semvec/pypi-packages huggingface dataset:
libder 0.0.6
Der
Requires Python: >=3.7

# libder

libder is a Python library for honouring our dearest brother Der.
## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install foobar.

```bash
pip install libder
```

## Usage

```python
import libder

# print Der
libder.printDer()

# currency converter
libder.convert2Der(amount=200)
```

## Contributing

Pull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License

[MIT](https://choosealicense.com/licenses/mit/)
nicegui-extentions 0.0.3
nicegui extensions
Requires Python: >=3.8

# nicegui_extentions

provide extension features for Nicegui

<a title="MIT" target="_blank" href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/license-MIT-orange.svg?style=flat-square"></a>

<p align="center">
<a href="https://github.com/T2XX/nicegui_extentions/blob/main/README.md">English</a>  |  <a href="https://github.com/T2XX/nicegui_extentions/blob/main/README_zh_CN">简体中文</a>
</p>

# extentions

### [markdown](https://github.com/T2XX/nicegui_extentions/blob/main/src/nicegui_extentions/markdown.py)

- powered by [vditor](https://github.com/Vanessa219/vditor/blob/master/README_en_US.md)![vditor](https://b3log.org/images/brand/vditor-128.png), you can see the feature from [here](https://github.com/Vanessa219/vditor/blob/master/README_en_US.md#--features)

#### how to use it

1. step 1, load Dependence before page require

```python
from nicegui_extentions.markdown import MarkdownPreViewer, initMarkdownPreViewerDependence
initMarkdownPreViewerDependence()
```

2. step 2, create widget and render markdown

```python
md = MarkdownPreViewer().render(markdown)
```

3. (optional)step 3, update markdown

```python
md.render(new_markdown)
```

### [reconnect_box](https://github.com/T2XX/nicegui_extentions/blob/main/src/nicegui_extentions/reconnect_box.py)

- before

  ![before](https://github.com/T2XX/nicegui_extentions/blob/main/image/1723880105580.png?raw=true)
- after

  ![after](https://github.com/T2XX/nicegui_extentions/blob/main/image/1723879996849.png?raw=true)

#### how to use it

1. step 1

```python
from nicegui_extentions.reconnect_box import set_reconnect_box
```

1. step 2

```python
# just can use after page load
set_reconnect_box()
# after page load to use it
app.on_connect(lambda: set_reconnect_box())
```

### [reorderable](https://github.com/T2XX/nicegui_extentions/blob/main/src/nicegui_extentions/draggable.py)

let an item reorderable in row or column

#### how to use it

1. step 1

```python
nicegui_extentions.reorderable import ReorderableItem,ReorderableColumn
```

1. step 2

```python
with ReorderableColumn() as col:
    with ReorderableItem(width_class="w-20") as draggable:
        ui.label("Draggable Item 1")

    with ReorderableItem(width_class="w-20") as draggable:
        ui.label("Draggable Item 2")

    with ReorderableItem(width_class="w-20") as draggable:
        ui.label("Draggable Item 3")
```
linkedin-job-scraper 0.0.2
Web scraper of jobs from LinkedIn

## Overview
A Python package with a web scraper of jobs from LinkedIn.

Can be used for:
- automated jobs search 🔍
- creating custom alerts 🚨
- collecting data for analytical reports 📈

Possible extention: extract keywords from job descriptions with LLMs.

### Classes
**JobSearch** - to get a list of jobs from LinkedIn job search page by defined parameters.

Parameters:
- keywords: keywords to search for.
- location: location to search in.
- distance (optional): distance in miles to search for.
- work_modes (optional): work modes codes to filter by: 1 - On-site, 2 - Remote, 3 - Hybrid, None - all.
- company_codes (optional): companies codes to filter by.
- industries (optional): industries codes to filter by.
- experience_levels (optional): experience levels to filter by: 1 - Internship, 2 - Entry level, 3 - Associate, 4 - Mid-Senior level, 5 - Director, 6 - Executive.
- easy_apply (optional): filter only Easy Apply jobs.
- posted_ago_max (optional): max number of second a job was posted ago, default 1 day.
 
**Job** - to parse the job page by a given url.

## How to use
```
from selenium import webdriver

driver = webdriver.Chrome()

params = {
    'keywords': 'Data Engineer',
    'location': 'Germany'
}
search = JobSearch(driver, **params)

jobs = search.get_jobs() # returns the list of dicts with parsed info (position, company name, job page link, posted ago and etc.)
job = Job(driver, jobs[0]['job_link'])
description = job.get_job_description()
```

An extended example can be found in *example.ipynb* notebook.
A2KA 0.1.7
Attention to Key Area, a plug and play interpretable network.
Requires Python: >=3.7

# A2KA
A2KA is a novel web architecture designed to
identify crucial areas by extracting biological information from the embedding space of large language models.
Make sure pytorch is installed firstly.
The github storage is: https://github.com/Dsadd4/NLSExplorer_1.0
## Installation

You can install A2KA via pip:

```bash
pip install A2KA
```
## Usage
A2KA
```python
from A2KA import A2KA
import torch
hidden_dimention = 512
#configure your A2KA sturcture
config = [8,8,32]
#If your datasize is significant large, extending the scale of the network may be a good choice.
#Such a config = 18*[64] means it has 18 layers and each layer has 64 basic attention units.
model =A2KA( hidden_dimention,config)
# tensor in a shape of (Batchsize,sequence_length, embedding dimension)
exampletensor = torch.randn(5,100,512)
prediction,layerattention = model(exampletensor)
print(prediction)
print(layerattention)
```

SCNLS (in linux system)
```python
from A2KA import SCNLS
#Example 
sequence_for_analysis = ['MSSAKRRKK','LSSSSKVR','MTNLP']
kth_set = 3
max_gap = 3
processorsnumber = 2
result = SCNLS(sequence_for_analysis,kth_set,max_gap,processorsnumber)
print(result)
```