semvec/pypi-packages huggingface dataset:
djangodeployer 0.1.2
Deploy django project

# djangodeployer  
Deploy django project with nginx, gunicorn, supervisor, postgresql on debian.

### Quick start    
Creates the basic config file.  
``` bash
djangodeployer init
```
Edit the basic configuration dj_config.json.  
``` jsonc
{
    "IP_ADDRESS": "0.0.0.0",        # server public ip address
    "SERVER_NAME": "django-server", # hostname of the server
    "LINUX_USER": "dj",             # server username
    "DJ_PROJ": "djangosite",        # django project name
    "PG_USER": "djadmin",           # postgresql database username
    "PG_DB": "djangodb",            # postgresql database name
    "DOMAIN_NAME": "mywebsite.com"  # domain name
}
```
Deploy with the command:
``` bash
djangodeployer deploy
```
### Requirements
 - Python3.6+

### Installation
```python3 -m pip install djangodeployer```

### Acknowledgments
 Most of the functions use the code from Corey Schafer's youtube channel:
 - https://www.youtube.com/watch?v=Sa_kQheCnds&t=2952s  
 - https://www.youtube.com/watch?v=goToXTC96Co  
 - CoreyMSchafer - https://github.com/CoreyMSchafer  
The code is wrapped in a python package to automate the deployment.  

### License
MIT
pegasus-dados 0.2.0
Plataforma Eletrônica de Governança e Accountability do SUS - Módulo dados
Requires Python: >=3.6

The PegaSUS project is divided into n modules. One of the implemented modules,
called dados, allows to create a schema of a PostgreSQL database, previously
created in the user computer, and to insert data that belongs to the following
information health systems of the Brazilian Public Health System (SUS): CNES,
SIH, SIA, SIM, SINASC or SINAN (more to come). The dados module also also allows
to directly read main data files (in DBC format) and auxiliary data files (in
DBF or CNV format) as pandas DataFrame objects. This second option is recommended
in the case of more punctual consultation.

To use the dados module by means of a PostgreSQL database it is necessary to
install the PostgreSQL DBMS (https://www.postgresql.org/download/) and it is
recommended to have a database management software such as pgAdmin (https://www.pgadmin.org/download/)
or DBeaver Community (https://dbeaver.io/), to proper visualize the structured
data.

To use the dados module on Linux or MacOS platforms it is necessary to install
the libffi dependency:
1) Linux: `$ sudo apt install libffi-dev`
2) MacOS: `$ brew install libffi`

For use on Windows platforms it is necessary to install the executable program
(*.exe*) dbf2dbc, which is part of the Datasus Tabwin program. To accomplish this,
download the "Tab415.zip" folder by selecting the eponymous link contained in the
first line of the "Name" column of the table that appears in http://www2.datasus.gov.br/DATASUS/index.php?area=060805&item=3.
Unpack the "Tab415.zip" folder, copy the executable "dbf2dbc.exe" and the file
"IMPBORL.DLL" (the unpacked folder "Tab415" and its *.zip* can be deleted) to a
newly created folder named "dbf2dbc" in the root directory of the user's computer,
which generally has the following form on Windows platforms:

```C:\```

Finally, the data module is installed using the command(on Linux platforms):

```$ sudo pip install pegasus-dados```
spineapi 0.1.0
Access your python functions through HTTP requests.
Requires Python: >=3.6

# Spine API
Access your python functions through HTTP requests.

It is simple, clean, and easy to use. It works with jupyter notebooks too.

## 1. Setup your api server

**Prerequisites**
1. Make sure your server is accessible through port 3000
2. Have Node.js installed on your server

**Installation**
```bash
$ git clone https://github.com/spineapi/spine-api
$ cd spine-api/server
$ npm install
```

**Run server**
```bash
$ node server.js
```
Now you can access your server through http://YOUR_SERVER_IP:3000. Copy the passcode.

### Installation
```bash
$ pip instal spineapi
```

## Connect your python script to the server
**1. Import the library**
```Python
from spineapi import Connection
```

**2. Define your function**
```Python
def hello_function (input):
  # ...
  # do something
  # ...
  return output
```

**3. Specify ```name``` and ```description``` to initialize your project**
```Python
spine_connection = Connection(
  project_path="hello_project",
  project_name="My first project",
  description="Arithmetic operations",
  base_url="http://localhost:3000",
  passcode="xxxx-xxxx-xxxx-xxxxx",
  author="", # Optional
  link="" # Optional
)
```

**4. Register your function(s)**
```Python
spine_connection.register_function(
  pathname='hello_function',
  function=hello_function,
  # ============ Optional ==================
  # Set True if you want to protect this API
  requiresAuth=False,
  authToken="xxx",
  # ========================================
)
```
The function will be accessible through ```/api/hello_project/hello_function```

**5. Run**
```
spine_connection.run()
```
That's it! You can now communicate with your ML model through HTTP post requests.

**6. Send requests**

**Note** You have to first run ```JSON.stringify(input)``` for the request data.
```javascript
const data = {
  input: JSON.stringify(YOUR_INPUT)
  // ============ Optional ==================
  // Required if the API is protected
  authToken: "xxx",
  // ========================================
}
```

Post request body to the endpoint.
```javascript
const url = 'http://localhost:3000/api/hello_project/hello_function';

fetch(
  url,
  {
    method: 'POST',
    body: JSON.stringify(data),
    headers: new Headers({ 'Content-Type': 'application/json' })
  }
)
.then(res => res.json())
.catch(error => console.error('Error:', error))
.then(response => console.log('Success:', response));
```

# License
[MIT](https://github.com/northfoxz/spine-api/blob/master/LICENSE)
aricma-pretty-print 0.0.3
A small printer package to get some better looking print statements for any cli.
Requires Python: >=3.6

# Pretty Print

This is a simple printer package. 
You can use it to print more useful information during development and also more readable prints for any production cli.  

The package got inspired by the [jest] testing cli for javascript. Its colors, indents and order is very user friendly. 



[jest]: (https://jestjs.io/)
watson-streaming 0.0.13
Speech to text transcription in real-time using IBM Watson
Keywords: ibm watson stt asr

watson-streaming
################

Real-time speech-to-text transcription using IBM Watson.

Links
-----

* Documentation: https://watson-streaming.readthedocs.io/en/latest
* Source code: https://github.com/Nagasaki45/watson-streaming
* PyPI releases: https://pypi.org/project/watson-streaming
* License: `MIT`_
* Test status: https://travis-ci.org/Nagasaki45/watson-streaming

.. _MIT: https://github.com/Nagasaki45/watson-streaming/blob/master/LICENSE
hdhrhttpapi 0.0.1
HDHomeRun HTTP API interface library for Python 3.
Requires Python: >=3.8
Keywords: tv television tuner tvtuner hdhomerun

HDHRHTTPAPI
-----------

This is a small library to retrieve information from HD HomeRun devices through the HTTP API and Python 3.

Background
----------

The [HDHomeRun products](https://www.silicondust.com/hdhomerun/) from SiliconDust are basically network TV tuners that broadcast the TV from 
your cable or antenna to your network. These devices has one or more built-in TV tunners so it allows to
stream the TV channels to the [SiliconDust](http://www.silicondust.com) and third-party applications.

There are several libraries for different lenguages to communicate to HDHomeRun devices but I develop this
library to use with homeassistant, a home automation software, to allow it to know the state of the tunners.

As homeassistant is developed in Python and has some restrictions to access directly to the I/O hardware, so 
this library has only some functions needed for my custom component for home assistant.

The goals of this project were as follows:

- Be able to discover the device(s) on the local network.
- Be able to poll for status.
- For a tuner to be able to get the current channel.

All goals have been completed.


Requirements
------------

- There is not other additional requirements than Python 3.8. Other versions not tested.

Development Resources
---------------------

- [Nat Burns](https://github.com/burnnat/ha-hdhomerun) custom component for home assistant
- [SiliconDust HD HomeRun development guide](https://www.silicondust.com/hdhomerun/hdhomerun_development.pdf)
- [HDHomeRun HTTP Development Guide](https://info.hdhomerun.com/info/http_api)
SmartHab 0.21
This package allows controlling devices in a SmartHab-powered home.

# python-smarthab
This Python library allows you to programmatically control your SmartHab-powered
home.

[![PyPI version](https://badge.fury.io/py/SmartHab.svg)](https://badge.fury.io/py/SmartHab)

## What is SmartHab?
[SmartHab](https://www.smarthab.fr/) is a company that installs their home 
automation solution in new buildings, and offers a mobile application for 
home owners or renters to control their home over the Internet.

Mobile App → API → SmartHab box (1 at each floor) → Z-Wave devices

## What does this library do?
`python-smarthab` connects to the SmartHab API and allows you to automate your
home without having to use the mobile application.

It might prove particularly useful if integrated into a home automation box or
software. Feel free to use it, it's under the GPL!

## How do I use it?
```bash
pip3 install SmartHab
```

```python
import pysmarthab

hub = pysmarthab.SmartHab()

# Login
await hub.async_login('smarthab.user@example.com', '1234567')

if not hub.is_logged_in:
    # Bad credentials :(
    raise SystemExit

# Get the list of available devices
devices = await hub.async_get_device_list()

# Close all roller shutters and turn on all lights
for device in devices:
    if isinstance(device, pysmarthab.Light):
        device.async_turn_on()

    if isinstance(device, pysmarthab.Shutter):
        device.async_close()
```

If you prefer to use it synchronously:

```python
import pysmarthab

hub = pysmarthab.SmartHab()

# Login
hub.login('smarthab.user@example.com', '1234567')

if not hub.is_logged_in:
    # Bad credentials :(
    raise SystemExit

# Get the list of available devices
devices = hub.get_device_list()

# Close all roller shutters and turn on all lights
for device in devices:
    if isinstance(device, pysmarthab.Light):
        device.turn_on()

    if isinstance(device, pysmarthab.Shutter):
        device.close()
```