semvec/pypi-packages huggingface dataset:
graphUtilscd-harshita 1.0.0
graphUtils - A Python package providing graph algorithms such as Dijkstra, Bellman-Ford, Floyd-Warshall, and Topological Sorting.

# graphUtilscd_harshita

`graphUtilscd_harshita` is a Python library that provides essential graph algorithms such as Dijkstra, Bellman-Ford, Floyd-Warshall, and Topological Sort.

## Installation

Install the package via pip:

```bash
pip install graphUtilscd_harshita
commitcrafter 0.3.2
A CLI tool to generate commit messages based on git diffs using AI
Requires Python: <4.0,>=3.12


![demo](https://github.com/user-attachments/assets/62ca9c47-e35a-4c2e-bbc0-0d19ee8b4df3)

# CommitCrafter 🎨

Craft your commit messages while you focus on coding. CommitCrafter analyzes your git diff and generates meaningful, conventional commit messages.

## ✨ What's Cool

- 🤖 **Multiple AI Brains**: Choose your favorite - GPT, Claude, Gemini, Mistral, or Ollama
- 🎯 **Smart Selection**: Pick from multiple suggestions for the perfect commit message  
- 📋 **Auto-Copy**: Selected message goes straight to your clipboard
- 🎭 **Conventional Commits**: Proper formatting with emojis included
- 🔌 **Just Works**: Zero config needed - works with any git repo

## 🚀 Quick Start

```bash
# Install with pipx
pipx install commitcrafter

# Use it (in any git repository)
commitcraft         # Uses Claude by default
commitcraft -a gpt  # Or use GPT instead
```

## 🎮 Choose Your Model

```bash
commitcraft -a claude   # Anthropic's Claude
commitcraft -a gpt      # OpenAI's GPT
commitcraft -a gemini   # Google's Gemini
commitcraft -a mistral  # Mistral AI
commitcraft -a ollama   # Local Ollama
```

## 🔑 Setup

You'll need an API key for your chosen model. Set it as an environment variable:

```bash
# Pick the one you need:
export ANTHROPIC_API_KEY='your-key'
export OPENAI_API_KEY='your-key'
export GEMINI_API_KEY='your-key'
export MISTRAL_API_KEY='your-key'
export OLLAMA_API_KEY='your-key'
```

## 📦 Requirements

- Python 3.12+
- A git repository
- An API key for your chosen model

## 🤝 Contributing

Found a bug? Have an idea? PRs and issues are always welcome!

## 📝 License

MIT - Do whatever you want with it!

## 🔗 Useful Links

### 🛠️ Built With
- [Pydantic-AI](https://github.com/pydantic/pydantic-ai) - Modern AI Agent Framework
- [Typer](https://typer.tiangolo.com/) - Beautiful CLI Builder
- [Rich](https://github.com/Textualize/rich) - Rich Terminal Output
- [GitPython](https://github.com/gitpython-developers/GitPython) - Git Operations
- [Pyperclip](https://github.com/asweigart/pyperclip) - Clipboard Management

### 🤖 AI Providers
- [Anthropic Claude](https://www.anthropic.com/) - Our Default Model
- [OpenAI](https://openai.com/) - GPT Integration
- [Google Gemini](https://gemini.google.com/) - Google's LLM
- [Mistral AI](https://mistral.ai/) - Open Source Models
- [Ollama](https://github.com/jmorganca/ollama) - Local AI Runtime
TOPSIS-Dishav-102217004 0.6
A Python package for implementing the TOPSIS decision-making method.
Requires Python: >=3.6

ï»¿# **TOPSIS - Dishav_Singla-102217004**

A Python implementation of the **TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)** method, designed for decision-making in multi-criteria decision analysis (MCDA).

---

## **Project Description**

This project implements the **TOPSIS** technique, a popular method used in decision-making that evaluates multiple alternatives based on various criteria. The project is focused on applying **TOPSIS** to a given dataset with multiple alternatives and criteria. The objective is to rank and identify the best alternative by calculating the **relative closeness** of each alternative to the ideal solution.

### **Objective**

- To apply **TOPSIS** on a sample dataset.
- To rank different alternatives based on defined criteria.
- To generate a ranking list of the alternatives for decision-making purposes.

---

## **Features**

- **Data Preprocessing**: The dataset is cleaned and normalized.
- **TOPSIS Algorithm**: Implementation of the **TOPSIS** method, including the calculation of:
  - Ideal and negative-ideal solutions.
  - Separation measures.
  - Relative closeness to the ideal solution.
- **Ranking**: Rank the alternatives based on the relative closeness.
- **Visualizations**: Plot results (optional, if applicable).
  
---

## **Installation**

### Prerequisites

Ensure you have **Python 3.x** installed. You can download it from the official website:

- [Python Official Website](https://www.python.org/downloads/)

### Install Dependencies

Clone this repository and install the necessary dependencies using **pip**:

```bash
git clone https://github.com/DISHAVSINGLA/TOPSIS-Dishav_Singla-102217004.git
cd TOPSIS-Dishav_Singla-102217004
pip install -r requirements.txt
```

### Install Library

```bash
pip install TOPSIS-DishavSingla-102217004
```