semvec/pypi-packages huggingface dataset:
cvpy-toolkit 0.1.0
Add your description here
Requires Python: >=3.11

Computer Vision for Python Toolkit

Welcome to `cvpy`, a Python library designed to simplify and streamline common computer vision tasks,
with a particular focus on segmentation.

## Features

### 1. **Image Segmentation Toolkit (`cvpy.imseg`)**
   The `cvpy.imseg` package provides robust tools for creating and manipulating:
   - **Bounding Boxes**: Easily define and adjust bounding boxes within images.
   - **Contours**: Tools to work with image contours, simplifying the process of identifying and working with object boundaries.

Within this package, you'll also find `cvpy.imseg.immasks`, which includes:
   - **Image Segmentation Masks**: Support for various representations of image segmentations (masks),
   enabling flexible and efficient manipulation of mask data. Including
    - **BinaryMask**
    - **SpareMask**
    - **CategoricalMask**
    - **CompoundMask**

### 2. **OpenCV Wrapper and Extender (`cvpy.cvw`)**
   The `cvpy.cvw` package acts as a wrapper and extender for OpenCV,
   adding new functionality and simplifying common OpenCV workflows,
   while maintaining compatibility with the broader OpenCV ecosystem.

   Highlights include
    - a simple GUI generator.
    - FileNotFound Errors for cv2.imread

## Installation
To install `cvpy`, simply run:
```bash
pip install cvpy-toolkit
```

## Getting Started
# TODO(add some examples)

## Contributing
We welcome contributions to `cvpy`. If you'd like to contribute, please fork the repository, make your changes, and submit a pull request.

---

Let me know if you'd like any changes or additions!
WebAssistant 0.0.4
WebAssistant: Quickly Deploy an AI Assistant Chatbot for your Personal Webpage
Keywords: ai chatbot website-builder gpt-4 rag

# WebAssistant

Easily deploy an AI assistant chatbot for your personal website.

This repository contains a Python library and the template for a React app, both of which use state-of-the-art Retrieval Augmented Generation (RAG) prompting methodologies.

## Prerequisites
1. Make sure you have an OpenAI API key
2. Have the base URL of the website for which you want to create the chatbot

## Using the Python Library

To use the python library, first install with

```bash
pip install WebAssistant
```

Then, write

```python
from WebAssistant import *
myChatbot = Hub()
myChatbot.launch(YOUR_OPENAI_API_KEY, YOUR_WEBSITE)
```

This will create a link to a chatbot built with Gradio that can answer questions about your website.

If you're looking for a very basic (and not recommended) solution, simply iframe the link on your webpage. Note that the link will expire every 3 days and will need to be regenerated using ``launch()``

## Using the React App
For a more flexible solution, run the following Python code. Make sure you have node installed first.

```python
from WebAssistant import *
myChatbot = Hub()
myChatbot.deployReactExpressApp(YOUR_OPENAI_API_KEY, YOUR_WEBSITE, YOUR_ROOT_DIRECTORY)
```

This will deploy a React frontend + Express backend to ``localhost:8080``. From there, you can upload it to Cloudflare, Render.com, Fly.io, Railway, or other providers, and then iframe the link generated by those sites inside a dedicated container in your website for the chatbot.

Note that you must NOT remove the credits to this repository included at the bottom of the chatbot UI.

## Questions?
Feel free to send any questions you have through the issues tab on Github.
Custmr-segmentation 0.1.1
A customer segmentation package for preprocessing data
Requires Python: >=3.6

# Customer Segmentation Package

## Overview

This data analysis package provides comprehensive tools for preprocessing, feature engineering, clustering, and feature selection/reduction of data. It streamlines and automates common data analysis tasks, making it easier to prepare datasets for further analysis and machine learning. The package includes functionalities for validating data structure, handling missing values, removing outliers, scaling data, and much more.

## Features

- **Data Preprocessing**
  - **Data Structure Validation:** Ensures that the dataset meets expected structural requirements.
  - **Null Value Removal:** Identifies and removes or imputes missing values.
  - **Outlier Removal:** Detects and removes outliers from the dataset.
  - **Data Scaling:** Standardizes or normalizes data for consistent analysis.

- **Feature Engineering**
  - **RFM (Recency, Frequency, Monetary) Calculation:** Computes RFM metrics for customer segmentation and analysis.
  - **Velocity Calculation:** Measures the rate of change in data over time.
  - **Growth Calculation:** Computes the growth metrics across data points.

- **Feature Selection and Reduction**
  - **Information Gain Calculation:** Evaluates the importance of features in predicting target variables.
  - **WOE (Weight of Evidence) and IV (Information Value) Calculation:** Assesses the predictive power of categorical features.
  - **PCA (Principal Component Analysis):** Reduces dimensionality of data and allows for inverse transformation to original space.

- **Advanced Clustering**
  - **Best Clustering Method Selection:** Provides various clustering algorithms (e.g., KMeans, DBSCAN, EM, MeanShift, Agglomerative) and selects the most suitable one based on data characteristics.
  

## Requirements

To use this package, you need to have the following installed:

- Python 3.7 or higher
- The following Python libraries:
  - `pandas`
  - `numpy`
  - `scikit-learn`
  - `scipy`
  - `matplotlib`
  - `seaborn`
  - `statsmodels`

You can install these dependencies using:

```bash
pip install -r requirements.txt
```
To import the package,you can use the following format:
```bash
from customer_segmentation_clustering.main import main
```