semvec/pypi-packages huggingface dataset:
ingreedypy 1.3.8
ingreedy-py parses recipe ingredient lines into a object
Keywords: ingreedy, ingreedypy, recipe, parser

# ingreedy-py
python port of https://github.com/iancanderson/ingreedy-js

## Installation
```bash
$ pip install ingreedypy
```

## Local Testing
```bash
$ pip install -e .[tests]
$ py.test --cov=ingreedypy ingreedytest.py
```
image-processing-hallynny 0.0.1
Image Processing Package using Skimage
Requires Python: >=3.8

# package_name

Description. 
The package package_name is used to:
	Processing:
		- Histrogram matching
		- Structural similarity
		- Resize imagem
	Utils:
		- Read image
		- Save image
		- Plot image
		- Plot result
		- Plot histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install image_processing_hallynny
```

## Author
Hallynnyh

## License
[MIT](https://choosealicense.com/licenses/mit/)
GooBa 0.1.6
Gooba is a python frontend web framework inspired by Reactjs.
Requires Python: >=3.6



# Gooba  
   
**Gooba** is a frontend framework written in Python, inspired by **React**, aimed at ***beginners*** who want to build web UIs with Python.    
    
Precautions:    
 - **DO NOT USE THIS ON PRODUCTION**: Gooba is a work in progress and not yet considered safe for production environments.   
 - **Fun Project**:  
   This project is developed for fun and learning purposes.     
  
**Features**:  
  
 - **Component-Based**: Gooba allows you to build reusable components similar to how you would in React.    
 -  **Pythonic Syntax**: Write your  
   UI components and logic entirely in Python, making it accessible for  
   Python developers.     
 - **Lightweight**: Designed to be simple and easy    to understand, perfect for learning and experimentation.  

    
**License**:    
    
 - Gooba is distributed under the **MIT** License. It is open-source and  
   free to use, including for commercial purposes, but remember that it  
   is not safe for production environments at this time.
experiment-goodies 0.2.7
Useful helpers, callbacks and more for experimenting with ml models
Requires Python: <4.0,>=3.10

# ML Experiment Goodies

[![Python 3.10](https://img.shields.io/badge/python-3.10-blue.svg)](https://www.python.org/downloads/release/python-3100/)

Helpers, handlers, callbacks and more that are nice to have when experimenting with ml models

## Setup

The most basic functionalities of this library is included in the base installation, which you have by running any of the commands below

```bash
pip install experiment-goodies
poetry add experiment-goodies
```

Geospatial utilities require some big packages, like rasterio and rio-cogeo. They are included inside the `geo` extra which you can install with one of the commands shown below.

```bash
pip install experiment-goodies[geo]
poetry add experiment-goodies -E geo
```

## Development

To develop in this repo, you will need to install some extra dependencies and make some additional configurations. Start by installing dev dependencies:

```bash
poetry install --group dev
```

Some tests depend on data managed by dvc, so you should pull files associated to this repo. Remote storage is configured to S3, so you should ensure you have the correct AWS credentials loaded in your system.

```bash
dvc pull
```
b4msa 2.2.4
Baselines for Multilingual Sentiment Analysis

A Baseline for Multilingual Sentiment Analysis (b4msa)
======================================================

.. image:: https://github.com/INGEOTEC/b4msa/actions/workflows/test.yaml/badge.svg
	   :target: https://github.com/INGEOTEC/b4msa/actions/workflows/test.yaml

.. image:: https://coveralls.io/repos/github/INGEOTEC/b4msa/badge.svg?branch=develop
	:target: https://coveralls.io/github/INGEOTEC/b4msa?branch=develop

.. image:: https://badge.fury.io/py/b4msa.svg
	:target: https://badge.fury.io/py/b4msa

.. image:: https://dev.azure.com/conda-forge/feedstock-builds/_apis/build/status/b4msa-feedstock?branchName=main
	:target: https://dev.azure.com/conda-forge/feedstock-builds/_build/latest?definitionId=16447&branchName=main

.. image:: https://img.shields.io/conda/vn/conda-forge/b4msa.svg
	:target: https://anaconda.org/conda-forge/b4msa

.. image:: https://img.shields.io/conda/pn/conda-forge/b4msa.svg
	:target: https://anaconda.org/conda-forge/b4msa

.. image:: https://readthedocs.org/projects/b4msa/badge/?version=latest		 
        :target: https://b4msa.readthedocs.io/en/latest/?badge=latest

b4msa is multilingual framework, that can be served as a baseline for sentiment analysis
classifiers, as well as a starting point to build new sentiment analysis
systems.

b4msa extends our work on creating a text classifier (see `microTC
<http://github.com/ingeotec/microtc>`_) by incorporating different
language dependent techniques such as:

* Stemming
* Stopword
* Negations

The documentation is on `readthedocs <https://b4msa.readthedocs.io>`_.
snac 1.2.1
Multi-Scale Neural Audio Codec

# SNAC 🍿

Multi-**S**cale **N**eural **A**udio **C**odec (SNAC) compresses audio into discrete codes at a low bitrate.

| 🎸 Music samples                                                                                         | 🗣️ Speech samples                                                                                       |
|----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|
| <video src='https://github.com/hubertsiuzdak/snac/assets/35269911/e8adac68-d3f1-4fc1-8cf9-f48d9bcd95ed'> | <video src='https://github.com/hubertsiuzdak/snac/assets/35269911/65ac2547-c711-49d4-8a5d-64d52e6d6ba1'> |

🎧 More audio samples available at https://hubertsiuzdak.github.io/snac/

## Overview

SNAC encodes audio into hierarchical tokens similarly to SoundStream, EnCodec, and DAC (see the image
on the left). However, SNAC introduces a simple change where coarse tokens are sampled less frequently,
covering a broader time span (see the image on the right).

This can not only save on bitrate, but more importantly this might be very useful for language modeling approaches to
audio generation. E.g. with coarse tokens of ~10 Hz and a context window of 2048 you can effectively model a
consistent structure of an audio track for ~3 minutes.

![snac.png](img%2Fsnac.png)

## Pretrained models

Currently, all models support only single audio channel (mono).

| Model                                                                       | Bitrate   | Sample Rate | Params | Recommended use case     | 
|-----------------------------------------------------------------------------|-----------|-------------|--------|--------------------------|
| [hubertsiuzdak/snac_24khz](https://huggingface.co/hubertsiuzdak/snac_24khz) | 0.98 kbps | 24 kHz      | 19.8 M | 🗣️ Speech               | 
| [hubertsiuzdak/snac_32khz](https://huggingface.co/hubertsiuzdak/snac_32khz) | 1.9 kbps  | 32 kHz      | 54.5 M | 🎸 Music / Sound Effects | 
| [hubertsiuzdak/snac_44khz](https://huggingface.co/hubertsiuzdak/snac_44khz) | 2.6 kbps  | 44 kHz      | 54.5 M | 🎸 Music / Sound Effects |

## Usage

Install it using:

```bash
pip install snac
```

To encode (and decode) audio with SNAC in Python, use the following code:

```python
import torch
from snac import SNAC

model = SNAC.from_pretrained("hubertsiuzdak/snac_32khz").eval().cuda()
audio = torch.randn(1, 1, 32000).cuda()  # placeholder for actual audio with shape (B, 1, T)

with torch.inference_mode():
    codes = model.encode(audio)
    audio_hat = model.decode(codes)
```

You can also encode and reconstruct in a single call:

```python
with torch.inference_mode():
    audio_hat, codes = model(audio)
```

⚠️ Note that `codes` is a list of token sequences of variable lengths, each corresponding to a different temporal
resolution.

```
>>> [code.shape[1] for code in codes]
[12, 24, 48, 96]
```

## Acknowledgements

Module definitions are adapted from the [Descript Audio Codec](https://github.com/descriptinc/descript-audio-codec).