dev-gpt/README.md

<h1 align="center">
GPT Deploy: One line to generate them all 🧙🚀
</h1>

<p align="center">
<img src="res/gpt-deploy-logo.png" alt="Jina NOW logo" width="150px">
</p>
<p align="center">
Turn your natural language descriptions into fully functional, deployed AI-powered microservices with a single command!
Your imagination is the limit!
</p>

<p align="center">
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[![Watch the video](res/thumbnail.png)](https://user-images.githubusercontent.com/11627845/231530421-272a66aa-4260-4e17-ab7a-ba66adca754c.mp4)

</p>
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.

## Quickstart
### Requirements
- OpenAI key with access to GPT-3.5 or GPT-4 

### Installation
```bash
pip install gptdeploy
gptdeploy configure --key <your openai api key>
```
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 "<description of the microservice>" \
--test "<specification of a test scenario>" \
--model <gpt-3.5 or gpt-4> \
--path </path/to/local/folder>
```
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 <path to microservice>
```


### 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 <path to microservice>
```


### Delete Microservice
To save credits you can delete your microservice via the following commands:
```bash
jc list # get the microservice id
jc delete <microservice id>
```

## 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
```
<img src="res/compliment_example.png" alt="Compliment Generator" width="400" />


### 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
```
<img src="res/news_article_example.png" alt="News Article Example" width="400" />

### 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
```
<img src="res/chemical_formula_example.png" alt="Chemical Formula Visualization" width="400" />

### 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
```
<img src="res/obj_render_example.gif" alt="2D Rendering of 3D Model" width="400" />

### 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
```
<img src="res/recommendation_example.png" alt="Product Recommendation" width="400" />

### 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
````
<img src="res/hacker_news_example.png" alt="Hacker News Search" width="400" />

### 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
```
<img src="res/animal_detector_example.png" alt="Animal Detector" width="400" />

### 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
```
<img src="res/meme_example.png" alt="Meme Generator" width="400" />

### 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
```
<img src="res/rhyme_generator_example.png" alt="Rhyme Generator" width="400" />

### 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
```
<img src="res/word_cloud_example.png" alt="Word Cloud Generator" width="400" />

### 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
```
<img src="res/obj_info_example.png" alt="3D Model Info" width="400" />

### 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
```
<img src="res/table_extraction_example.png" alt="Table Extraction" width="400" />

### 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
```
<img src="res/audio_to_mel_example.png" alt="Audio to Mel Spectrogram" width="400" />

### Text to speech
```bash
gptdeploy generate \
--description "Convert text to speech" \
--test "Hello, welcome to GPT Deploy!" \
--model gpt-4 \
--path microservice
```
<a href=res/text_to_speech_example.wav><img src="res/text_to_speech_example.png" alt="Text to Speech" width="400" /></a>

<audio controls>
  <source src="res/text_to_speech_example.wav" type="audio/mpeg">
  Your browser does not support the audio element.
</audio>

### 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
```
<img src="res/heatmap_example.png" alt="Heatmap Generator" width="400" />

### QR Code Generator
```bash
gptdeploy generate \
--description "Generate QR code from URL" \
--test "https://www.example.com" \
--model gpt-4 \
--path microservice
```
<img src="res/qr_example.png" alt="QR Code Generator" width="400" />

### 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
```
<img src="res/mandelbrot_example.png" alt="Mandelbrot Set Visualizer" width="400" />


### Markdown to HTML Converter

```bash
gptdeploy generate --description "Convert markdown to HTML" --test "# Hello, welcome to GPT Deploy!"
```

<img src="res/markdown_to_html_example.png" alt="Markdown to HTML Converter" width="400" />



[//]: # (## 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 &#40;GPT Deploy&#41; 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?