semvec/pypi-packages huggingface dataset:
Flask-Plugin 0.1.0
An extension to add support of Plugin in Flask.
License: MIT
Requires Python: >=3.7

# Flask-Plugin

![Python](https://img.shields.io/badge/Python-3.7%2B-blue) ![License](https://img.shields.io/github/license/guiqiqi/flask-plugin) ![test](https://github.com/guiqiqi/flask-plugin/actions/workflows/unittest.yml/badge.svg) ![pylint](https://github.com/guiqiqi/flask-plugin/actions/workflows/pylint.yml/badge.svg) [![codecov](https://codecov.io/gh/guiqiqi/flask-plugin/branch/main/graph/badge.svg?token=DE329H13JM)](https://codecov.io/gh/guiqiqi/flask-plugin)


[中文版本](https://github.com/guiqiqi/flask-plugin/blob/main/readme-zh.md)

An extension to add support of Plugin in Flask.

**Features:**

1. Define plugin routes in the same way as Application, Blueprint, while providing all the Flask features (Template rendering, url_for, message flashing, signals, etc.)
2. Each plugin can be started, stopped, reloaded while Flask is *running*.
3. Configured with Flask, no need to configure separately.
4. Auto-discovery and management for plugins.

## Install

Install Flask-Plugin using pip:

```bash
pip install flask-plugin
```

## Quick Start

1. Entering the `example` directory, you will find the following directory structure, the plugin `hello` inside  `plugins` directory:

   ```
   example
   ├── app.py
   └── plugins
       └── hello
           ├── __init__.py
           └── plugin.json
   ```

2. The plugin manager is loaded in the `app.py` file, and the hello plugin is started:

   ```python
   from flask import Flask
   from flask_plugin import PluginManager

   app = Flask(__name__)
   manager = PluginManager(app)
   plugin = manager.find(id_='962e3b6cd8b74d02a5a02f1e3651ef87')
   if plugin:
       manager.load(plugin)
       manager.start(plugin)

   ...
   # API Management code here
   app.run()
   ```

3. Define plugin info in `SayHello/plugin.json` file:

   ```json
   {
        "id": "962e3b6cd8b74d02a5a02f1e3651ef87",
        "domain": "hello",
        "plugin": {
            "name": "Greeting",
            "author": "Doge",
            "summary": "Hello Flask-Plugin."
        },
        "releases": []
   }
   ```

3. Instantiated the `Plugin` in `SayHello/__init__.py` and define the route as you did in `Flask`:

   ```python
   from flask_plugin import Plugin
   from flask import redirect, url_for

   plugin = Plugin()

   ...
   # Other routes defined here

   @plugin.route('/say/<string:name>', methods=['GET'])
   def say(name: str):
       return 'Hello ' + name
   ```

4. Accessing `/plugins/hello/` and see the greeting:

   ```
   Hello Anonymous!
   ```

   Stop the plugin with accessing `/api/stop/347336b4fcdd447985aec57f2bc5793c`, check url above again, and get a `HTTP 404` error.

Documentation for Flask-Plugin avaliable on: [Flask-Plugin Documentation](https://docs.flask-plugin.org)

## Thanks

This project is based on many open source projects of the [Pallets group](https://palletsprojects.com/), and I would like to express my thanks here.

Also thanks to my family and friends.
mlpipeline-1 0.0.7
An AutoML library
License: MIT
Keywords: AutoML

Currently it prepares the data ready for a Machine Learning Classification problem (mainly for now Tabular Dataset). I am soon planning to add other features for Regression problem as well as for Clustering based Problem. My purpose of creating this project is to build an AutoML library, which reduces writing more code to a create a model.
How you can use this is by following the below step:
```
pip install mlpipeline_1==0.0.7
```
```
from ml_pipeline.pipeline import ml_pipe
```
Then you need to pass certain parameters to the ml_pipe function, the first one is the "location of the csv file" which you want to do Classification on, second parameter being the "target variable name", and the last one being the "split size" (value between 0 - 1) i.e., what percent of the whole dataset you want to use as a test set.
```
X_train, X_test, y_train, y_test = ml_pipe(file_path, target_var, split_ratio)
```
The output of the above function you can pass to any Classification model for training.

Soon planning to coming up with more and more features. Planning to make this a full-fledged AutoML library.

Change Log
==========

0.0.7 (18/02/2022)
--------------------
- This is my Seventh Release of the project with some changes solving some errors from the previous version and updating the readme file for better clarity for the users on how to use this.
shellcord 0.2.0-6dev0
Easily create runbooks from your shell commands
License: MIT
Requires Python: >=3.7,<4.0
Keywords: runbook,shell

# Shellcord
Generate runbooks and READMEs from your shell sessions.




## Installation

## Usage


## Supported platforms
Note that this tool is in alpha and still not thoroughly tested on shells/platforms besides the following:
### Python
* 3.7
* 3.9
### Operating systems
* Linux `5.11.0-49-generic x86_64 GNU/Linux `

### Shells
Confirmed working in:
* zsh: `zsh 5.8 (x86_64-ubuntu-linux-gnu)`
* bash: `GNU bash, version 5.1.4(1)-release (x86_64-pc-linux-gnu)`



### Limitations
Ideally, this tool would be a simple invocation of [script](https://man7.org/linux/man-pages/man1/script.1.html) with some logic to generate a runbook from that typescript file. However, given that `script` only uses a psuedo-terminal, getting the exit code is non-trivial without basically making your own shell. So we do the next best thing and use each individual shells `precmd/PROMPT_COMMAND` instead. 

Currently, shellcord collects:
* The command being run
* The exit code of that command

Ideally though, it would also be able to get stdout so the generated runbook has example output.

## Development
This tool is currently under development and any support is more than welcome, especially if you want to get shellcord working in your shell of choice.

Please cut issues as you see fit based on usage and feel free to send pull-requests.

### Testing 
Run the tests `poetry run python3 -m pytest`



### How it works
Shellcord works by modifying the shell's `precmd/PROMP_COMMAND` or whatever equivalent with our own function which will:
* Generate a unique id for each command
* Get the exit code of the last command run
* Get the actual command which was run last
* Dump all that data to a `scord-log` file

Once all the command data has been collected, shellcord will then use the `scord-log` file to generate a runbook based on the parameters selected.


### Components
* init.sh: Used to setup a scord session and modify precmd
* de-init.sh: Used to close the session and undo all the precmd work 
* shellcord.py: Used to generate runbooks and tag commands
fpsensor 0.2.6
Python library for ZhianTec fingerprint sensors
License: MIT
Requires Python: >=3.7,<4.0
Keywords: python,utilities

# FPSensor 
[![PyPI version](https://badge.fury.io/py/fpsensor.svg)](https://badge.fury.io/py/fpsensor) 
[![Docs Status](https://readthedocs.org/projects/fpsensor/badge/?version=latest)](https://fpsensor.readthedocs.io/en/latest/?badge=latest)
[![License](https://img.shields.io/:license-mit-blue.svg?style=flat-square)](https://badges.mit-license.org)

This library allows to use the ZhianTec ZFM-x fingerprint sensors. Some other models like R302, R303, R305, R306, R307, 
R551 and FPM10A also work.

**Note:** This library is based on [pyfingerprint](https://github.com/bastianraschke/pyfingerprint) and 
[embutils](https://github.com/cwichel/embutils). 

## Installation
You can get the packaged version from [PyPI](https://pypi.org/project/fpsensor/):
```
pip install fpsensor
```