semvec/pypi-packages huggingface dataset:
llm-code-review 0.1.1
Command line tool to code review GitHub pull requests with LLMs.
Requires Python: >=3.10

# LLM Code Review

## Overview

`llm-code-review` is a command-line tool for automated code reviews of GitHub pull requests using OpenAI's GPT models or local models via Ollama. It fetches modified files, extracts diffs, retrieves full file contents for context, and generates concise, constructive code reviews.

## Features
- Supports OpenAI API (`gpt-4o-mini`, etc.)
- Supports local models via Ollama
- Fetches diffs from GitHub pull requests
- Retrieves full file contents for context
- Provides AI-generated feedback with before/after code snippets
- Filters reviews to focus only on Python files

## Installation

Install the package from PyPI:
```sh
pip install llm-code-review
```

## Prerequisites
Before using the tool, you must set up the required API keys as environment variables:

- **GitHub Token**: To authenticate API requests, create a GitHub personal access token (with `repo` scope for private repositories) and set it as an environment variable.
- **OpenAI API Key**: Obtain an API key from OpenAI and set it as an environment variable.

### Setting Up Environment Variables
Add the following lines to your shell profile (e.g., `~/.bashrc`, `~/.zshrc`):

```sh
export GITHUB_TOKEN="your_github_token"
export OPENAI_API_KEY="your_openai_api_key"
```

Then, reload your shell profile:

```sh
source ~/.bashrc  # or source ~/.zshrc
```

Alternatively, you can store them in a `.env` file and use `python-dotenv` to load them.

## Usage

### Basic Command

To review a GitHub pull request:
```sh
llm-code-review owner/repository PR_NUMBER
```

Example:
```sh
llm-code-review octocat/hello-world 42
```

### Using a Specific Model

#### OpenAI
```sh
llm-code-review octocat/hello-world 42 --model openai:gpt-4o-mini
```

#### Ollama (local model)
```sh
llm-code-review octocat/Hello-World 42 --model ollama:deepseek-r1:8b
```
This command fetches the pull request #42 from the `octocat/Hello-World` repository, retrieves modified Python files, and reviews the changes using OpenAI.

## How It Works
1. Fetches pull request details from GitHub.
2. Retrieves the full content of modified Python files.
3. Sends the diff and full file content to OpenAI for review.
4. Displays feedback, focusing on potential improvements and critical issues.

## Notes
- Only Python (`.py`) files are reviewed.
- The review focuses on changes in the PR while using the full file context.
- If a file has more than three issues, only the most important one is highlighted unless critical issues are found.

## License
MIT License

## Contributions
Feel free to open issues or submit pull requests!
RahkaranAPI 0.0.1
Rahkaran Authentication Client
Requires Python: >=3.6


# Rahkaran Authentication Client

A Python client for handling Rahkaran authentication sessions.

## Installation

---
```bash
pip install RahkaranAPI
```
---

## Usage

---
```python
from RahkaranAPI import RahkaranAPI

# Initialize client
SG = RahkaranAPI(
    server_name="127.0.0.1",
    port="80",
    username="admin",
    password="admin",
    rahkaran_name="DEV",
    protocol="http"
)

# Get session
try:
    session = auth_client.login()
    print(f"Authenticated session: {session}")
except Exception as e:
    print(f"Authentication failed: {str(e)}")
```
---

---
```go
r = RahkaranAPI("DEV")

print(
    r._send_get(
        "/General/AddressManagement/Services/AddressManagementWebService.svc/GetRegionalDivisionList"
    )
)
data = [{"Type ": 1, "FirstName": "Ehsan", "LastName": "Rezaei"}]
print(
    r._send_post(
        "/General/PartyManagement/Services/PartyService.svc/GenerateParty",
    data,
    )
)

```
---
cometa-git-tools 0.1.13
Git tools for Commitizen and PR Summary Generation using AI
Requires Python: >=3.7
Keywords: git commitizen pr-summary ai gemini conventional-commits

# Cometa Git Tools

Una colección de herramientas Git para mejorar el flujo de trabajo de desarrollo, incluyendo un generador de mensajes de commit basado en AI y un generador/creador de Pull Requests.

## Características

- 🤖 **AI Conventional Commits**: Genera mensajes de commit siguiendo la convención de Conventional Commits usando Google Gemini AI
- 📝 **PR Manager**: Genera y crea Pull Requests automáticamente con resúmenes inteligentes
- 🎨 **Interfaz Amigable**: Interfaz de línea de comandos intuitiva y colorida
- 🔧 **Fácil Configuración**: Comando `cz-setup` para configurar todo automáticamente

## Instalación

### Usando pip

```bash
pip install cometa-git-tools
```

### Instalación desde el código fuente ss

```bash
git clone https://github.com/cometa/cometa-git-tools.git
cd cometa-git-tools
pip install -e .
```

### Configuración

1. Configura tu API key de Google Gemini:
```bash
export GEMINI_API_KEY='your-api-key'
```

2. (Opcional) Agrega la variable a tu archivo .bashrc o .zshrc para hacerla permanente:
```bash
echo 'export GEMINI_API_KEY="your-api-key"' >> ~/.zshrc  # o ~/.bashrc
```

3. Configura Commitizen para usar el plugin AI:
```bash
# Configuración rápida (recomendado)
cz-setup

# Opciones avanzadas:
cz-setup --help         # Ver todas las opciones disponibles
cz-setup --no-global   # Solo configurar el proyecto actual
cz-setup --no-project  # Solo configurar globalmente
```

### Archivos de Configuración

El comando `cz-setup` creará/modificará dos archivos:

1. **~/.commitizen/config.toml**: Configuración global de commitizen
2. **./pyproject.toml**: Configuración local del proyecto

## Uso

### Commitizen AI

Este plugin extiende Commitizen para generar mensajes de commit usando AI:

```bash
# Agrega tus cambios
git add .

# Genera un mensaje de commit con AI
cz commit
```

### PR Summary Generator

Herramienta completa para gestionar Pull Requests:

```bash
# Genera un resumen del PR actual para poder visualizarlo en el editor de texto
pr-summary generate

# Crea un nuevo PR con resumen automático
pr-summary create

# Crea un PR especificando la rama base
pr-summary create --base main

# Ver todas las opciones disponibles
pr-summary --help
```

La herramienta permite:
- 📝 Generar resúmenes detallados de los cambios
- ✨ Crear PRs directamente desde la línea de comandos
- 🔄 Especificar la rama base del PR
- 🤖 Generar títulos y descripciones usando AI

## Licencia

MIT License - ver [LICENSE](LICENSE) para más detalles.

## Contribuir

Las contribuciones son bienvenidas! Por favor, lee [CONTRIBUTING.md](CONTRIBUTING.md) para detalles sobre nuestro código de conducta y el proceso para enviar pull requests.

## Soporte

Si encuentras algún problema o tienes una sugerencia, por favor crea un issue en el [repositorio de GitHub](https://github.com/cometa/cometa-git-tools/issues).
academicjobscraper 0.1.0
A package to scrape and filter academic job listings
Requires Python: >=3.6
Keywords: academic jobs, job scraping, web scraping

# AcademicJobScraper

A Python package for scraping and filtering academic job listings from academicjobsonline.org.

## Installation

```bash
pip install academicjobscraper
```

## Usage

### As a Python Module

```python
from academicjobscraper import AcademicJobScraper

# Initialize the scraper with required keywords and optional file names
scraper = AcademicJobScraper(
    keywords=["machine learning", "deep learning", "AI"],  # Required
    links_file="job_links.csv",      # Optional (default: job_links.csv)
    data_file="jobs_data.json",      # Optional (default: jobs_data.json)
    results_file="relevant_jobs.csv"  # Optional (default: relevant_jobs.csv)
)

# Start scraping with a mother link
scraper.scrape("https://academicjobsonline.org/your-search-url")
```

### Command Line Interface

```bash
# Basic usage with required parameters
academicjobscraper "https://academicjobsonline.org/your-search-url" "machine learning" "deep learning" "AI"

# With optional file name parameters
academicjobscraper "https://academicjobsonline.org/your-search-url" \
    "machine learning" "deep learning" "AI" \
    --links-file custom_links.csv \
    --data-file custom_data.json \
    --results-file custom_results.csv
```

## Features

- Scrapes job listings from academicjobsonline.org
- Extracts detailed job information
- Filters jobs based on provided keywords
- Customizable output file names
- Progress tracking during scraping

## Output Files

The scraper generates three files:

1. `job_links.csv` - Contains all scraped job URLs
2. `jobs_data.json` - Contains detailed information for all jobs
3. `relevant_jobs.csv` - Contains filtered jobs matching the keywords

## License

This project is licensed under the MIT License - see the LICENSE file for details.
calorimetry-analysis 0.1.2
Analyze calorimetry temperature changes with GUI in Jupyter
Requires Python: >=3.9

# Calorimetry Analysis
## Introduction
This tool provides a graphical user interface if used in Jupyter for 
analyzing temperature versus time data sets from calorimetry experiments. 
The tool estimates the impact of heat leakage and stirring by fitting lines 
to the regions immediately before and after the temperature change. See the 
documentation (TODO) for details.

## Installation
```commandline
pip install calorimetry-analysis
```