semvec/pypi-packages huggingface dataset:
tkShadow 1.0.3.1
在您的 tkinter 项目上为控件或 Canvas 元素添加阴影
Requires Python: >=3.10

<h1 align="center">tkShadow</h1>

<p align="center">
<code>tkShadow</code> 可以在您的 <code>tkinter</code> 项目上为控件或 <code>Canvas</code> 元素添加阴影。
<br/>
</p>

<p align="center">
<a href="https://github.com/CodeCrafter-TL/tkShadow/releases"><img alt="Version" src="https://img.shields.io/github/v/release/CodeCrafter-TL/tkShadow?include_prereleases&logo=github&label=Version" title="Latest Version" /></a>
<a href="https://github.com/CodeCrafter-TL/tkShadow"><img alt="Size" src="https://img.shields.io/github/languages/code-size/CodeCrafter-TL/tkShadow?label=Size&logo=github" title="Code Size"/></a>
<br/>
<a href="https://github.com/CodeCrafter-TL/tkShadow/watchers"><img alt="Watchers" src="https://img.shields.io/github/watchers/CodeCrafter-TL/tkShadow?label=Watchers&logo=github&style=flat" title="Watchers" /></a>
<a href="https://github.com/CodeCrafter-TL/tkShadow/forks"><img alt="Forks" src="https://img.shields.io/github/forks/CodeCrafter-TL/tkShadow?label=Forks&logo=github&style=flat" title="Forks" /></a>
<a href="https://github.com/CodeCrafter-TL/tkShadow/stargazers"><img alt="Stars" src="https://img.shields.io/github/stars/CodeCrafter-TL/tkShadow?label=Stars&color=gold&logo=github&style=flat" title="Stars" /></a>
<a href="https://github.com/CodeCrafter-TL/tkShadow/issues"><img alt="Issues" src="https://img.shields.io/github/issues/CodeCrafter-TL/tkShadow?label=Issues&logo=github" title="Issues" /></a>
<a href="https://github.com/CodeCrafter-TL/tkShadow/pulls"><img alt="Pull Requests" src="https://img.shields.io/github/issues-pr/CodeCrafter-TL/tkShadow?label=Pull%20Requests&logo=github" title="Pull Requests" /></a>
<a href="https://github.com/CodeCrafter-TL/tkShadow/discussions"><img alt="Discussions" src="https://img.shields.io/github/discussions/CodeCrafter-TL/tkShadow?label=Discussions&logo=github" title="Discussions" /></a>
</p>

<p align="center">
<a href="https://github.com/CodeCrafter-TL/tkShadow/pulse"><img alt="Insights" src="https://repobeats.axiom.co/api/embed/58991389f220273e0fcc84d34f30e97ad026be9f.svg" /></a>
</p>

<p align="center">
    <a href="https://star-history.com/#CodeCrafter-TL/tkShadow&Date">
        <picture>
            <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=CodeCrafter-TL/tkShadow&type=Date&theme=dark" />
            <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=CodeCrafter-TL/tkShadow&type=Date" />
            <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=CodeCrafter-TL/tkShadow&type=Date" />
        </picture>
    </a>
</p>


📦 安装
----------------------

### ✅ 稳定版本

* 🔖 最新版本 : `1.0.3`
* 🕓 发布日期 : 2024-07-16

```bash
pip install tkshadow==1.0.3
```

👀 更多
--------------

* 📚 教程和文档: [GitHub Pages](https://codecrafter-tl.github.io/tkShadow/)
* 🚀 存储库:
[GitHub](https://github.com/codecrafter-tl/tkshadow)
reno-aina 0.1.0
reno aina
Requires Python: >=3.11

# AINA IS ALL IN AL

目前定位可能是一个rag 框架 。


目前已实现

调用大模型 ok

调用qwen2 大模型（本地）

调用文心一言（千帆大模型）（在线）

文心一言 没有开源只能调用线上版本目前有免费次数可用去官网申请即可

多轮对话+展示页面

python 版本 ok

3.12.4

版本规划

v0.0.1  运行模型(支持多模型) 输入&回答

v0.1.0   多轮对话 +展示页面

v1.0.0  引入 rag 本地文档+搜索引擎

v1.1.0 多功能 模板

v1.2.0  多模态支持

v1.3.0 支持多平台接入
django-keycloak-lma-cut 0.1.7
Cut down version of the package django_keycloak_lma


INFO:


settings.KEYCLOAK_OIDC_PROFILE_MODEL = OpenIdConnectProfile

Available features:
- bearer token auth with introspect and user creation (KeycloakTokenAuthentication)
- permission check (auth/permissions)


Examples of using features: tests/lma
DIAL-API-THULASI24 1.0.0
Dail_API

Cisco Router NETCONF Script

This Python script retrieves configuration data from a Cisco router using NETCONF. It utilizes the ncclient library for NETCONF communication and optionally leverages Google's Generative AI service (currently Gemini) for XML request generation.

Features:

    Connects to Cisco IOS XE or IOS XR routers.
    Retrieves configuration data using NETCONF.
    Extracts leaf nodes from the configuration data.
    Allows user selection of specific attributes through:
        Line number from a generated file.
        Attribute name search.
    Creates the corresponding NETCONF XML request (optional, uses Google Generative AI).

Installation:

Bash

Plain Text

pip install -r requirements.txt

Usage:

    Configure Script:
        Update GOOGLE_API_KEY environment variable with your Google API key (optional for Generative AI).
        Edit main function to specify Cisco router version (IOS XE or IOS XR).
    Run Script:
        Execute python main.py
    Follow Prompts:
        Provide router details (IP, username, password).
        Select attributes for retrieval (optional, uses Google Generative AI).

Dependencies:

    ncclient
    xmltodict
    argparse
    langchain (optional, for Generative AI)
pythonasm-linhhanpy 0.0.1
A Python library for ASM
Requires Python: >=3.6

# pythonasm Library
I. Overview

This is a Python library that contains a series of functions related to data processing and operations. It can simulate the input and output of an assembler. The author is Lin Honghan, a Chinese sixth-grade primary school student. The pypi account is linhhanpy, and the gitee account is linhhpy. It was made during the summer vacation when being bored.
More functions will be updated in the future, adding an assembler virtual machine and using real assembler instructions.

II. Main Functions
- Defined basic mathematical operation functions: add (addition), sub (subtraction), mul (multiplication), div (division, handling the case where the divisor is 0).
- Handles instructions such as db, mov, etc.
- operation function: Matches and performs corresponding operation operations according to specific instruction patterns.
- check function: Used for checking specific conditions.
- asm function: Can read the specified file, parse the instructions in it, and perform corresponding processing.

III. Usage Method

After importing the relevant modules, you can call the functions within for usage.
IV. Dependent Libraries
re: Used for regular expression operations.
os: Used for file and directory-related operations.
V. Sample Code
```python
import pythonasm  # 假设库名为 pythonasm

pythonasm.asm('pyasm.asm')
```
```asm
#pyasm.asm
msg db "abc"
mov ax,3
mov bx,0
mov cx,msg
mov dx,3
int 80h
mov ax,4
mov bx,1
mov dx,3
int 80h
```
```input
#command_input
123
```
```command
#command_out
123
```
VI. Copyright Statement

This library is open source, but the author and source must be indicated. The final interpretation right belongs to Lin Honghan.
# pythonasm 库

 一、概述
这是一个包含了一系列与数据处理和操作相关功能的 Python 库，能模拟汇编器的输入输出，作者为中国六年级小学生林泓翰pypi账号linhhanpy，gitee账号linhhpy，暑假无聊做的。
以后会更新更多功能，增加汇编虚拟机和使用真正的汇编指令。

 二、主要功能
 - 定义了基本的数学运算函数：`add`（加法）、`sub`（减法）、`mul`（乘法）、`div`（除法，处理除数为 0 的情况）。
 - 处理`db`，`mov`等指令。
 - `operation` 函数：根据特定的指令模式匹配并执行相应的运算操作。
 - `check` 函数：用于进行特定条件的检查。
 - `asm` 函数：能够读取指定文件，解析其中的指令并进行相应处理。

 三、使用方法
导入相关模块后，即可调用其中的函数进行使用。

 四、依赖库
 - `re` ：用于正则表达式操作。
 - `os` ：用于文件和目录相关操作。

 五、示例代码
```python
import pythonasm

pythonasm.asm('pyasm.asm')
```
```asm
#pyasm.asm
msg db "abc"
mov ax,3
mov bx,0
mov cx,msg
mov dx,3
int 80h
mov ax,4
mov bx,1
mov dx,3
int 80h
```
```input
#command_input
123
```
```command
#command_out
123
```
六、版权声明
本库开源，但需标明作者和出处，最终解释权归林泓翰所有。
DIAL-API 1.0.0
Dail_API

Cisco Router NETCONF Script

This Python script retrieves configuration data from a Cisco router using NETCONF. It utilizes the ncclient library for NETCONF communication and optionally leverages Google's Generative AI service (currently Gemini) for XML request generation.

Features:

    Connects to Cisco IOS XE or IOS XR routers.
    Retrieves configuration data using NETCONF.
    Extracts leaf nodes from the configuration data.
    Allows user selection of specific attributes through:
        Line number from a generated file.
        Attribute name search.
    Creates the corresponding NETCONF XML request (optional, uses Google Generative AI).

Installation:

Bash

Plain Text

pip install -r requirements.txt

Usage:

    Configure Script:
        Update GOOGLE_API_KEY environment variable with your Google API key (optional for Generative AI).
        Edit main function to specify Cisco router version (IOS XE or IOS XR).
    Run Script:
        Execute python main.py
    Follow Prompts:
        Provide router details (IP, username, password).
        Select attributes for retrieval (optional, uses Google Generative AI).

Dependencies:

    ncclient
    xmltodict
    argparse
    langchain (optional, for Generative AI)
skymavis-solver 1.1
Module Bypass Sky Mavis Security Verification


**`skymavis_solver`** is a Python package designed to bypass CAPTCHA security verification used by Sky Mavis. This tool processes CAPTCHA images to determine if the challenge has been successfully bypassed.

### Features:

- **Specific to Sky Mavis CAPTCHA**: Works exclusively with Sky Mavis security verifications.
- **Image Zooming Required**: Ensure CAPTCHA images are properly zoomed in before processing for best results.

### Usage:

```python
from skymavis_solver import bypass_captcha

# Provide a properly zoomed CAPTCHA image
image_path = 'path/to/captcha_image.png'

# Process the CAPTCHA
result = bypass_captcha(image_path)

# Check the result
if result:
    print("CAPTCHA bypassed successfully!")
else:
    print("CAPTCHA not bypassed.")
ollama-api 0.1.1
A Python client for the Ollama API
Requires Python: >=3.6

# Ollama API Python Client

A Python client for interacting with the Ollama API.

## Installation

```bash
pip install ollama_api
```

## Usage

```python
from ollama_api import OllamaClient

client = OllamaClient()
response = client.generate_completion(model="llama3", prompt="Why is the sky blue?")
print(response)
```

## Documentation

For more details, refer to the [Ollama API documentation](https://github.com/ollama/ollama/tree/main/docs).


### Step 6: Install and Test

Navigate to the root directory of your package and install it locally:

```bash
pip install .
```

Now you can test the package by importing and using the `OllamaClient` class in a Python script or interactive session.

This basic package structure and client class should give you a good starting point for interacting with the Ollama API using Python. You can expand and refine it further based on your specific needs and the API's capabilities.
django-auto-translator 0.1.2
Automatically translate Django's pot Files
Requires Python: >=3.8

[![Upload Python Package](https://github.com/myridia/django-auto-translator/actions/workflows/publish.yml/badge.svg)](https://github.com/myridia/django-auto-translator/actions/workflows/publish.yml)


# ![django-deep-translator](img/django-deep-translator.png ) django-auto-translator  

Autotranslate django `.po` translation files package built on top of  [deep-translator](https://pypi.org/project/deep-translator/)

Finds all the generated pot (.po) files under the locale paths (LOCALE_PATHS) specified in django project settings, and translates them automatically with default source language as **english**.



## Install 

### Install via PIP 

```bash
  pip install django-auto-translator
  ```

## Add `'django-auto-translator'` to your `INSTALLED_APPS` setting:

```py
INSTALLED_APPS = (
 ...
 'django-auto-translator',
 )

```

## Command translate to all  languages 

```bash 
python manage.py translate_messages
```

## Option Parameter
- ``-f, --set-fuzzy``: Set the 'fuzzy' flag on autotranslated entries
- ``-l, --locale 'locale'``: Only translate the specified locales
- ``-u, --untranslated``: Only translate the untranslated messages
- ``-s, --source-language``: Override the default source language (en) used for translation
- ``-S, --silent``: Don't show the verbose output
- ``-t, --throttle``: Throttle the maximum of seconds for the next translate request(default=10 sec)
         

### Example:
```bash
    python manage.py translate_messages -l 'de' -l 'es'
```

## Important Django Language Setting

In your settings, list the relative path to locale folders, example:

```py
LOCALE_PATHS = (
    'locale',
    'home/locale',
    'products/locale',
    'services/locale',
)
```



---

## Credits

Forked from:  https://github.com/wmo-raf/django-deep-translator

Artwork Logo from Eypros: https://openclipart.org/artist/Eypros
pinyin-order 0.0.1
包含中文的字符串使用拼音比较大小，可用于使用拼音排序字符串等的需求。
Requires Python: >=3.8

# pinyin-order

包含中文的字符串使用拼音比较大小，可用于使用拼音排序字符串等的需求。

```python
from pinyin_order import PinyinOrderStr, pinyin_sorted


result1 = PinyinOrderStr('汉字和English') == PinyinOrderStr('汉子和english')  # True
result2 = PinyinOrderStr('汉字') > PinyinOrderStr('hanzi')  # True
result3 = PinyinOrderStr('abc啊') <= PinyinOrderStr('ABC哦')  # True

strings = ['a哦', 'a啊', '汉字', '这是abc', '者时ABC', '123', 'abc', '漢字', '🏀🐔🏀', ' ']
result4 = pinyin_sorted(strings)
# [' ', '123', 'abc', 'a啊', 'a哦', '汉字', '漢字', '这是abc', '者时ABC', '🏀🐔🏀']
```
db-copilot-tool 0.0.8
nl2sql tool for prompt flow
Requires Python: >=3.8

nl2sql tool for prompt flow

# How to create promptflow endpoint your workspace
## 1. Prepare your workspace
Open your workspace in Azure ML studio portal <br>
Example Studio Portal URL: <br>
https://main.ml.azure.com/?wsid=/subscriptions/1b75927d-563b-49d2-bf8a-772f7d6a170e/resourceGroups/ragdev/providers/Microsoft.MachineLearningServices/workspaces/RAGDev&flight=promptDesigner&tid=72f988bf-86f1-41af-91ab-2d7cd011db47
## 2. Create a ci (compute instance)
Create a ci with name with prefix "ci-lin-cpu-0-" , for example ci-lin-cpu-0-alan
## 3. Config Customer Application on your ci
### 3.1 Application Name
Application Name has to be: promptflow-runtime
### 3.2 Port
Target Port and Published Port have to be: 8080
### 3.3 Docker Image
docker image can be modulesdkpreview/prt:tag. <br>
For example: 
modulesdkpreview/prt:v20230420-140738
### 3.4 Restart ci
## 4. Install dbcopilot on your ci
### 4.1 Open Terminal of your ci
### 4.2 upload whl files to your ci
dbcopilot-0.2.0-py3-none-any.whl (get it from https://dev.azure.com/TScience/NL2Code/_artifacts/feed/AIMS.TScience.NL2Code/PyPI/dbcopilot/overview/0.3.0)<br>
db_copilot_tool-0.1.0-py3-none-any.whl <br>
embeddingstore-0.0.1-py3-none-any.whl (optional) <br>
promptflow_sdk-0.0.1-py3-none-any.whl (optional) <br>
### 4.3 Install whl files in promptflow container
Find your promptflow container id with command: docker ps -a <br>
Copy whl files to promptflow container with command: docker cp xxx.whl <container_id>:/xxx.whl <br>
Login promptflow container with command: docker exec -it <container_id> /bin/bash <br>
install whl files with command: pip install xxx.whl
### 4.4 Install Microsoft ODBC in promptflow container
Reference Doc: https://learn.microsoft.com/en-us/sql/connect/odbc/linux-mac/installing-the-microsoft-odbc-driver-for-sql-server?view=sql-server-ver16&tabs=ubuntu18-install%2Calpine17-install%2Cdebian8-install%2Credhat7-13-install%2Crhel7-offline#18 <br>
Commands: <br>
apt-get update <br>
apt-get install -y curl lsb-core <br>
curl https://packages.microsoft.com/keys/microsoft.asc | apt-key add - <br>
curl https://packages.microsoft.com/config/ubuntu/$(lsb_release -rs)/prod.list > /etc/apt/sources.list.d/mssql-release.list <br>
apt-get update <br>
ACCEPT_EULA=Y apt-get install -y msodbcsql18 <br>
apt-get install -y unixodbc-dev <br>
### 4.5 Restart promptflow container
exit
docker stop container_id <br>
docker start container_id <br>

## 5. Copy Local DB file to promptflow container (optional)

## 6. Get promptflow endpoint url
promptflow endpoint url can be found in the ci overview page, click "promptflow-runtime" link <br>
bk-audit 1.2.3b0
Bk Audit SDK
Requires Python: <4,>=3.8

![logo.png](https://github.com/TencentBlueKing/bk-audit-python-sdk/blob/master/assests/logo.png)

[![license](https://img.shields.io/badge/license-MIT-brightgreen.svg?style=flat)](https://github.com/TencentBlueKing/bk-audit-python-sdk/blob/master/LICENSE.txt)
[![Release Version](https://img.shields.io/badge/release-1.2.3b0-brightgreen.svg)](https://github.com/TencentBlueKing/bk-audit-python-sdk/releases)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/TencentBlueKing/bk-audit-python-sdk/pulls)
[![codecov](https://codecov.io/github/TencentBlueKing/bk-audit-python-sdk/branch/master/graph/badge.svg?token=CUG20ZMOVQ)](https://codecov.io/github/TencentBlueKing/bk-audit-python-sdk)
[![Test](https://github.com/TencentBlueKing/bk-audit-python-sdk/actions/workflows/unittest_py3.yml/badge.svg)](https://github.com/TencentBlueKing/bk-audit-python-sdk/actions/workflows/unittest_py3.yml)

[(English Documents Available)](https://github.com/TencentBlueKing/bk-audit-python-sdk/blob/master/readme_en.md)

## Overview

bk-audit-python-sdk 是蓝鲸审计中心 (BK-AUDIT) 提供的用于快速接入审计体系的 Python SDK

## Features

- [Basic] 兼容 Python2/Python3
- [Audit] 支持自定义审计事件处理，将非标准字段格式化为审计事件
- [Audit] 支持自定义审计事件输出，内置审计事件输出到日志文件或通过 OT 上报
- [Contrib] Django Support, 集成了 Django 专用事件处理，通过注入 Request 对象简化审计事件上报
- [Contrib] OpenTelemetry Support, 集成 OT Log 上报模块，直接上报到 OT Collector

## Getting started

### Installation

```bash
$ pip install bk-audit
```

### Usage

- [使用文档](https://github.com/TencentBlueKing/bk-audit-python-sdk/blob/master/docs/usage.md)

## Roadmap

- [版本日志](https://github.com/TencentBlueKing/bk-audit-python-sdk/blob/master/release.md)

## Support

- [蓝鲸论坛](https://bk.tencent.com/s-mart/community)
- [蓝鲸 DevOps 在线视频教程](https://bk.tencent.com/s-mart/video/)
- [蓝鲸社区版交流群](https://jq.qq.com/?_wv=1027&k=5zk8F7G)

## BlueKing Community

- [BK-CMDB](https://github.com/Tencent/bk-cmdb)：蓝鲸配置平台（蓝鲸 CMDB）是一个面向资产及应用的企业级配置管理平台。
- [BK-CI](https://github.com/Tencent/bk-ci)：蓝鲸持续集成平台是一个开源的持续集成和持续交付系统，可以轻松将你的研发流程呈现到你面前。
- [BK-BCS](https://github.com/Tencent/bk-bcs)：蓝鲸容器管理平台是以容器技术为基础，为微服务业务提供编排管理的基础服务平台。
- [BK-PaaS](https://github.com/Tencent/bk-paas)：蓝鲸 PaaS 平台是一个开放式的开发平台，让开发者可以方便快捷地创建、开发、部署和管理 SaaS 应用。
- [BK-SOPS](https://github.com/Tencent/bk-sops)：标准运维（SOPS）是通过可视化的图形界面进行任务流程编排和执行的系统，是蓝鲸体系中一款轻量级的调度编排类 SaaS 产品。
- [BK-JOB](https://github.com/Tencent/bk-job) 蓝鲸作业平台(Job)是一套运维脚本管理系统，具备海量任务并发处理能力。

## Contributing

如果你有好的意见或建议，欢迎给我们提 Issues 或 Pull Requests，为蓝鲸开源社区贡献力量。   
[腾讯开源激励计划](https://opensource.tencent.com/contribution) 鼓励开发者的参与和贡献，期待你的加入。

## License

基于 MIT 协议， 详细请参考 [LICENSE](https://github.com/TencentBlueKing/bk-audit-python-sdk/blob/master/License.txt)