semvec/pypi-packages huggingface dataset:
kyotsu 0.0.1
Kyōtsū 共通 is a package with common logic and use cases used as foundation for microservices.
License: MIT
Requires Python: >=3.10
Keywords: development utilities common generic

[![Github Test](https://github.com/four-velocity/kyotsu/workflows/Test/badge.svg?event=push&branch=master&style=flat-square)]()
[![Coverage](https://coverage-badge.samuelcolvin.workers.dev/four-velocity/kyotsu.svg?style=flat-square)]()
[![PyPI - Version](https://img.shields.io/pypi/v/kyotsu)](https://pypi.org/project/kyotsu/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/kyotsu)](https://pypi.org/project/kyotsu/)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
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# Kyōtsū 共通
A package with common logic and use cases used as foundation for microservices.
shrinkit 0.1.11
The package that helps to compress everything for ML/AI projects.
License: MIT
Requires Python: >=3.10.0


# Shrinkit: A GUI-Based AutoML Python Library

Shrinkit is a powerful GUI-based Python library designed for automating machine learning tasks. With its intuitive interface, Shrinkit simplifies the process of building, training, and evaluating machine learning models, making it accessible to users of all skill levels. Shrinkit is a <b>No-code</b> package which can be used through a graphical interface to upload, process and train datasets on the go!

## Features
- **User-Friendly Interface**: Shrinkit provides a user-friendly graphical interface that enables users to interactively perform various machine learning tasks without writing a single line of code.
- **Automated Model Selection**: With Shrinkit, users can automatically select the best machine learning model for their dataset using advanced algorithms and techniques.
- **Hyperparameter Optimization**: Shrinkit optimizes model hyperparameters to enhance model performance and accuracy, ensuring optimal results.
- **Visualization Tools**: Shrinkit offers a range of visualization tools to analyze and interpret model performance, feature importance, and more.
- **Export and Deployment**: Once a model is trained, Shrinkit allows users to export the model for deployment in production environments seamlessly.

## Installation
To install Shrinkit, simply use pip:

```bash
pip install shrinkit
```

## Sample Usage
![Sample Training Usage](/graphics/sample_training.png)

## Links
Streamlit App: [https://shrinkit.streamlit.app/](https://shrinkit.streamlit.app/)</br>
PyPI Package: [https://pypi.org/project/shrinkit/](https://pypi.org/project/shrinkit/)</br>
Source Code: [https://github.com/blurred-machine/shrinkit](https://github.com/blurred-machine/shrinkit)</br>

## Contributing
Contributions to Shrinkit are welcome! Feel free to submit bug reports, feature requests, or pull requests on the GitHub repository.

## License
This project is licensed under the Apache License - see the [LICENSE](/LICENSE) file for details.
Gemini-cli-by-Yoru-cyber 1.1.0
A small Python package to use Gemini on your terminal.
License: MIT
Requires Python: >=3.6

<p align="center">Gemini-cli</p>

<p align="center">
<img src="https://img.shields.io/badge/python-3.11+-blue.svg">
 </p>

<p align="center">This is a command line tool to use the Gemini API provided by Google cloud.</p>

## Usage

### Install dependencies

#### Clone the respository

```sh
git clone https://github.com/Yoru-cyber/Gemini-cli
```

#### Install dependencies

```sh
pip install -r requirements.txt
```

#### Setup your API key

First you need to have a [Google Cloud API key](https://ai.google.dev/tutorials/python_quickstart#setup_your_api_key) to use Gemini.
Once you have you API key, just create a .env file inside the Gemini-cli folder

#### Create .env
```sh
touch .env
```
#### Adding the key
Now you need to add your key as a variable

```sh
GOOGLE_API_KEY = YOUR KEY GOES HERE
```
You can rename it if you want but then you need to change where it says ***GOOGLE_API_KEY*** on **main.py**

#### Running
Now just execute on the terminal
```sh
python3 main.py
```
That's all 🤠

## Feedback

<p align="center">
<img src="https://media.tenor.com/PrNnVlIkeckAAAAM/chopper-crying.gif">
</p>

<p align="center">All types of positive feedback are welcomed.</p>
<p align="center">This is my first published project hoping that is useful to someone.</p>
<p align="center">Be free to improve or modify.</p>
downloads 1.1.0
Easy file downloads
License: MIT
Requires Python: >=3.12,<4.0

# Downloads

[![test](https://github.com/audy/downloads/workflows/test/badge.svg)](https://github.com/audy/downloads/actions?query=workflow%3Atest)
[![PyPI version](https://badge.fury.io/py/downloads.svg)](https://pypi.org/project/downloads/)
[![Downloads](https://pepy.tech/badge/downloads/month)](https://pepy.tech/project/downloads)

Easier HTTP downloads in Python 3.7+

## Features

1. Easier to remember than `urllib`
2. Files are not written unless download finishes
3. Progress bar!

## Installation

```
pip install downloads==1.1.0
```

## Usage

```python
from downloads import download

download("http://i.imgur.com/ij2h06p.png")

# output path is automatically determined and returned
# but you can specify it manually if that"s your thing:

download("http://i.imgur.com/i5pJRxX.jpg", out_path="cheezburger.jpg")

# or, if you want to be fancy:

download("https://www.gutenberg.org/files/2600/2600-0.txt", progress=True)
```

That's it!