semvec/pypi-packages huggingface dataset:
hugpee 0.1.1
A Python module which simplifies the creation of RESTful CRUD endpoints in hug, for peewee models.
License: MIT

# HugPee #
HugPee simplifies the creation of RESTful CRUD endpoints in hug, for peewee models.

# Installing HugPee #

Installing Hugpee is very simple, just run:

```bash
pip3 install hugpee --upgrade
```

# Usage #

Consider a scenario where `Person` is our peewee model. Creating the CRUD endpoints for hug is very simple with HugPee:

```python
hugpee.HugPee(Person)
```

And then load them to hug.

```python
@hug.extend_api()
def get_all_hugs():
    return [modelendpoint]
```

Here is the full working example:

```python
import hug
from peewee import SqliteDatabase, Model
from peewee import PrimaryKeyField, CharField, DateField, BooleanField
import hugpee

db = SqliteDatabase(":memory:")

class Person(Model):
    ID = PrimaryKeyField()
    name = CharField()
    birthday = DateField()
    is_relative = BooleanField()
    class Meta:
        database = db

db.create_tables([Person])

hugpee.HugPee(Person)

@hug.extend_api()
def get_all_hugs():
    return [hugpee]

```

This will create the following endpoints:

| HTTP ACTION | URI(s)                 | Accepted Parameters             |
|-------------|------------------------|---------------------------------|
| POST        | /person                | name, birthday, is_relative     |
| GET         | /person , /person/{id} | id                              |
| PUT         | /person , /person/{id} | id, name, birthday, is_relative |
| DELETE      | /person , /person/{id} | id                              |

Note that the primary key, in this case the id, can be provided either as part of the URI or as a parameter.
BadgeGenerator 1.0.0
A Python Badger Generator to use and automatize your projects
License: MIT
Keywords: Badge BadgeGen BadgeGenerator Generator Brasil Brazil shield Shields

A Python Badger Generator to use and automatize your projects

    Just use like:

        from BadgeGenerator import Badge

        Badge()
    
    -Ex:
        from BadgeGenerator import Badge

        Badge(color="red",logo="python",subject="Python")
nano-editorconfig 1.0.1
Wrapper around GNU nano to enable EditorConfig features
License: MIT
Requires Python: >=3.6

# nano-editorconfig, the `nec` plus ultra

The [GNU Nano](https://nano-editor.org/) editor does not provide any support for
[EditorConfig](https://editorconfig.org/) generalized configuration. This package offers a wrapper named `nec` that
generates a Nano configuration file on-the-fly, based on EditorConfig settings applicable to the first editable
file named on the command line.

Remark that many EditorConfig settings are not supported by Nano. For instance, Nano does not handle any other
text encoding besides UTF-8 (or even ASCII, if UTF-8 support is not built in). The EditorConfig settings that are
handled by this package are:

- `indent_style`
- `indent_size`
- `tab_width` (overriden by `indent_size`)
- `insert_final_newline`
- `max_line_length`
realtimeplt 0.0.1
Realtime plot of loss plot in Jupyter Notebook for Keras, PyTorch and others.
License: MIT
Requires Python: >3.5
Keywords: keras,pytorch,plot,chart,deep-learning

![PyPI status](https://img.shields.io/pypi/status/livelossplot.svg)
![MIT license - PyPI](https://img.shields.io/pypi/l/livelossplot.svg)
![Python version - PyPI](https://img.shields.io/pypi/pyversions/livelossplot.svg)
[![GitHub Workflow Status](https://img.shields.io/github/workflow/status/stared/livelossplot/Python%20package)](https://github.com/stared/livelossplot/actions)

## Installation
```bash
pip install realtimeplt
```
realtimeplt is a Python library made by VisionBrain which foucuses on getting the Live training loss plot in Jupyter Notebook for Keras, PyTorch and TensorFlow.Our main objective is to bring the ability to  visualize the data hyperparameters in real time.


## Most Noticeable changes:
-Runs on the latest version of python
-Runs on the latest version of keras
-Runs on the latest version of pytorch
-Runs on the latest version of tensorflow



## Use 
The major dependency of realtimeplt is livelossplot
```python
from livelossplot import PlotLossesKeras
model.fit(X_train, Y_train,
          epochs=10,
          validation_data=(X_test, Y_test),
          callbacks=[PlotLossesKeras()],
          verbose=0)
```

## Supported DataScience Libraries
Note*** Better results when used with Jupyter Notebook.

- Matplotlib
- Seaborn
- SciPy
- Bokeh

## Created by [Lead Developer ](https://aryan05.github.io/) & the VisionBrain team, MNNIT Allahabad. Thanks for using our library. 

## Note*** realtimeplt is a faster and accurate version of [liveplot](https://github.com/PhilReinhold/liveplot) & in no manner VisionBrain is trying to coppy liveplot.