semvec/pypi-packages huggingface dataset:
text2pix 2025.5.100903
A library to generate images from text descriptions.
Requires Python: >=3.6

[![PyPI version](https://badge.fury.io/py/text2pix.svg)](https://badge.fury.io/py/text2pix)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/text2pix)](https://pepy.tech/project/text2pix)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)

# Text2Pix

`Text2Pix` is an innovative Python package that generates images based on textual descriptions. It leverages advanced image generation APIs to transform descriptive text into visual representations.

## Installation

To install `Text2Pix`, use pip:

```bash
pip install text2pix
```

## Usage

Using `Text2Pix` is straightforward. Here's a simple example:

```python
from text2pix import generate_image_from_text

text_description = "A serene lake surrounded by mountains at sunset."
image_content = generate_image_from_text(text_description)

# Save or display the image content as needed.
# for example, to save the image to a file:
with open('lake.png', 'wb') as f:
    f.write(image_content)
```

The result will be an image file named `lake.png` that visually represents the text description.

![lake.png](https://wsrv.nl/?cx=0&cy=0&cw=1000&ch=960&fit=cover&a=focal&&url=https://image.pollinations.ai/prompt/A%20serene%20lake%20surrounded%20by%20mountains%20at%20sunset.)

This package is ideal for creative projects, prototyping, and anywhere you need quick visual representations from text.

## Features

- Generate images from text descriptions.
- Easy to use with a simple function call.
- Lightweight and requires minimal setup.

## Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the [issues page](https://github.com/chigwell/text2pix/issues).

## License

This project is licensed under the [MIT License](https://choosealicense.com/licenses/mit/).
threat-scan 0.1.0
ThreatScan is an open-source Python package designed to detect potential physical threats in videos
Requires Python: >=3.9

# ThreatScan
**ThreatScan** is an open-source Python package designed to detect potential physical threats in videos. It leverages the power of video masking autoencoder (VideoMAE) classification models, fine-tuning them to specifically identify events such as fires and other user-defined threats.

## Overview

This package provides a streamlined approach to:

* **Utilize pre-trained VideoMAE models:** Benefit from state-of-the-art video understanding capabilities.
* **Fine-tune for threat detection:** Adapt the base model to accurately classify specific threat events present in video data.
* **Scalable threat analysis:** Process video streams or individual video files for real-time or batch analysis.
* **Extensible architecture:** Easily integrate custom threat classes and extend the model's capabilities.

## Installation

You can install ThreatScan using pip:
```
pip install threatscan
```

# Getting Started

This document outlines the setup and usage instructions for the project.

## Setting up your Virtual Environment

It's highly recommended to use a virtual environment to isolate project dependencies. Follow these steps:

1.  **Create a virtual environment:**
    ```
    python3 -m venv .venv
    ```
    This command creates a new virtual environment in a directory named `.venv` within your project.

2.  **Activate the virtual environment:**

    * **On macOS and Linux:**
        ```
        source .venv/bin/activate
        ```
        Your terminal prompt should now be prefixed with `(.venv)`, indicating that the virtual environment is active.

    * **On Windows (Command Prompt):**
        ```
        .venv\Scripts\activate
        ```

    * **On Windows (PowerShell):**
        ```powershell
        .venv\Scripts\Activate.ps1
        ```

## Installing Dependencies

Once the virtual environment is activated, install the project's required libraries using pip:

```
pip install -r requirements.txt
```

## Running the Application
You can run the main application in different modes:

* Live Feed (Default)
To run the application using your default webcam or live video feed:

```
python3 -m main
```

* With a Video File
To run the application using a specific video file:

```
python3 -m main --source detector/examples/fire.mov
```
Replace `detector/examples/fire.mov` with the actual path to your video file.

* With a Custom Model
To run the application using a specific trained model:

```
python3 -m main --model mymodel/path/etc
```
Replace `mymodel/path/etc` with the correct path to your model file.


## Training your own model
To train your own data go to [Training README](training/README.md)

# Contributing
Contributions are welcome! Please fork the repository and submit a pull request with your improvements.

# License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
pyfritzsms 0.1.4
Python library to send SMS via AVM FRITZ!Box
Requires Python: >=3.9
Keywords: fritzbox, sms

# Python library to send SMS via AVM FRITZ!Box

## Requirements

- AVM FRITZ!Box with internal or USB cellular modem and SMS enabled SIM card.
- User account with app-based second-factor enabled and TOTP secret available.

## Example usage

```python
from fritzsms.fritzbox import FritzBox
from aiohttp import ClientSession
import asyncio


async def async_main_test():
    async with ClientSession() as session:
        box = FritzBox("fritz.box", session)
        box.set_otp("TOTP-secret")
        print(box.get_otp()) # for confirmation during setup
        await box.login("username", "password")
        uid = await box.send_sms("mobile-number", "Hello World!")
        await box.delete_sms(uid)
        print(await box.list_sms())
        await box.logout()


if __name__ == "__main__":
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    loop.run_until_complete(async_main_test())
```
text-thief 0.0.2
A library to scrape text from website and web pages
Requires Python: >=3.10
Keywords: scraper, text, text proccesing, website scraper, words cloud

# A library to parse and scrape text from websites

This library will provide 3 ways to scrape the text from the website:
* The first method is to scrape all text from a single webpage. 
* The second method is to scrape text from the whole website. That includes sitemaps too.
* The third method is to scrape text from the specified list.
Also you could specify a target element (by CSS selector) to scrape only intended parts of webpage.
Empressia-SQLAlchemy-H2 1.0.2
Empressia製のSQLAlchemy用のDialectです。 H2 DatabaseへのJayDeBeApiを使用したJDBC接続をサポートします。
Requires Python: >=3.10
Keywords: Empressia, H2 Database, JDBC, JayDeBeApi, SQLAlchemy

# Empressia SQLAlchemy H2

## 概要

Empressia製のSQLAlchemy用のDialectです。  
H2 DatabaseへのJayDeBeApiを使用したJDBC接続をサポートします。  
接続するための最低限の実装と、基本的な委譲で行えるマッピングしかしていません。  

## 使い方

sqlalchemy.create_engineを呼ぶ前に、empressia_sqlalchemy_h2をimportしておいてください。  
SQLAlchemyへDialectを登録します。  

```python
import empressia_sqlalchemy_h2;
```

JayDeBeApiを使用しているため、  
環境変数JAVA_HOMEに、JDKへのパスを指定しておく必要があります。  
例えば、pythonで設定するには以下のようにします。  

```python
os.environ["JAVA_HOME"] = r"/path/to/JDK/";
```

H2のjarへのパスは、環境変数CLASSPATHに設定するか、  
sqlalchemy.create_engineにjars引数として文字列の配列で渡してください。  

```python
os.environ["CLASSPATH"] = r"/path/to/h2-<version>.jar";
```

```python
sqlalchemy.create_engine("<URL>", jars=[r"/path/to/h2-<version>.jar"]);
```

URLは、以下の形式をサポートしています。  

> h2:///<database>  
> h2+jaydebeapi:///<database>  

databaseには、JDBCのsubnameを指定します。  

例えば、次のようなJDBCの接続文字列について考えます。  

> jdbc:h2:mem:TestDB  

この場合は、以下がsubnameとなります。  

> mem:TestDB  

sqlalchemy.create_engineに渡すURLは、次のようになります。  

> h2:///mem:TestDB  

subnameにMODEを指定することで、勝手に、Dialectの動作を切り替えます。  
MSSQLServer、MariaDB、MySQL、Oracle、PostgreSQLのモードであれば、これだけで十分だと思います。  

> h2:///mem:TestDB;MODE=MSSQLServer  

さらに、Dialectの振る舞いを差し替えたい場合は、  
sqlalchemy.create_engineを呼ぶときに、DelegateDialectを指定するか、DelegateAttributesを指定してください。  
妥当と思う範囲で振る舞いを委譲します。  

Dialectを用意してある場合は、DelegateDialectを指定してください。  
Dialectを用意してない場合や、DelegateDialectだけでは問題が起きる場合は、DelegateAttributesを指定してください。  
DelegateAttributesを優先的に使用します。  

## テスト用のユーティリティ

テスト用に、ユーティリティを用意しています。  
H2 Databaseのjarファイルをダウンロードすることができます。  

```
empressia_sqlalchemy_h2.test.Utilities.downloadH2Jar("h2-<version>.jar", "<SHA-256 HEX文字列>");
```

## その他

このプロジェクトをテストするときは、config.jsonを用意します。  
パスなどが通っていればいりません。  

```json
{
	"JAVA_HOME" : "/path/to/JDK/",
	"H2Path" : "h2-<version>.jar",
	"H2Hash" : "h2-<version>.jarのSHA-256の16進数表現"
}
```
sbcli-fix-logger 0.0.5
CLI for managing SimplyBlock cluster


# SBCLI (SimplyBlock CLI)

Simplyblock provides a feature-rich CLI (command line interface) to deploy SimplyBlock Cluster and to manage the SimplyBlock Control plane

## Install

The CLI can be installed using pypi package [sbctl](https://pypi.org/project/sbctl/)

```
pip install --upgrade sbctl
```

# Components

## Simply Block Core
Contains core logic and controllers for the simplyblock cluster

## Simply Block CLI
The list of all the available CLI options can be here [CLI docs](./simplyblock_cli/README.md)

## SimplyBlock Web API

Web app that provides API to manage the cluster. More about this in [README.md](./simplyblock_web/README.md)


### local development

FoundationDB requires a client library (libfdb_c.dylib) for the Python bindings to interact with the database.
Depending on the OS architecture, please install the appropriate version from the official github repo

```
wget https://github.com/apple/foundationdb/releases/download/7.3.3/FoundationDB-7.3.3_arm64.pkg
```
setup the code on a management node and the webApp code can be developed by building the `docker-compose-dev.yml` file.

```
sudo docker compose -f docker-compose-dev.yml up --build -d
```