: # () [//]: # (### Carbon Footprint Calculator) [//]: # (```bash) [//]: # (gptdeploy generate --description "Estimate a company's carbon footprint based on factors like transportation, electricity usage, waste production etc..." --test "Jina AI") [//]: # (```) [//]: # () [//]: # (### Real Estate Valuation Estimator) [//]: # (```bash) [//]: # (gptdeploy generate --description "Create a microservice that estimates the value of a property based on factors like location, property type, age, and square footage." --test "Berlin Friedrichshain, Flat, 100m², 10 years old") [//]: # (```) [//]: # () [//]: # (### Gene Sequence Alignment) [//]: # (```bash) [//]: # (gptdeploy generate --description "Align two DNA or RNA sequences using the Needleman-Wunsch algorithm" --test "AGTC, GTCA") [//]: # (```) [//]: # () [//]: # (### Barcode Generator) [//]: # (```bash) [//]: # (gptdeploy generate --description "Generate a barcode from a string" --test "Hello, welcome to GPT Deploy!") [//]: # (```) [//]: # () [//]: # (### File Compression) [//]: # (```bash) [//]: # (gptdeploy generate --description "Compress a file using the gzip algorithm" --test "content of the file: Hello, welcome to GPT Deploy!") [//]: # (```) [//]: # () [//]: # (### Watermarking Images) [//]: # (```bash) [//]: # (gptdeploy generate --description "Add a watermark (GPT Deploy) to an image" --test "https://images.unsplash.com/photo-1602738328654-51ab2ae6c4ff") [//]: # (```) [//]: # () [//]: # (### File Metadata Extractor) [//]: # (```bash) [//]: # (gptdeploy generate --description "Extract metadata from a file" --test "https://images.unsplash.com/photo-1602738328654-51ab2ae6c4ff") [//]: # (```) [//]: # () [//]: # (### Video Thumbnail Extractor) [//]: # (```bash) [//]: # (gptdeploy generate --description "Extract a thumbnail from a video" --test "http://techslides.com/demos/sample-videos/small.mp4") [//]: # (```) [//]: # () [//]: # (### Gif Maker) [//]: # (```bash) [//]: # (gptdeploy generate --description "Create a gif from a list of images" --test "https://images.unsplash.com/photo-1564725075388-cc8338732289, https://images.unsplash.com/photo-1584555684040-bad07f46a21f, https://images.unsplash.com/photo-1584555613497-9ecf9dd06f68") [//]: # (```) [//]: # () [//]: # () [//]: # (### Sound Visualizer) [//]: # () [//]: # (```bash) [//]: # (gptdeploy generate --description "Visualize a sound file and output the visualization as video combined with the sound" --test "some/mp3/file.mp3") [//]: # (```) [//]: # (## Upcoming Challenges) [//]: # (### Color Palette Generator) [//]: # (```bash) [//]: # (gptdeploy generate --description "creates aesthetically pleasing color palettes based on a seed color, using color theory principles like complementary or analogous colors" --test "red") [//]: # (```) [//]: # () [//]: # (### Depth Map Generator) [//]: # (```bash) [//]: # (gptdeploy generate --description "Generate a depth map from a 3d Object" --test "https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/models/wolf.obj") [//]: # (```) [//]: # () [//]: # (### ASCII Art Generator) [//]: # (```bash) [//]: # (gptdeploy generate --description "Convert image to ASCII art" --test "https://images.unsplash.com/photo-1602738328654-51ab2ae6c4ff") [//]: # (```) [//]: # (generate --description "Get a png as input and return a vectorized version as svg." --test "Make sure when you convert the image back, it looks similar." --path microservice --verbose) ## Technical Insights The graphic below illustrates the process of creating a microservice and deploying it to the cloud elaboration two different implementation strategies. ```mermaid 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] ``` 1. GPT Deploy identifies several strategies to implement your task. 2. It tests each strategy until it finds one that works. 3. 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. 4. 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. 5. 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. 6. If it fails 10 times in a row, it moves on to the next approach. ## 🔮 vision Use natural language interface to generate, deploy and update your microservice infrastructure. ## ✨ Contributors If 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 works - [ ] add video to README.md - [ ] bug: it can happen that the code generation is hanging forever - in this case aboard and redo the generation - [ ] new user has free credits but should be told to verify account Nice to have: - [ ] smooth rendering animation of the responses - [ ] if the user runs gptdeploy without any arguments, show the help message - [ ] don'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 mode - [ ] tests - [ ] clean up duplicate code - [ ] support popular cloud providers - lambda, cloud run, cloud functions, ... - [ ] support local docker builds - [ ] autoscaling enabled for cost saving - [ ] add more examples to README.md - [ ] support multiple endpoints - example: todolist microservice with endpoints for adding, deleting, and listing todos - [ ] support stateful microservices - [ ] The 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 feedback - [ ] support for other large language models like Open Assistent - [ ] for cost savings, it should be possible to insert less context during the code generation of the main functionality - no jina knowledge is required - [ ] use gptdeploy list to show all deployments - [ ] gptdeploy delete to delete a deployment - [ ] gptdeploy update to update a deployment - [ ] test 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 data - [ ] section for microservices built by the community - [ ] test feedback for playground generation (could be part of the debugging) - [ ] should we send everything via json in the text attribute for simplicity? - [ ] fix release workflow - [ ] after the user specified the task, ask them questions back if the task is not clear enough or something is missing Proposal: - [ ] just generate the non-jina related code and insert it into an executor template - [ ] think about strategies after the first approach failed?