semvec/pypi-packages huggingface dataset:
bubble-client 0.7.3
Python client for Bubble.io APIs
Requires Python: >=3.7, <4

[Bubble.io]: https://bubble.io/

Bubble-Client
=============

[![PyPI](https://img.shields.io/pypi/v/bubble-client.svg)](https://pypi.org/project/bubble-client)
[![License](https://img.shields.io/github/license/Refty/bubble-client)](LICENSE)
[![Code style](https://img.shields.io/badge/code%20style-black-black)](https://github.com/ambv/black)

Python client for the [Bubble.io][] APIs

Installation
------------

```shell
pip install bubble-client
```

Examples
--------

* Get users (or any Bubble thing, really):

```python
>>> from bubble_client import configure, BubbleThing
>>> configure(base_url=..., token=...)

>>> class User(BubbleThing):
...     pass

>>> async for user in User.get():
...     print(user)
User({'name': 'Beatrix Emery', ...})
User({'name': 'Dr. Jekyll', ...})

>>> user.name
'Dr. Jekyll'
```

* Change values:

```python
>>> user.name = "Mr. Hyde"
>>> await user.save()
>>> user.name
'Mr. Hyde'
```

* Count users:

```python
>>> User.count()
2
```

* Add a new user:

```python
>>> user = User(name="Sir Charles Emery")
>>> await user.save()
>>> User.count()
3
```

* Join stuff!

```python
>>> class Pet(BubbleThing):
...     pass

>>> pet = await pet.get_one()
>>> await pet.join("created_by", User)

>>> pet.type
'dog'
>>> pet.created_by
User({'name': 'Mr. Hyde', ...})
>>> pet.created_by.name
'Mr. Hyde'
```

* Also works on cursors!

```python
>>> async for pet in Pet.get().join("created_by", User):
...     print(pet)
Pet({'type': 'dog', 'created_by': User({'name': 'Mr. Hyde', ...}), ...})
Pet({'type': 'donkey', 'created_by': User({'name': 'Beatrix Emery', ...}), ...})
```
aimodelshare-nightly 0.0.104
Deploy locally saved machine learning models to a live rest API and web-dashboard.  Share it with the world via modelshare.org
Requires Python: >=3.7


# aimodelshare
### The mission of the AI Model Share Platform (website w/ integrated Python library) is to provide a trusted non profit repository for machine learning model prediction APIs (python library + integrated website at modelshare.org.  A beta version of the platform is currently being used by Columbia University students, faculty, and staff to test and improve platform functionality.

### In a matter of seconds, data scientists can launch a model into this infrastructure and end-users the world over will be able to engage their machine learning models.

* ***Launch machine learning models into scalable production ready prediction REST APIs using a single Python function.*** 

* ***Details about each model, how to use the model's API, and the model's author(s) are deployed simultaneously into a searchable website at modelshare.org.*** 

* ***Deployed models receive an individual Model Playground listing information about all deployed models. Each of these pages includes a fully functional prediction dashboard that allows end-users to input text, tabular, or image data and receive live predictions.*** 

* ***Moreover, users can build on model playgrounds by 1) creating ML model competitions, 2) uploading Jupyter notebooks to share code, 3) sharing model architectures and 4) sharing data... with all shared artifacts automatically creating a data science user portfolio.*** 

# Use the aimodelshare Python library to deploy your model, create a new ML competition, and more.
* [Tutorials for deploying models](https://www.modelshare.org/search/deploy?search=ALL&problemdomain=ALL&gettingstartedguide=TRUE&pythonlibrariesused=ALL&tags=ALL&pageNum=1).

# Find model playground web-dashboards to generate predictions now.
* [View deployed models and generate predictions at modelshare.org](https://www.modelshare.org)

# Installation

You can then install aimodelshare from PyPi
```
pip install aimodelshare-nightly
```