semvec/pypi-packages huggingface dataset:
discord-file-compressor 0.1.5
A tool to compress files for Discord's 10MB limit
Requires Python: >=3.7

# Discord File Compressor

A Python tool to compress files to meet Discord's 10MB upload limit while maintaining quality.

## Features

- Compresses videos and images while maintaining quality
- Supports multiple file formats
- Easy-to-use command line interface
- Automatically detects file type and applies appropriate compression
- Copies compressed file path to clipboard
- Shows desktop notifications

## Installation

1. Install Python 3.7 or higher from [python.org](https://python.org)

2. Install ffmpeg:
   - On macOS: `brew install ffmpeg`
   - On Windows: Download from [ffmpeg.org](https://ffmpeg.org/download.html)
   - On Ubuntu/Debian: `sudo apt-get install ffmpeg`

3. Install the compressor:

On Linux/macOS:
pynicehash 0.0.13
Python to interac with Nicehash API v2
Requires Python: >=3.6

# pynicehash
This is a library to interact with the nicehash API v2 in python.

# feature
For now pynicehash support only rig management, get and set mining rig state.

# usage

```
nh = pynicehash.NiceHash(api_url, organisation_id, api_key, api_secret)

rigs = nh.get_rigs()

for rig in rigs:
  print (rig.name)
  for device in rig.devices:
    print (device.name)
```
# configuration
When creating the NiceHash object you have to pass your connection credential.
**api_url** you have two choice:
* https://api-test.nicehash.com for testing, in testing you probably will not have any mining rig.
* https://api2.nicehash.com for production.

**organisation_id**
The organisation_id is the the id you can find in you page where you create you api key.

**api_key**
You have to select miningg rig permission when creating the API key
The key you generate in you account profile

**api_secret**
The secret you generate in you account profile

## screenshot
Here is a screen shot of the location for the organisation id and where to create the API key.
![api_creation](https://github.com/nslythe/pynicehash/raw/main/assets/api_creation.png)
curlify3 0.5
yet another library to convert request object to curl command
Requires Python: >=3.8

# yet another library to convert request object to curl command

[![PyPI](https://img.shields.io/pypi/v/curlify3.svg)](https://pypi.python.org/pypi/curlify3)
[![PyPI](https://img.shields.io/pypi/dm/curlify3.svg)](https://pypi.python.org/pypi/curlify3)

### Support request's objects from:

- requests
- httpx
- aiohttp server
- starlette/fastapi

## Installation

```sh
pip install curlify3
```

## Example

```py
from curlify3 import to_curl
import requests

response = requests.get("http://google.ru")
print(to_curl(response.request))
# curl -H 'user-agent: python-requests/2.32.3' -H 'accept-encoding: gzip, deflate' -H 'accept: */*' -H 'connection: keep-alive' http://www.google.ru/
```
blinker 1.9.0
Fast, simple object-to-object and broadcast signaling
Requires Python: >=3.9

# Blinker

Blinker provides a fast dispatching system that allows any number of
interested parties to subscribe to events, or "signals".


## Pallets Community Ecosystem

> [!IMPORTANT]\
> This project is part of the Pallets Community Ecosystem. Pallets is the open
> source organization that maintains Flask; Pallets-Eco enables community
> maintenance of related projects. If you are interested in helping maintain
> this project, please reach out on [the Pallets Discord server][discord].
>
> [discord]: https://discord.gg/pallets


## Example

Signal receivers can subscribe to specific senders or receive signals
sent by any sender.

```pycon
>>> from blinker import signal
>>> started = signal('round-started')
>>> def each(round):
...     print(f"Round {round}")
...
>>> started.connect(each)

>>> def round_two(round):
...     print("This is round two.")
...
>>> started.connect(round_two, sender=2)

>>> for round in range(1, 4):
...     started.send(round)
...
Round 1!
Round 2!
This is round two.
Round 3!
```
machinelite 0.0.2
Sci-Lite is Light version of Supervised Machine Learning Model
Requires Python: >=3.6

# **MachineLite**


**MachineLite** provides a streamlined solution for evaluating various supervised machine learning algorithms, simplifying the data scientist's workflow by automating model integration and evaluation. With just a few lines of code, SciLite enables quick access to essential machine learning evaluations, saving valuable time and effort.

---

### Installation

To install the package, run:

```bash
pip install machinelite
```

---

### Usage

Basic example of how to use the package in Python:

```bash
from machinelite.univariate.regression import Regression as reg
result = reg(X_train, X_test, y_train, y_test)
print(result)
```

---

### Features

- **One-Click Model Integration:** Supports easy integration and evaluation of multiple supervised machine learning models, whether for regression or classification.
- **Efficient Model Evaluation:** Simplifies machine learning model evaluations with standardized output, aiding in faster analysis and comparisons.

---

### Contributing

Contributions to MachineLite are warmly welcomed! If you'd like to contribute, please fork the repository and submit a pull request or open an issue to discuss improvements.

---

### Documentation

**MachineLite:** [View Here](https://pypi.org/project/machinelite/0.0.1/)

---

### License

[![MIT License](https://img.shields.io/badge/License-MIT-green.svg)](https://github.com/AyushJainSparsh/MachineLite?tab=MIT-1-ov-file)

---

### Contact
For any questions or issues, feel free to reach out to:

* Author: Ayush Jain Sparsh
* Email: ayushjainsparsh2004.ajs@gmail.com

---