semvec/pypi-packages huggingface dataset:
spotidex 1.0.0
Spotify Downloader TUI
License: MIT
Requires Python: <4.0,>=3.8
Keywords: spotify, downloader, spotidex, music

# Spotidex ‎ [![PyPI - Version](https://img.shields.io/pypi/v/spotidex.svg)](https://pypi.org/project/spotidex) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/spotidex.svg)](https://pypi.org/project/spotidex)


Spotidex is a cutting-edge powerful TUI app build using textual which is designed to enhance your Spotify experience. 

With an intuitive and user-friendly interface, Spotidex allows you to effortlessly download your favorite Spotify songs by simply pasting the Spotify link. Liberate your music and take it with you wherever you go, even when offline.

Spotidex supports a wide range of Spotify links, enabling 
users to download playlists, individual tracks, and entire albums effortlessly.

https://github.com/libin-codes/spotidex/assets/129957992/dc1e20e2-3cb8-4385-bde4-e81bdc5c35b7

## Installation

Install Spotidex via pipx:

```console
pipx install spotidex
```

if you dont have pipx installed then install via: 

```console
pip install pipx
```

## Usage

After you have installed spotidex , you can run it by typing spotidex in your terminal 

![](https://github.com/libin-codes/spotidex/assets/129957992/40102e8a-6369-455d-95c6-4d7a53ffbcd4)


## Compatibility

Spotidex runs on Linux, macOS, and Windows. Spotidex requires Python 3.8 or above.

## Disclaimer !!!

Spotidex uses YouTube as a source for music downloads. This method is used to avoid any issues related to downloading music from Spotify.

Users are responsible for their actions and potential legal consequences. We do not support unauthorized downloading of copyrighted material and take no responsibility for user actions.

## License

`spotidex` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
svc-toolkit 1.1.0
A self-contained singing voice conversion application using the so-vits-svc architecture, with Deep U-Net model for vocal separation feature and easy to use GUI.
License: MIT
Requires Python: <3.12,>=3.10

# Singing Voice Conversion Toolkit

![Tests](https://github.com/jljl1337/svc-toolkit/actions/workflows/tests.yml/badge.svg)
![Deployment](https://github.com/jljl1337/svc-toolkit/actions/workflows/deployments.yml/badge.svg)
[![Codecov](https://codecov.io/gh/jljl1337/svc-toolkit/graph/badge.svg?token=QBM6OLIG00)](https://codecov.io/gh/jljl1337/svc-toolkit)

A self-contained singing voice conversion application using the so-vits-svc architecture, 
with Deep U-Net model for vocal separation feature and easy to use GUI.

## Getting Started

### Installation

1. Install [Python](https://www.python.org/downloads/) (3.10 is recommended, but 3.10 - 3.11 should work)

2. Install [pipx](https://pipx.pypa.io/stable/installation/)

3. Install the package by running this following terminal command if you only have one Python version installed:

```
pipx install svc-toolkit
```

To install with a specific Python version, use the `--python` flag. For example, to install with Python 3.10:

```
pipx install svc-toolkit --python 3.10
```

<details markdown>
<summary>Using NVIDIA GPU</summary>

To use the package with NVIDIA GPU, you need to upgrade the following dependencies:

```
pipx inject svc-toolkit torch==2.1.1 torchaudio==2.1.1 --pip-args="-U" --index-url https://download.pytorch.org/whl/cu121
```

For CUDA version 11.*, you can change the `cu121` to `cu118`. So the command will be:

```
pipx inject svc-toolkit torch==2.1.1 torchaudio==2.1.1 --pip-args="-U" --index-url https://download.pytorch.org/whl/cu118
```

</details>

Note that AMD GPUs are not actively supported, but you can try using the package with the CPU version of PyTorch.

For other installation options, see [Installation](https://jljl1337.github.io/svc-toolkit/installation/).

### Usage

#### Windows

```
svct.exe
```

#### macOS/Linux

```
svct
```

For the detailed usage guide, see [Usage](https://jljl1337.github.io/svc-toolkit/usage/).

## Development

For the detailed development guide, see [Development](https://jljl1337.github.io/svc-toolkit/development/).

## About

This project is the implementation of the final year project for the Bachelor of
Science in Computer Science, Department of Computer Science, City University of Hong
Kong, named "Singing Voice Conversion from Fully Mixed Track with GUI", with project code
`23CS062`.
conscience 0.1.0
Perform automated testing of TKinter GUI applications
License: MIT
Requires Python: <4.0,>=3.12

# conscience

__A library to perform automated testing of Tkinter GUI applications__

_Forced to observe, without agency, the consequences of their actions_

## 

Conscience is a tool that allows automated acceptance testing
of Tkinter GUI applications.

The tool is designed for automated assessment of introductory
programming assignments.

As such, there is included support to recognize GUI widgets
based upon a description of their functionality.
Mojo2py 1.3
A python package which converts a mojo file (.mojo or .🔥) into a python file.
License: MIT
Keywords: conversion

  <div class="examples" id="examples" display="flex" flex-direction="row" align="center">
<h2 align="center"> Mojo2py  <img src="https://img.shields.io/badge/Mojo2py-v1.3-orange"></img></h2>
   
  </div>
<br>
  <div class="examples" id="examples" display="flex"  align="center">
<img align="center" src="https://i.imgur.com/4mkqrIq.png" height="70" width="70"></img>

  </div>
<br>
  <div class="examples" id="examples" display="flex"  align="center">
<p>A python package which converts a mojo file (.mojo or .🔥) into a python file.</p>
  </div>

### Installation  using pip
```shell
pip install mojo2py
pip3 install mojo2py
```
### Or clone the repo
```shell
git clone git@github.com:venvis/mojo2py.git
```


#### Initialize a mojo file , for example (example.mojo or example.🔥) :
```mojo

from python import Python 

def matplotlib(x:PythonObject,y:PythonObject):
    var plt=Python.import_module("matplotlib.pyplot")
    var np=Python.import_module("numpy")
    var xval=np.array(x)
    var yval=np.array(y)
    plt.plot(xval,yval)
    plt.show()

fn show() raises:
    try:
        matplotlib([1,2,3,4,5],[6,7,8,9,10])
    except:
        print("Error")

fn main():
    try:
        show()  
    except:
        pass   
```        

#### Create a python file , for example (trial.py) : 

```python
from mojo2py import convert #import the class to convert
file=convert("example.mojo") # or file=convert("example.🔥")
file.final() # Call the final method to generate example.py file from example.mojo
```
#### A file called example.py , with the same name as the mojo file will be created in the same directory and the code is as follows :

```python
import matplotlib.pyplot as plt 
import numpy as np 
 

def matplotlib(x,y):
    xval=np.array(x)
    yval=np.array(y)
    plt.plot(xval,yval)
    plt.show()

def show():
    try:
        matplotlib([1,2,3,4,5],[6,7,8,9,10])
    except:
        print("Error")

def main():
    try:
        show()  
    except:
        pass                  
if __name__=="__main__":
    main()
```