Dev-GPT: Your Automated Development Team

⚠️ This is an experimental version. ⚠️

Product Manager

Developer

DevOps

Tell your AI team what microservice you want to build, and they will do it for you. Your imagination is the limit!

Welcome to Dev-GPT, where we bring your ideas to life with the power of advanced artificial intelligence! Our automated development team is designed to create microservices tailored to your specific needs, making your software development process seamless and efficient. Comprised of a virtual Product Manager, Developer, and DevOps, our AI team ensures that every aspect of your project is covered, from concept to deployment. ## Quickstart ```bash pip install dev-gpt dev-gpt generate ``` ### Requirements - OpenAI key with access to gpt-3.5-turbo or gpt-4 - if you want to enable your microservice to search for web content, you need to set the GOOGLE_API_KEY and GOOGLE_CSE_ID environment variables. More information can be found [here](https://developers.google.com/custom-search/v1/overview). ```bash dev-gpt configure --openai_api_key dev-gpt configure --google_api_key (optional if you want to use google custom search) dev-gpt configure --google_cse_id (optional if you want to use google custom search) ``` 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. ## Docs ### Generate Microservice ```bash dev-gpt generate \ --description "" \ --model \ --path ``` To generate your personal microservice two things are required: - A `description` of the task you want to accomplish. (optional) - The `model` you want to use - either `gpt-3.5-turbo` or `gpt-4`. `gpt-3.5-turbo` is ~10x cheaper, but will not be able to generate as complex microservices. (default: largest you have access to) - A `path` on the local drive where the microservice will be generated. (default: ./microservice) 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 or $0.05 to $0.30 for GPT-3.5-Trubo. ### 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 dev-gpt 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 dev-gpt 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 Dev-GPT. ### Compliment Generator ```bash dev-gpt generate \ --description "The user writes something and gets a related deep compliment." \ --model gpt-4 ```

### Extract and summarize news articles given a URL ```bash dev-gpt generate \ --description "Extract text from a news article URL using Newspaper3k library and generate a summary using gpt. Example input: http://fox13now.com/2013/12/30/new-year-new-laws-obamacare-pot-guns-and-drones/" \ --model gpt-4 ``` News Article Example

### Chemical Formula Visualization ```bash dev-gpt generate \ --description "Convert a chemical formula into a 2D chemical structure diagram. Example inputs: C=C, CN=C=O, CCC(=O)O" \ --model gpt-4 ```

### 2d rendering of 3d model ```bash dev-gpt 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 - example input: https://graphics.stanford.edu/courses/cs148-10-summer/as3/code/as3/teapot.obj" \ --model gpt-4 ``` 2D Rendering of 3D Model

### Product Recommendation ```bash dev-gpt generate \ --description "Generate personalized product recommendations based on user product browsing history and the product categories fashion, electronics and sport. Example: Input: browsing history: prod1(electronics),prod2(fashion),prod3(fashion), output: p4(fashion)" \ --model gpt-4 ```

### Hacker News Search ```bash dev-gpt 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)" \ --model gpt-4 ````

### Animal Detector ```bash dev-gpt 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), Example input: https://images.unsplash.com/photo-1444212477490-ca407925329e" \ --model gpt-4 ``` Animal Detector

### Meme Generator ```bash dev-gpt generate \ --description "Generate a meme from an image and a caption. Example input: https://media.wired.com/photos/5f87340d114b38fa1f8339f9/master/w_1600%2Cc_limit/Ideas_Surprised_Pikachu_HD.jpg, TOP:When you discovered GPT Dev" \ --model gpt-4 ```

### Rhyme Generator ```bash dev-gpt generate \ --description "Given a word, return a list of rhyming words using the datamuse api" \ --model gpt-4 ```

### Word Cloud Generator ```bash dev-gpt generate \ --description "Generate a word cloud from a given text" \ --model gpt-4 ```

### 3d model info ```bash dev-gpt generate \ --description "Given a 3d object, return vertex count and face count. Example: https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/models/wolf.obj" \ --model gpt-4 ```

### Table extraction ```bash dev-gpt generate \ --description "Given a URL, extract all tables as csv. Example: http://www.ins.tn/statistiques/90" \ --model gpt-4 ```

### Audio to mel spectrogram ```bash dev-gpt generate \ --description "Create mel spectrogram from audio file. Example: https://cdn.pixabay.com/download/audio/2023/02/28/audio_550d815fa5.mp3" \ --model gpt-4 ```

### Text to speech ```bash dev-gpt generate \ --description "Convert text to speech" \ --model gpt-4 ```

### Heatmap Generator ```bash dev-gpt generate \ --description "Create a heatmap from an image and a list of relative coordinates. Example input: 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 ```

### QR Code Generator ```bash dev-gpt generate \ --description "Generate QR code from URL. Example input: https://www.example.com" \ --model gpt-4 ```

### Mandelbrot Set Visualizer ```bash dev-gpt generate \ --description "Visualize the Mandelbrot set with custom parameters. Example input: center=-0+1i, zoom=1.0, size=800x800, iterations=1000" \ --model gpt-4 ```

### Markdown to HTML Converter ```bash dev-gpt generate --description "Convert markdown to HTML" ```

[//]: # (## TO BE TESTED) [//]: # (### Password Strength Checker) [//]: # (```bash) [//]: # (dev-gpt 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) [//]: # (dev-gpt generate --description "Convert text to morse code" --test "Hello, welcome to GPT Dev!") [//]: # (```) [//]: # (### IP Geolocation) [//]: # (```bash) [//]: # (dev-gpt generate --description "Given an IP address, return the geolocation information" --test "142.251.46.174") [//]: # (```) [//]: # (### Currency Converter) [//]: # (```bash) [//]: # (dev-gpt generate --description "Converts any currency into any other" --test "1 usd to eur") [//]: # (```) [//]: # (### Image Resizer) [//]: # (```bash) [//]: # (dev-gpt generate --description "Given an image, resize it to a specified width and height" --test "https://images.unsplash.com/photo-1602738328654-51ab2ae6c4ff") [//]: # (```) [//]: # (### Weather API) [//]: # (```bash) [//]: # (dev-gpt generate --description "Given a city, return the current weather" --test "Berlin") [//]: # (```) [//]: # () [//]: # (### Sudoku Solver) [//]: # (```bash) [//]: # (dev-gpt 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) [//]: # (dev-gpt 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) [//]: # (dev-gpt 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) [//]: # (dev-gpt generate --description "Align two DNA or RNA sequences using the Needleman-Wunsch algorithm" --test "AGTC, GTCA") [//]: # (```) [//]: # () [//]: # (### Barcode Generator) [//]: # (```bash) [//]: # (dev-gpt generate --description "Generate a barcode from a string" --test "Hello, welcome to Dev-GPT!") [//]: # (```) [//]: # () [//]: # (### File Compression) [//]: # (```bash) [//]: # (dev-gpt generate --description "Compress a file using the gzip algorithm" --test "content of the file: Hello, welcome to Dev-GPT!") [//]: # (```) [//]: # () [//]: # (### Watermarking Images) [//]: # (```bash) [//]: # (dev-gpt generate --description "Add a watermark (Dev-GPT) to an image" --test "https://images.unsplash.com/photo-1602738328654-51ab2ae6c4ff") [//]: # (```) [//]: # () [//]: # (### File Metadata Extractor) [//]: # (```bash) [//]: # (dev-gpt generate --description "Extract metadata from a file" --test "https://images.unsplash.com/photo-1602738328654-51ab2ae6c4ff") [//]: # (```) [//]: # () [//]: # (### Video Thumbnail Extractor) [//]: # (```bash) [//]: # (dev-gpt generate --description "Extract a thumbnail from a video" --test "http://techslides.com/demos/sample-videos/small.mp4") [//]: # (```) [//]: # () [//]: # (### Gif Maker) [//]: # (```bash) [//]: # (dev-gpt 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) [//]: # (dev-gpt 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) [//]: # (dev-gpt 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) [//]: # (dev-gpt 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) [//]: # (dev-gpt 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. Dev-GPT 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. Dev-GPT 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 dev-gpt 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 dev-gpt list to show all deployments - [ ] dev-gpt delete to delete a deployment - [ ] dev-gpt 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?