semvec/pypi-packages huggingface dataset:
gptop 0.0.7
Handles communication with GPTs
Requires Python: >=3.6
Keywords: gptop GPT operator llm ai

<div align="center">
  <img  src="https://user-images.githubusercontent.com/33267791/233712514-b47aabb4-1821-4f67-8214-33d6fe2d6402.png" alt="GPTOP Logo" />
</div>

# GPT Operator

[![PyPI version](https://badge.fury.io/py/gptop.svg)](https://badge.fury.io/py/gptop)

**Enhance your GPT applications with external operations**

GPT Operator (`gptop`) serves as a [call operator](https://en.wikipedia.org/wiki/Operator_assistance) for your application. Simply provide `gptop` with a prompt, and it will determine the required external operation to fulfill the prompt and, if desired, execute that operation on your behalf.

## Features

* Execute operations based on a given prompt
* Create, update, and remove operations in vector database

## Requirements

* Pinecone Vector Database Index
* OpenAI access to gpt-4

## Installation

Install the released version via pip:

```bash
$ pip install gptop
```

[WARNING] The package is currently in Alpha stage, so please exercise caution before using it in production.

## Repository Setup

If you don’t have Python installed, [download it here](https://www.python.org/downloads/).

1. Clone this repository.

2. Navigate to the project directory:

   ```bash
   $ cd gpt-operator
   ```

3. Create a new virtual environment:

   ```bash
   $ virtualenv virt
   $ source virt/bin/activate
   ```

4. Install the requirements:

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

5. Make a copy of the example environment variables file:

   ```bash
   $ cp .env.example .env
   ```

6. Add your [OpenAI API key](https://beta.openai.com/account/api-keys) to the newly created `.env` file.

7. Add your [Pinecone API key and Region](https://docs.pinecone.io/docs/quickstart) to the newly created `.env` file.

## CLI

1. Run the application:

    ```bash
    $ ./run.sh
    ```

To set up an operation on your machine, please see the [example](https://github.com/ncrews35/gpt-operator/tree/mainline/example).

## Contributing

We are actively looking for new contributors to help improve and expand the GPT Operator repository. All contributions are welcome! Please feel free to reach out for more information on how you can contribute.
fancybbox 0.0.12
Fancy Bounding Box - Rectangle for Object Detection
Keywords: python,image processing,bbox,bbox regtangle,fancy rectangle,bounding box

# Fancybbox
[![PyPi version](https://img.shields.io/pypi/v/fancybbox)](https://pypi.org/project/fancybbox/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

A package that allows to build Lib is a package that enables users to create visually appealing bounding boxes for object detection in image processing deep learning projects.  
With a current version of 0.0.10, the package is constantly being updated and improved. The package offers a variety of features, including the ability to customize the appearance of bounding boxes with different colors, line widths, and styles. 
Additionally, it provides users with the flexibility to adjust the size and shape of the bounding boxes to meet their specific needs.'


Developed by Prashant Verma (c) 2023

This project still very much experimental and may change significantly. 

## Install
Install with all dependencies:

```bash
pip install fancybbox
```

## Examples of How To Use (Alpha Version)

Creating A Sample detection function

```python
import cv2
from fancy_bbox import FancyBox

# Load an image
image = cv2.imread("images/img_1.jpg")

# Detect an object and get its bounding box coordinates
x, y, w, h = [100, 100, 200, 200]

# Create a FancyBox object and draw it on the image
fancy_box = FancyBox(x, y, w, h, border_thickness=1, border_color=(0, 255, 0))
image_with_box = fancy_box.target_angle_bbox(image)


# Display the image with the box
cv2.imshow("Fancy Box Example", image_with_box)
cv2.waitKey(0)
```