semvec/pypi-packages huggingface dataset:
means-testing 0.0.5
A simple library to compare the means of two or more independent samples and check for statistical differences.
Requires Python: >=3.11

# Means-Testing
A simple library to compare the means of two or more *independent* samples and check for statistical differences.

## Installation
```
pip install means-testing
```

## Basic usage
```python
from means_testing import MeansTester

# Initialize a MeansTester, that takes in samples and tests for statistical differences
StatToolbox = MeansTester(*samples)

# Check if means are significantly different
StatToolbox.test_means()
```

After carrying out the statistical test, one can determine if the samples passed are statistically different or not via the property `SignificantDifference` as shown below.
```python
StatToolbox.SignificantDifference
> True (if samples are statistically different) or
> False (if samples are NOT statistically different)
```

For more examples check out the [examples](https://github.com/RenZhen95/means-testing/tree/master/examples).

## Citation
The functions and statistical tests here are impelemented based on the book by Riffenburgh, *Statistics in Medicine*:


Riffenburgh R H. Statistics in Medicine. Elsevier/Academic Press, Amsterdam, Netherlands; 2012
GUILab 0.1.0
A lightweight Python GUI library for building applications quickly.
Requires Python: >=3.7
Keywords: gui, python, ttkbootstrap, tkinter, GUILab

\# GUILab



\*\*GUILab\*\* is a Python GUI library built on top of \[ttkbootstrap](https://ttkbootstrap.readthedocs.io/) and `tkinter`.  

It provides an easy way to create modern GUI applications with widgets, media support, and more — all in a single module.



---



\## \*\*Features\*\*



\- Create windows with dark/light themes

\- Buttons, Labels, TextBoxes, CheckBoxes, RadioButtons

\- Sliders, ProgressBars, Meters, Frames, Tabs, Dropdowns, Datepickers, SpinBoxes

\- MenuBar and Toolbar

\- Table/Treeview support

\- Canvas for shapes (circle, rectangle, line, oval, polygon) and images

\- Media support: image, audio, and video embedding



---



\## \*\*Installation\*\*



Install directly from PyPI:



```bash

pip install guilab
rag-pipeline-lib 0.1.1
Modüler, hibrit (dense + BM25) RAG pipeline kütüphanesi
Requires Python: >=3.9
Keywords: rag, retrieval, llm, nlp, vector, bm25, hybrid

# RAG Pipeline

RAG Pipeline, Retrieval-Augmented Generation (RAG) mimarisi ile metin tabanlı arama ve yanıt üretme işlemlerini kolaylaştıran bir Python kütüphanesidir.  
Bu kütüphane ile belgelerinizi indeksleyebilir, FAISS veya diğer vektör veri tabanlarıyla arama yapabilir ve LLM modelleri ile entegre edebilirsiniz.

---

## 📦 Kurulum

PyPI üzerinden:
```bash
pip install rag-pipeline-lib
```

Yerel geliştirme modu (kaynak kodu değiştirip test etmek için):
```bash
git clone https://github.com/kullaniciadi/rag_pipeline.git
cd rag_pipeline
pip install -e .
```

---

## 🚀 Quickstart

```python
from rag_pipeline import RAGPipeline, FixedSizeChunking
from rag_pipeline.document_loaders import PDFLoader
from rag_pipeline.embeddings import OllamaEmbeddings
from rag_pipeline.llms import OllamaLLM
from rag_pipeline.vector_stores import FAISSVectorStore

# Bileşenleri başlat
embedding = OllamaEmbeddings("nomic-embed-text:latest", base_url="http://ollama:11434")
vector_store = FAISSVectorStore(collection_name="my_collection", dimension=768)
llm = OllamaLLM("llama3.2:3b", base_url="http://ollama:11434")
chunking = FixedSizeChunking(chunk_size=450, overlap=100)

# Pipeline oluştur
rag = RAGPipeline(vector_store, embedding, llm, chunking)

# Belgeleri yükle
docs = PDFLoader.load_folder("./documents")
rag.add_documents(docs)

# Sorgu yap
print(rag.query("What are the feature methods used in cattle identification?"))
```

---

##  Proje Yapısı

```
rag_pipeline/
│
├── rag_pipeline/
│   ├── __init__.py
│   ├── pipeline.py
│   ├── chunking/
│   ├── document_loaders/
│   ├── embeddings/
│   ├── llms/
│   ├── vector_stores/
│   └── retrievers/
│
├── tests/
│
├── setup.py
├── pyproject.toml
├── LICENSE
└── README.md
```

---

##  Lisans

Bu proje **MIT Lisansı** ile lisanslanmıştır.  
Tüm detaylar için [LICENSE](LICENSE) dosyasına bakabilirsiniz.

```
MIT License

Copyright (c) 2025 İsim

Permission is hereby granted, free of charge, to any person obtaining a copy...
```

---

## Özellikler

- FAISS ve diğer vektör veritabanı desteği
- Hibrit retrieval desteği (BM25 + vektör arama)
- Modüler yapı
- Geliştirici dostu API

---

##  Katkıda Bulunma

1. Bu projeyi forklayın
2. Yeni bir branch oluşturun (`git checkout -b feature/ozellik`)
3. Değişikliklerinizi commit edin (`git commit -m 'Yeni özellik eklendi'`)
4. Branch’inizi push edin (`git push origin feature/ozellik`)
5. Pull Request oluşturun

---

## İletişim

Sorularınız veya önerileriniz için: **ornek@email.com**
NPhish 0.2.1
Ultimate phishing tool in python. Includes popular websites like facebook, twitter, instagram, github, reddit, gmail and many others.

<h1 align="center">NPhish</h1>

### [+] Description :

***Ultimate phishing tool in python. Includes popular websites like facebook, twitter, instagram, github, reddit, gmail and many others.***

### [+] Installation

##### Install primary dependencies (git and python)

 - For Debian
    - ```sudo apt install git python -y```
 - For Arch
    - ```sudo pacman -S git python --noconfirm```
 - For Fedora
    - ```sudo yum install git python -y```
 - For Termux
    - ```pkg install git python -y```

##### Install this tool

 - ```pip install NPhish```

##### Run the tool
 - ```NPhish```

#### Or, directly run
```
pip install NPhish && NPhish

```

### Features:

 - Multi platform (Supports most linux)
 - 65 Website templates for 50+ sites.
 - Dual Tunneling (Ngrok and Cloudflared)
 - Easy to use
 - Possible error diagnoser
 - Built-in masking of url
 - Custom masking of url
 - Portable file (Can be run from any directory)
 - Get IP Address and many other details along with login credentials

### Requirements

 - `Active Internet Connection`
 - `Python(3)`
 - `PHP`
 - `Curl`
 - `Unzip`
 - `Wget`
 
If not found, all of the required packages will be installed on first run

#### Tested on

 - `Termux`
 - `Kali-Linux`

## Usage

1. Run the Tool
2. Choose a Website
3. Wait sometimes for setting up all
4. Send the generated link to victim
5. Wait for victim login. As soon as he/she logs in, credentials will be captured.

## [+] Disclaimer
***This tool is developed for educational purposes. Here it demonstrates how phishing works. If anybody wants to gain unauthorized access to someones social media, he/she may try out this at his/her own risk. You have your own responsibilities and you are liable to any damage or violation of laws by this tool. The author is not responsible for any misuse of NPhish!***

### This repository is open source to help others. So if you wish to copy, consider giving credit !!!

####  If this tool helped you, consider staring repository. Your stars encourage me a lot !!!