semvec/pypi-packages huggingface dataset:
lifx-photons-arranger 0.9.3
A web interface for changing the user co-ordinates of LIFX tiles
Requires Python: >=3.12
Keywords: arranger, lifx, photons

Photons Tile Arranger
=====================

This is a web UI for arranging the positions of a number of LIFX Tiles relative
to each other.

It is build on top of `Photons <https://photons.delfick.com>`_ and once
installed can be run with::

    lifx lan:arrange

Doing this will start a server and then open your web browser to show a web UI
for arranging the tiles. The tiles will each be given a different pattern with
a white line at the bottom of the tile.

Clicking on a panel will highlight that panel in the web interface and on the
tile itself.

Moving a tile will change the ``user_x/user_y`` properties on the tile and make
the tile glow.

You can have multiple tabs open to the server and each tab will be in sync with
each other. Once all tabs are closed, the tiles will return to the colours they
had before the arrange was started.

This can be installed with::

    pip3 -m pip install lifx-photons-arranger
aiagentic 0.0.1
AI Agent Prompt Management Tool
Requires Python: >=3.8

# AIAgentic

An advanced Agentic AI Prompt Engineering and Management Tool, focusing on efficient prompt management, asynchronous processing, and stream parsing.

🚧 **Project Status: Under Development** 🚧

This project is currently in active development and testing on TestPyPI. The official PyPI release will be available soon.

## Features (Coming Soon)

- **Prompt File Management**
  - Reliable prompt file format validation and parsing
  - Support for prompt version control and variable injection
  - Decoupling of engineering framework and prompt modules

- **Stream Result Parsing**
  - Real-time processing of AI text streams
  - Incremental parsing of JSON structures
  - Flexible callback mechanisms

- **Async Request Management**
  - Support for multiple API node async requests
  - Built-in dynamic load balancing
  - Simple configuration interface

## Installation

Currently in testing phase. You can install the test version via:

```bash
pip install --index-url https://test.pypi.org/simple/ aiagentic
```

Official PyPI release coming soon!

## Quick Start

Coming soon with the first stable release.

## Roadmap

- [x] Project initialization
- [x] Basic project structure
- [x] Core functionality implementation
- [ ] Test coverage
- [ ] Documentation
- [ ] First stable release

## Development Status

- Current Version: 0.0.1 (Pre-alpha)
- Test Phase: Active on TestPyPI
- Planned First Stable Release: Q1 2025

## Contributing

This project is in its early stages. Contributions will be welcome once we establish our contribution guidelines.

## License

BSD License - See LICENSE file for details

## Contact

Project maintained by Will Leung (<786121272@qq.com>)
webStreamViewer 1.0.0
opencv-style image display that displays the image/stream on a local webserver instead of a screen.
Requires Python: >=3.6

# README for Web Viewer

## Introduction
The WebVideoViewer is a Python application that allows you to create subpages with live video streams from a webcam or other source. It uses the Flask framework to handle web requests and displays video frames in real-time.

## Requirements
- Python 3.x
- OpenCV (cv2) library
- Flask library

## Installation
1. Clone the repository or download the code files.
2. Install the required dependencies by running the following command:
   ```
   pip install opencv-python flask
   ```

## Usage
1. Import the necessary modules:
   ```python
   import webViewer
   ```

2. Create an instance of the `VideoStream` class:
   ```python
   app = webViewer.VideoStream()
   ```

3. Start the Flask web server on a specified port (default is 80):
   ```python
   app.run()
   ```

4. Continuously capture frames from the webcam and update subpages:
   ```python
   cap = cv2.VideoCapture(0)
   
   while True:
       _, frame = cap.read()
       gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
       app.imshow("frame", frame)
       app.imshow("gray", gray)
   ```

5. Access the subpages:
   - The home page lists all the available subpages. Visit the root URL (e.g., `http://localhost:80/`) to see the home page.
   - Each subpage displays a live video stream. Add the subpage name to the root URL (e.g., `http://localhost:80/video1`) to access a specific subpage.

## Customization
You can customize the following aspects of the Flask Subpage App:

- **Templates**: The app uses HTML templates to render the home page and subpages. By default, it looks for the templates in the `templates` folder relative to the script's location. You can specify custom template paths when creating a `VideoStream` instance:
   ```python
   app = VideoStream(homePageTemplate="path/to/home.html", subpageTemplate="path/to/subpage.html")
   ```

- **Port**: The default port for the Flask web server is 80. If you want to use a different port, specify it when creating a `VideoStream` instance:
   ```python
   app = VideoStream(port=5000)
   ```


## License

This project is licensed under the [MIT License](https://opensource.org/licenses/MIT). Feel free to modify and use it according to your needs.

## Acknowledgments

-

 This application was inspired by the Flask video streaming tutorial from the [Official Flask Documentation](https://flask.palletsprojects.com/)
- OpenCV - Open Source Computer Vision Library: [https://opencv.org/](https://opencv.org/)
- Flask - A micro web framework written in Python: [https://flask.palletsprojects.com/](https://flask.palletsprojects.com/)