semvec/pypi-packages huggingface dataset:
structure-generator-by-xakepanonim 0.3.0
This package automatically generates a project architecture description and saves it in the README.md file.
Requires Python: >=3.6

# Project Structure Generator

This package automatically generates a project architecture description and saves it in the `README.md` file.

## Installation

Install the package using pip:
```bash
pip install structure-generator-by-xakepanonim
```

## Usage

Before using the package, make sure you are in the root directory of the project and run the following command:

```bash
generate-structure .
```

## Configuration

The package supports multiple configuration file options for specifying exclusions:

1. `structure.toml` — the default configuration file
2. `pyproject.toml` — use the section `[tool.structure_generator]` in this file

Parameters:

- `exclude` — a list of files to exclude from the generation process
- `read_docstrings` — if `true`, the documentation strings (docstrings) will be read for each file
- `output_file` — the path to the file where the architecture description will be saved

Example configuration in `structure.toml`:
```toml
exclude = [
    'venv',
    '__pycache__',
    '.git',
]

read_docstrings = true
output_file = "README.md"
```

Example configuration in `pyproject.toml`:
```toml
[tool.structure_generator]
exclude = [
    'venv',
    '__pycache__',
    '.git',
]

read_docstrings = true
output_file = "README.md"
```

---

# Генератор Структуры Проекта

Этот пакет автоматически генерирует описание архитектуры проекта и сохраняет его в файл `README.md`.

## Установка

Установите пакет через pip:
```bash
pip install structure-generator-by-xakepanonim
```

## Использование

Перед тем как использовать пакет, убедитесь, что вы находитесь в корневой папке проекта, и выполните следующую команду:

```bash
generate-structure .
```

## Конфигурация

Пакет поддерживает несколько вариантов файлов конфигурации для указания исключений:

1. `structure.toml` — дефолтный файл конфигурации
2. `pyproject.toml` — используйте секцию `[tool.structure_generator]` в этом файле

Параметры:

- `exclude` — список файлов, которые необходимо исключить из генерации
- `read_docstrings` — если `true`, то будет считано описание документации для каждого файла
- `output_file` — путь к файлу, в который будет сохранено описание архитектуры

Пример настройки в `structure.toml`:
```toml
exclude = [
    'venv',
    '__pycache__',
    '.git',
]

read_docstrings = true
output_file = "README.md"
```

Пример настройки в `pyproject.toml`:
```toml
[tool.structure_generator]
exclude = [
    'venv',
    '__pycache__',
    '.git',
]

read_docstrings = true
output_file = "README.md"
```
kvrocks-dao 0.0.7
Testing
Requires Python: >=3.8


## Installation

```pip
pip install kvrocks-dao
```


setup config.ini in the root of project

```ini
    [kvrocks]
    host = 0.0.0.0
    port = 6666
```

## Examples

```py
    from kvrocks_dao import BaseDAO, BaseEntity

    class MediaEnity(BaseEntity):
        def __init__(self, _id: int, name: str, type: int):
            super().__init__(_id)
            self.name = name
            self.type = type

    class MediaDao(BaseDAO[MediaEnity]):
        def __init__(self):
            super().__init__(MediaEnity, "media")

    dao = MediaDao()

   # --- or ---

    dao = BaseDAO.from_entity(MediaEnity, "media")

    # get 
    # return MediaEnity
    media = dao.get(_id)

    # set
    media = Media(1, "Nhớ", 10)
    dao.set(media)


    # set from dataframe
    for _, row in df.iterrows():
        media = MediaEnity.from_dict(row.to_dict())
        print(media.to_dict())
        dao.set(media)

    # count
    dao.count()   

    # get list 
    # return List[tuple[int, MediaEnity]]
    dao.mget()

```
CapSpot 0.1.0
An image matching package using ONNX models
Requires Python: >=3.7

# CapSpot
CAPTCHA Resolution Tool

[![PyPI](https://img.shields.io/pypi/v/CapSpot.svg)](https://pypi.org/project/CapSpot/)
[//]: # ([![License](https://img.shields.io/github/license/miaohancheng/CapSpot)](https://github.com/miaohancheng/CapSpot/blob/main/LICENSE))

## Introduction

`CapSpot` is an image matching library based on ONNX models that locates and identifies sub-images within a target image using a template. It is designed to solve CAPTCHA challenges by finding specific click points in images, making it an invaluable tool for automated testing and security applications.

## Example Outputs
### Example 1
- **Target Image**

![test.png](test_pic/test.png)

- **Detection Template and Order**

![test_mo.png](test_pic/test_mo.png)

- **Detection Results**

![result.png](test_pic/result.png)

- **JSON Response**
```json
{'img0': [103, 35, 145, 76], 'img1': [162, 90, 196, 127], 'img2': [175, 189, 199, 218]}
```

### Example 2
- **Target Image**

  ![test1.png](test_pic/test1.png)
- **Detection Template and Order**

  ![test1_mo.png](test_pic/test1_mo.png)
- **Detection Results**

![result1.png](test_pic/result1.png)

- **JSON Response**
```json
{'img0': [24, 28, 89, 93], 'img1': [243, 60, 300, 119], 'img2': [134, 128, 191, 184]}
```


## Features

- - Utilizes pre-trained ONNX models for object detection and similarity calculations.
- - Supports optional GPU acceleration.
- - Offers a simple and easy-to-use API.

## Installation
### Via pip:
```bash 
pip install CapSpot
```

### From source:
```bash
git clone https://github.com/miaohancheng/CapSpot.git
cd CapSpot
python setup.py install
```

## Usage
```python
from CapSpot import ImageMatcher

if __name__ == '__main__':
    matcher = ImageMatcher()
    matches = matcher.match_images('./test_pic/test1_mo.png', './test_pic/test1.png')
    matcher.display_results('./test_pic/test1.png', matches)
    print(matches)

```

### Configuration Parameters

	•	ImageMatcher class
	•	sim_model_name: Name of the similarity model (ONNX format), with a default model provided.
	•	det_model_name: Name of the detection model (ONNX format), with a default model provided.
	•	match_images method
	•	template_file: Path to the template image.
	•	target_file: Path to the target image.

## Model Downloads

Place the required model files (similarity_model.onnx and detection_model.onnx) in the models/ directory.

## Dependencies

	•	Python 3.6 or higher
	•	numpy
	•	opencv-python
	•	Pillow
	•	onnxruntime

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Contact

	•	Author: Miao HanCheng
	•	Email: hanchengmiao@gmail.com
the-cves-cli 0.1.10
The CVEs CLI
Requires Python: >=3.12

# Installation instructions
## Prerequisities 
- Python 3.12 or higher
- UV CLI


### Once the cli is published to PyPi, install it using pip:
```pip install the_cves_cli```
### Otherwise meanwhile one needs to clone the repo, build and install from local: 
1. ```uv build```
2. Install it from local
``` uv pip install /Users/user/the-cves/dist/the_cves_cli-0.1.1-py3-none-any.whl```

### Running the CLI
```the-cves-cli  --help```

### Env variables required
```shell
THE_CVES_CLI_HOST=<host of the cves backend>
THE_CVES_CLI_USERNAME=<user name of the app>
THE_CVES_CLI_API_KEY=<the cves app token>
THE_CVES_CLI_CONFLUENCE_DOMAIN=<confluence domain>
THE_CVES_CLI_CONFLUENCE_TOKEN=<confluence token>
THE_CVES_CLI_CONFLUENCE_USER=<confluence user>
```

example for cli commands:
running a new CVE report (from the repo root path):
```shell
PYTHONPATH=$(pwd) python the_cves_cli/the_cves_cli/main.py run_report <product-id> <release-id> <confluence-space-key> --image-id=<image-id-value> --cve=<cve-value>

```