semvec/pypi-packages huggingface dataset:
kdocpyxl 0.0.2
Kingsoft cloud document excel Python read and write library
Requires Python: >=3.6

# kdocpyxl

kdocpyxl 是一个用于读写金山文档云 Excel 的 Python 库。

## 功能

- 从金山云表格中读取或写入数据

## 安装

使用 pip 安装 kdocpyxl：

```bash
pip install kdocpyxl
```

## 使用方法
[AirScript 脚本令牌](https://airsheet.wps.cn/docs/apitoken/intro.html)

### 1. 创建AirScript脚本
- 登录金山云表格。
- 点击界面顶部的【效率】标签。
- 在下拉菜单中选择【高级开发】。
- 点击【AirScript脚本编辑器】开始创建一个新的AirScript脚本。

### 2. 编写AirScript代码
- 在脚本编辑器中，将本项目源码中的 `airscript.js` 代码复制并粘贴到编辑器中。
- 确保代码无误后，点击保存。

### 3. 获取Webhook URL和API Token
- 在脚本编辑器中，找到并复制生成的 `webhook_url`，这是外部应用触发脚本的URL。
- 为脚本创建一个 `api_token`，这是一个安全令牌，用于验证外部请求的合法性。

### 4. 配置脚本参数
- 在你的代码中，设置 `webhook_url` 和 `api_token` 这两个参数，以便脚本能够正确地与金山云表格交互。

## 贡献
欢迎贡献代码，你可以通过提交 Pull Request 或者通过 Issue 讨论新功能或者报告 bug。

## 许可证
本项目采用 MIT 许可证。
pudly 2.0.0
Add your description here
Requires Python: >=3.12

# P.U.D.L.y

Python Url Downloader Library 🤷

[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/8-bit-hunters/puddle/main.svg)](https://results.pre-commit.ci/latest/github/8-bit-hunters/puddle/main)
[![pytest](https://github.com/8-bit-hunters/pudly/actions/workflows/testing.yaml/badge.svg)](https://github.com/8-bit-hunters/pudly/actions/workflows/testing.yaml)
[![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)
[![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)
[![gitmoji](https://img.shields.io/badge/gitmoji-%20😜%20😍-FFDD67.svg?style=flat-square)](https://gitmoji.dev/)

The goal of this library to create easy to use functions to download files in Python.

- [Pypi](https://pypi.org/project/pudly/)
- [Github](https://github.com/8-bit-hunters/pudly)
- [Documentation](https://8-bit-hunters.github.io/pudly/)

## Examples

### Downloading one file

The `download` function can be used to download a file from an url. The function returns the downloaded file's path.

```python
from pudly.pudly import download

url = "https://databank.worldbank.org/data/download/WDI_CSV.zip"
file = download(url)

assert file.exists()
```

It takes optional arguments to specify the download directory or any query parameters for the request.

```python
from pudly.pudly import download
from pathlib import Path

url = "https://api.worldbank.org/v2/en/indicator/NY.GDP.MKTP.CD"
query_parameters = {"downloadformat": "csv"}
download_directory = Path("data")

file = download(url, download_dir=download_directory, query_parameters=query_parameters)

assert file.exists()
```

### Downloading multiple files

The `download_files_concurrently` function uses threading to download files in parallel. It returns the list of the
downloaded file's path.

```python
from pathlib import Path
from pudly.pudly import download_files_concurrently

urls = [
    "https://api.worldbank.org/v2/en/indicator/SP.POP.TOTL?downloadformat=csv",
    "https://api.worldbank.org/v2/en/indicator/NY.GDP.MKTP.CD?downloadformat=csv",
    "https://api.worldbank.org/v2/en/indicator/EN.POP.DNST?downloadformat=csv",
]
download_dictionary = Path("data")

files = download_files_concurrently(urls, download_dir=download_dictionary)

for file in files:
    assert file.exists()
```
win11-notificationbar 0.0.2
win11_notificationbar
Requires Python: >=3.7

# Windows 11 通知栏通知系统

## 简介
本项目旨在实现一个基于Windows 11的通知栏通知系统。通过使用`winsdk`库，我们可以创建丰富的通知消息，并支持多种交互方式，如按钮点击、输入框、进度条等。

## 目录
1. [简介](#简介)
2. [安装依赖](#安装依赖)
3. [核心功能](#核心功能)
    - [创建通知](#创建通知)
    - [添加文本](#添加文本)
    - [添加图标](#添加图标)
    - [添加图片](#添加图片)
    - [添加进度条](#添加进度条)
    - [添加音频](#添加音频)
    - [添加按钮](#添加按钮)
    - [添加输入框](#添加输入框)
    - [添加选择框](#添加选择框)
4. [异步通知](#异步通知)
5. [更新进度](#更新进度)
6. [清除通知](#清除通知)
7. [总结](#总结)
8. [附录](#附录)

## 安装依赖
在开始之前，确保已经安装了所需的依赖库。可以通过以下命令安装：
```bash
pip install winsdk
pip install pywin32
```
## 核心功能

### 创建通知
使用`notify`函数创建一个基本的通知：
```python
notify(title="Hello", body="This is a notification", inputs=["Enter your name"])
```
### 添加图标
设置通知的图标：
```python
notify(title="Hello", body="This is a notification", icon="path/to/icon.png")
```
### 添加图片
在通知中添加图片：
```python
notify(title="Hello", body="This is a notification", image="path/to/image.png")
```
### 添加进度条
显示进度条：

```python
notify(title="Hello", body="This is a notification", progress={"value": "50"})
```
### 添加音频
播放音频文件：
```python
notify(title="Hello", body="This is a notification", audio="path/to/audio.mp3")
```

### 添加按钮
添加按钮以实现用户交互：
```python
notify(title="Hello", body="This is a notification", button="Click me")
```

### 添加输入框
添加输入框以获取用户输入：
```python
notify(title="Hello", body="This is a notification", input="Enter your name")
```
### 添加选择框
添加选择框以供用户选择：
```python
notify(title="Hello", body="This is a notification", selection=["Option 1", "Option 2"])
```


## 异步通知
使用`toast`函数创建异步通知，支持更复杂的交互逻辑：
```python
from packaging_tutorial.win11_notificationbar.win11_notificationbar import notify

notify(
    title="标题",
    body="内容",
    icon="https://example.com/icon.png",
    image="https://example.com/image.png",
    button="点击我",
    input="输入框",
    progress={"value": "0.5", "status": "进行中"},
    # audio="ms-winsoundevent:Notification.Looping.Alarm",
    on_click=lambda: print("点击了通知"),

)

```
![img.png](img.png)

## 更新进度
动态更新通知中的进度条：
```python
update_progress({"value": "75"})
```
## 总结
通过本项目，我们实现了在Windows 11中创建丰富且交互性强的通知系统。这些功能可以帮助开发者更好地与用户进行互动，提升用户体验。
debounce-redis-guard 0.0.8
A Python library for debouncing requests with support for multiple workers.
Requires Python: >=3.6

# debounce-redis-guard

debounce-redis-guard is a Python library designed to handle debouncing of requests in applications that support multiple workers. This library helps to prevent multiple identical requests from being processed simultaneously, thus improving efficiency and reducing redundant processing.

## Features

- Debounce requests to avoid duplicate processing
- Supports multiple workers
- Integration with Redis for distributed locking and caching
- Custom logging

## Installation

```bash
pip install debounce-redis-guard
```




# Initialize Debouncer with logger and Redis configuration

```

from debounce_redis_guard import Debouncer, RedisConfig

debouncer = Debouncer(logger=my_logger, redis_config=my_redis_config)

```


## Usage

```
@debouncer.debounce(ttl=60, function_name='my_function', identity_name='data')
async def my_function(data):
    # Your function logic here
    return {'message': 'Processed'}
    
```

## Example Usage

```
@router.get("/perform-final-payment", , name="final payment perform by users - should only call one time.")
@debouncer.debounce(ttl=599, function_name="getAllPaymentGatewayList")
async def finalPay():

    response = code functionality
    return response

```

## Implementation Behavior with Concurrent clone request with ApiDebouncer: Sequence Diagram
![Screenshot](https://raw.githubusercontent.com/cdeepana/debounce_redis_guard/refs/heads/main/concurrent_clone_request.png)


## Implementation Behavior with  clone requests (time to time - within TTL duration) with ApiDebouncer: Sequence Diagram

[//]: # (![Screenshot]&#40;docs/images/delayed_clone_request.png&#41;)
![Screenshot](https://raw.githubusercontent.com/cdeepana/debounce_redis_guard/refs/heads/main/delayed_clone_request.png)


## Jmeter Summary Report Sample to Compair with and without debouncing feature

[//]: # (![Screenshot]&#40;docs/images/experiment.png&#41;)
![Screenshot](https://raw.githubusercontent.com/cdeepana/debounce_redis_guard/refs/heads/main/experiment.png)
web-extractor-AB7zz 0.0.5
A Python library for extracting structured data from web pages using AI.
Requires Python: >=3.10

# Web Data Extractor

A Python library for extracting structured data from web pages using AI. This library uses Google's Gemini AI to intelligently extract and format data according to your specified schema.

## Features

- Easy-to-use interface for web data extraction
- AI-powered content analysis using Google's Gemini AI
- Flexible schema definition for structured data extraction
- Automatic handling of web page fetching and parsing

## Installation

```bash
pip install web_extractor
```

## Quick Start

1. First, get your Gemini AI API key from [Google AI Studio](https://makersuite.google.com/app/apikey)

2. Create a `.env` file in your project root and add your API key:

```
GEMINI_API_KEY=your_api_key_here
```

3. Use the library in your code:

```python
from web_extractor import WebDataExtractor
import os
from dotenv import load_dotenv

# Load API key from .env file
load_dotenv()
gemini_api_key = os.getenv("GEMINI_API_KEY")

# Create the extractor
extractor = WebDataExtractor(api_key=gemini_api_key)

# URL to extract data from
url = "https://www.amazon.in/Celestron-AstroMaster-130-EQ-Telescope/dp/B000MLL6RS"

# Define your schema
schema = {
    "name": "string",
    "price": "float",
    "description": "string"
}

# Extract the data
result = extractor.extract(url, schema)

# Use the extracted data
print("Product Name:", result["name"])
print("Price:", result["price"])
print("Description:", result["description"])
```

## Schema Definition

The schema is a dictionary where:
- Keys are the field names you want to extract
- Values are the expected data types ("string", "float", "integer", etc.)

Example schemas:

```python
# Product schema
schema = {
    "name": "string",
    "price": "float",
    "rating": "float",
    "review_count": "integer"
}
```

## Requirements

- Python 3.10 or higher
- Google Gemini AI API key
- Internet connection for web scraping and AI processing