semvec/pypi-packages huggingface dataset:
pacc 0.0.606
Python Android Cluster Control 为在Windows上使用Python通过USB（通用串行总线）或WLAN

该Python模块用于通过USB或WiFi对Android集群（包含模拟器，目前正在适配雷电模拟器
        LDConsole）进行控制，集成了ADB、UIAutomator、数据库、中央控制系统、计算机视觉（图像识别）、验证码破解、
        人工智能（自然语言处理）等功能。

        目前最新版已集成的功能如下：

        1. ADB（安卓调试桥）及UIAutomator（用户界面自动化器）

        2. LDConsole（雷电控制台）及雷电模拟器安卓调试桥及用户自动化器

        3. MySQL数据库的增删改查

        4. WebSocket双端互通

        待集成的功能如下：

        1. 夜神模拟器（Nox.exe、NoxConsole.exe、nox_adb.exe）

        已实现的中央监控系统如下：

        1. 淘宝/拼多多全自动远程刷单APP中央监控系统脚本

        待实现的中央监控系统如下：

        1. 欢友全自动聊天、评论、接打视频脚本app中央监控系统脚本

        2. 汉字大英雄全自动看广告脚本app中央监控系统脚本

        已实现的中央控制脚本如下：

        1. 快手极速版全自动刷金币（刷视频、刷广告、刷直播）中央控制系统脚本

        正在实现中的中央控制脚本如下：

        1. 趣头条全自动刷金币中央控制系统脚本

        待实现的中央控制脚本如下：

        1. 刷咸鱼币中央控制脚本

        2. 陶特红包中央控制脚本

        3. 美团浏览中央控制脚本

        4. 点淘全自动刷金币中央控制脚本

        5. 抖音全自动抢福袋脚本app中央控制脚本

        6. 淘礼金全自动抢单（淘宝0元购）中央控制脚本

        7. 他趣全自动拉黑女粉、聊天、接视频中央控制脚本

        8. 拼多多自动连接人工客服

        9. 抖音极速版全自动刷金币中央控制脚本

        10. 拼多多批发版一折商品全自动采集并发送至QQ群的中央控制脚本

        11. 含羞草传媒拉新（全自动拉人头）中央控制系统

        源码可通过如下渠道获取：

        Gitee：https://gitee.com/coco56/pacc

        Github：https://github.com/cocos56/pacc
urlprofiler 1.5.2
Fetches available metadata about a URL
Requires Python: >=3.7

![Workflow](https://github.com/github/docs/actions/workflows/python-app.yml/badge.svg)

# urlprofiler

**urlprofiler** fetches metadata about websites. For instance:

```
>>> from urlprofiler.profile.profile_url import profile_url
>>> metadata = profile_url("https://example.com")
>>> print(metadata)
{"url": "https://example.com", "was_redirected": True....}
```
urlprofiler makes it easy to access metadata about a URL, such as:

- HTTP status codes
- Connection latency
- SSL certificate information
- IP address of the hosting server

It can be imported as a Python package, or run directly on the command line using
`urlprofiler <URL>`. It can even take multiple URLs, and returns metadata in the order
the URLs are passed.

# Installing urlprofiler

urlprofiler is available on PyPI:

`$ python -m pip install urlprofiler`

Officially urlprofiler runs on 3.7 and above. It was built on a macOS operating system and 
has been tested on Linux. Don't forget to activate a virtual environment if you plan to use
`urlprofiler` within your own package:

`$ python -m venv venv`

# Data points & best practices

- HTTP status with string translation e.g., 200, OK
- Redirection history for the given URL
- Connection latency (in seconds)
- Cookies returned by the HTTP connection
- SSL certificate data, including: -
    - Issuer
    - Expiry Date
    - DNS names
    - Organization details
- IP address of host server

To achieve reliable results, be sure to supply a full URL, includng protocol, domain name 
and TLD. Please be aware that, if only a domain and TLD is supplied, `urlprofiler` will add 
`http` to the beginning of the supplied string.

# Cloning the repository and running the tests

You can clone the repository by using `git clone` followed by a link to this repository.

To run the tests, install pytest via `$ pip install pytest` in a virtual environment. 
You can then run the tests with `pytest -v`. Alternatively, you can install tox with 
`pip install tox` and run all associated tools (for code coverage, linting et al.) via `$ tox`.
torch-ort-infer 1.13.1
Accelerate PyTorch models with ONNX Runtime OpenVINO EP
Requires Python: >=3.7

[OpenVINO™ Integration with Torch-ORT](https://github.com/pytorch/ort#-inference) accelerates PyTorch models using OpenVINO™ Execution Provider for ONNX Runtime. This product is designed for PyTorch developers who want to get started with OpenVINO™ in their inferencing applications. It delivers OpenVINO™ inline optimizations that enhance inferencing performance with minimal code modifications.

OpenVINO™ Integration with Torch-ORT accelerates inference across many AI models on a variety of Intel® hardware such as:

* Intel® CPUs
* Intel® integrated GPUs
* Intel® Movidius™ Vision Processing Units - referred to as VPU.

## Installation

### Requirements
* Ubuntu 18.04, 20.04
* Python 3.7, 3.8 or 3.9

#### This package supports:
* Intel® CPUs
* Intel® integrated GPUs
* Intel® Movidius™ Vision Processing Units (VPUs).

The torch-ort-infer package has dependency on the onnxruntime-openvino package that will be installed by default to run inference workloads. This onnxruntime-openvino package comes with pre-built libraries of OpenVINO™ version 2022.2.0 eliminating the need to install OpenVINO™ separately. The OpenVINO™ libraries are prebuilt with CXX11_ABI flag set to 0.

For more details, please refer to [OpenVINO™ Execution Provider for ONNX Runtime.](https://onnxruntime.ai/docs/execution-providers/OpenVINO-ExecutionProvider.html)

## Post-installation step

Once torch-ort-infer is installed, there is a post-installation step:

`python -m torch_ort.configure`

## Usage

By default, Intel® CPU is used to run inference. However, you can change the default option to either Intel® integrated GPU or Intel® VPU for AI inferencing. Invoke the [provider options](https://github.com/pytorch/ort/blob/main/torch_ort_inference/docs/usage.md#essential-apis) to change the hardware on which inferencing is done.

For more API calls and environment variables, see [Usage](https://github.com/pytorch/ort/blob/main/torch_ort_inference/docs/usage.md).

## Samples
For quick start, explore the [samples](https://github.com/pytorch/ort/tree/main/torch_ort_inference/demos) for few HuggingFace and TorchVision models.

## License
OpenVINO™ Integration with Torch-ORT is licensed under [MIT](https://github.com/pytorch/ort/blob/main/LICENSE). By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.

## Support
Please submit your questions, feature requests and bug reports via [GitHub Issues](https://github.com/pytorch/ort/issues).

## How to Contribute
We welcome community contributions to OpenVINO™ Integration with Torch-ORT. If you have an idea for improvement:

* Share your proposal via [GitHub Issues](https://github.com/pytorch/ort/issues).
* Submit a [Pull Request](https://github.com/pytorch/ort/pulls).
image-processing-ghf-package 0.0.1
Test version - Image processing. Projeto criado pela Karina Kato para o bootcamp de ciência de dados da Unimed-BH.
Requires Python: >=3.8

# Desafio Unimed BH: Pacote de Processamento de Imagens

## DescriÃ§Ã£o
- Esse projeto consiste na criaÃ§Ã£o de um pacote para python, com base no curso ministrado por Karina Kato no bootcamp de ciÃªncia de dados da Unimed-BH.
- Pacote de exemplo, baseado no [scikit-image](https://scikit-image.org)
---

## Passos para a hospedagem no Pypi
- Primeiro se realiza uma instalaÃ§Ã£o ou upgrade do `pip`, `twine`, `setuptools` e `wheel`

python -m pip install --upgrade pip
python -m pip install --user twine
python -m pip install --user setuptools
python -m pip install --user wheel

- Cria-se as distribuiÃ§Ãµes

python setup.py sdist bdist_wheel

- SerÃ£o criadas 3 pastas no projeto: 
  - build
  - dist
  - image_processing_package.egg-info

- O prÃ³ximo passo Ã© o upload no Test Pypi.

python -m twine upload --repository-url https://test.pypi.org/legacy/ dist/*

- O comando para upload no Pypi Ã©:

python -m twine upload --repository-url https://upload.pypi.org/legacy/ dist/*

## InstalaÃ§Ã£o
- Para a instalaÃ§Ã£o local, Ã© necessÃ¡rio instalar os requerimentos primeiro.

pip install -r requirements.txt


Para a instalaÃ§Ã£o do pacote, basta usar o [pip](https://pip.pypa.io/en/stable/) direcionando para o image-processing-ghf-package

```bash
pip install image-processing-ghf-package==0.0.1
```
---
## Uso
- Basta importar os mÃ³dulos de acordo com o que deseja

```python
from image-processing-ghf-package.processing import transformation
transformation.resize_image(image_test, 0.5)
```
---
## Autor
Geraldo Henrique Fonseca

## LicenÃ§a
[MIT](https://choosealicense.com/licenses/mit/)