semvec/pypi-packages huggingface dataset:
tgcc 1.0.0
TechGeeks Compiler Collections
Requires Python: >=3.6

![Logo](assets/favicon.png)<br>
# TGCC
[![PyPI Release Create](https://github.com/TechGeeks-Dev/tgcc/actions/workflows/Release-Create.yaml/badge.svg)](https://github.com/TechGeeks-Dev/tgcc/actions/workflows/Release-Create.yaml)<br>

## Installation

```shell
pip install tgcc
```

## Usage

First create a `tgcc.json` file with a **`compiler`** key:
```json
{
    "compiler": "compiler.json"
}
```
You can replace `compiler.json` with anything else and create that file with the value of:

```json
{
    "file": "file.md",
    "Content-Type": "text/markdown",
    "to": "html"
}
```

Then run:
```shell
tgcc start
```
abhishek-helloworld 0.0.2
Say Hello!

# Hello World

This is an example project demonstrating how to publish a python module to PyPI. 

## Installation

Run the following to install:

```python
pip install helloworld
```

## Usage

```python
from helloworld import say_hello

# Generate "Hello, World!"
say_hello()

# Generate "Hello, Everybody!"
say_hello("Everybody")
```

# Developing Hello World

To install helloworld, along with the tools you need to develop and run tests, run the following in your virutalenv:

```bash
$ pip install -e .[dev]
$ pip install -e .'[dev]' # for ZSH 
$ pip install -e ."[dev]" # for Windows 
```
fastrlapi 0.0.1
reinforcement learning high-level API.

### Reinforcement Learning API made simple - FAST_RLAPI

fastrlapi is a high level reinforcement learning api focusing on ease of use and simplicity.
Written in Python and running on top of established reinforcement learning libraries like
[tf-Agents](https://github.com/tensorflow/agents), 
[tensorforce](https://github.com/tensorforce/tensorforce) or 
[keras-rl](https://github.com/keras-rl/keras-rl).


### Examples
---
````
from fastrlapi.agents import PpoAgent
from fastrlapi.callbacks import plot

ppoAgent = PpoAgent('CartPole-v0')
ppoAgent.train([plot.State(), plot.Loss(), plot.Rewards()])
````


#### More Detailed
````
from fastrlapi.agents import PpoAgent
from fastrlapi.callbacks import plot

ppoAgent = PpoAgent( 'Orso-v1',fc_layers=(500,500,500))
ppoAgent.train([plot.State(), plot.Loss(), plot.Rewards(), plot.Actions(), 
                plot.StepRewards(), plot.Steps(), plot.ToMovie()], 
                learning_rate = 0.0001, num_iterations = 500, max_steps_per_episode=50 )
````

### Available Algorithms and Backends
---

|algorithm | [tf-Agents](https://github.com/tensorflow/agents) | [tensorforce](https://github.com/tensorforce/tensorforce) | [keras-rl (suspended)](https://github.com/keras-rl/keras-rl) | fastrlapi class name |
|----------|:---------:|:-----------:|:--------:| :---: | 
|[CEM](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.81.6579&rep=rep1&type=pdf) |`not available`  |`not available`  |`yes`  | CemAgent | 
|[Dqn](https://arxiv.org/abs/1312.5602)           |`yes`            |`yes`    |`yes`            | DqnAgent | 
|[Double Dqn](https://arxiv.org/abs/1509.06461)   |`open`           |`not available`    |`yes`  | DoubleDqnAgent|
|[Dueling Dqn](https://arxiv.org/abs/1511.06581)  |`not available`  |`yes`    |`yes`            | DuelingDqnAgent|
|[Ppo](https://arxiv.org/abs/1707.06347)          |`yes`            |`yes`    |`not available`  | PpoAgent |
|Random                                           |`yes`            |`yes`    |`not available`  | RandomAgent |
|[REINFORCE](http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf)  |`yes`  |`yes` |`not available`| ReinforceAgent | 
|[SAC](https://arxiv.org/abs/1801.01290)          |`preview`        |`not available` |`not available`|SacAgent|


### Installation
---
Install from pypi using pip:

```python
pip install fastrlapi
```
python-postx 1.0.4
PostX is a package that provides a simple interface for posting messages to multiple platforms.
Requires Python: >=3.6
Keywords: messaging,slack,teams,messages

[![pipeline status](https://gitlab.com/mediatech15/python-postx/badges/main/pipeline.svg)](https://gitlab.com/mediatech15/python-postx/-/commits/main) 
[![coverage report](https://gitlab.com/mediatech15/python-postx/badges/main/coverage.svg)](https://gitlab.com/mediatech15/python-postx/-/commits/main)
[![PyPI version](https://badge.fury.io/py/python-postx.svg)](https://badge.fury.io/py/python-postx)


# Python PostX

PostX is a package that provides a simple interface for posting messages to multiple platforms.
The user has the ability to send the same message to multiple services or to an indivdual service. 
The same message is translated to the appropriate calls for the each platform.

Full Docs: [Here](https://mediatech15.gitlab.io/python-postx/)

## Getting Started

Installing via pip

`pip install python-postx`

## Quick Start

```python
from postx import Broker
from postx.message import Message

# Create the broker
broker = Broker()

# You can register as many endpoints as you wish of any combination
broker.register('slack', 'your slack webhook url')
broker.register('teams', 'your teams webhook url')

# Make your message
message = Message()
message.title('My Awesome Update')
message.sub_title('This is an awesome update')
message.text('The update is for all people to see and affects everything')
message.list_unordered(['item 1', 'item 2'])
message.link('Read More Here', 'link to more information')

# Or a shorter way
message = Message()
message.title('My Awesome Update') \
    .sub_title('This is an awesome update') \
    .text('The update is for all people to see and affects everything') \
    .list_unordered(['item 1', 'item 2']) \
    .link('Read More Here', 'link to more information')

# Or even shorter
message = Message()
message.title('My Awesome Update') \
    .sub('This is an awesome update') \
    .text('The update is for all people to see and affects everything') \
    .ul(['item 1', 'item 2']) \
    .link('Read More Here', 'link to more information')

# Send the message to all
broker.send(message)

# Send the message to slack only
broker.send_single_service(message, 'slack')

# Send the message to a single endpoint
broker.send_single_link(message, 'your registered endpoint url')
```

## Supported targets

- Microsoft Teams
- Slack

## Meta

Kyle Mason - [Mediatech15 - Gitlab](https://gitlab.com/mediatech15)

Distributed under the MIT license. See LICENSE for more information.
c2d-api 0.0.3
Python package for API of Chat2Desk
Requires Python: >=3.7, <4
Keywords: chat2desk,chat,c2d

## Introduction

Package c2d_api provide tools to interact with API of Chat2Desk. You can simply use this tools to:
- Send messages to clients
- Assign chats to different operators
- Assign tags to clients
- Get statistics
- And more actions

Full description of API you can find on the [official site](https://chat2desk.com/)

## Installation
To install package open your terminal and use command:
```sh
pip install c2d-api
```

## Getting started

After package installation you need to do import:
```sh
from c2d_api.api import *
```

Then create API object using your Chat2Desk access token:
```sh
api = Chat2DeskApi(access_token='YOUR_ACCESS_TOKEN')
```
Now you can use this object to interact with ChatToDesk API.

## Using methods
To create client you need to use code:
```sh
api.clients_post(phone='7XXXXXXXXXX', transport='YOUR_TRANSPORT', channel_id='YOUR_CHANNEL_ID', nickname='ExampleClient')
```
After client created you can send message to him:
```sh
message = 'This is example message!'
api.messages_post(client_id='CLIENT_ID', text=message)
```
You can get messages from chats. For example, if you need to get 500 messages from 'YOUR_TRANSPORT', you can use code:
```sh
api.messages_get(transport='YOUR_TRANSPORT', limit=500)
```

All methods copy the structure of API requests, so you can use the official documentation of Chat2Desk API to get them all.