semvec/pypi-packages huggingface dataset:
taskgpt 0.1.5
A CLI tool that helps you run GPT-based agents locally

# taskGPT

taskGPT is a Python-based command-line tool for running AI agents powered by OpenAI and Gemini models. It offers a guided setup experience, installs dependencies automatically, and configures API keys globally for seamless future use.

---

## Features

- Run GPT-based agents from your terminal with a simple command
- One-time setup for dependencies and API keys
- Supports both OpenAI and Gemini API keys
- Persists API keys to system environment (Windows Registry / shell config)
- Automatically detects setup and avoids redundant installations
- Docker support for isolated environments
- Installable via PyPI or GitHub

---

## Installation

### Option 1: Install via `pip`

You can install taskGPT from either PyPI or GitHub:

#### From PyPI

```bash
pip install taskgpt
```

#### From GitHub

```bash
pip install git+https://github.com/KetanHegde/taskGPT.git
```

---

### Option 2: Run using Docker

If you prefer using containers, taskGPT works great inside Docker.

#### Build the Docker image

```bash
docker build -t taskgpt .
```

#### Run the container


##### Unix/Linux

```bash
docker run -it --rm \
  -v "$PWD":/app \
  -w /app \
  -e OPENAI_API_KEY=your-openai-key \
  -e GEMINI_API_KEY=your-gemini-key \
  taskgpt
```


##### Windows CMD

```bash
docker run -it --rm ^
  -v "%cd%":/app ^
  -w /app ^
  -e OPENAI_API_KEY=your-openai-key ^
  -e GEMINI_API_KEY=your-gemini-key ^
  taskgpt
```


##### Windows Powershell

```bash
docker run -it --rm `
  -v "${PWD}:/app" `
  -w /app `
  -e OPENAI_API_KEY=your-openai-key `
  -e GEMINI_API_KEY=your-gemini-key `
  taskgpt
```


---

## Usage

Once installed, run the tool using:

```bash
taskgpt
```

### First Run

On the first launch, `taskgpt` will:

1. Install missing Python dependencies
2. Prompt you to enter `GEMINI_API_KEY` and/or `OPENAI_API_KEY` if not already set
3. Save them permanently in your system environment
4. Ask you to restart your terminal (only on first setup)

Subsequent runs will skip setup and launch the agent directly.

---

## Development

To contribute to this project:

### Clone the repository

```bash
git clone https://github.com/KetanHegde/taskGPT.git
cd taskGPT
```

### Install dependencies

```bash
pip install -e .
```

---
automacao-blaze 0.1.0
Automatização do site Blaze usando Selenium
Requires Python: >=3.7

# automacao-blaze

Automação do site Blaze.bet usando Selenium e WebDriver Manager.

## Instalação

```bash
pip install automacao-blaze
```

## Uso

```python
from automacao_blaze import AutomacaoBlazeChrome

bot = AutomacaoBlazeChrome("email", "senha", "https://blaze.bet.br/pt/games/roleta-brasileira")
```
django-logtailer 1.3.0
Allows to read log files from disk with a tail like web console on Django admin interface.
Requires Python: >=3.9

=================================
Django LogTailer
=================================

:Version: 1.3
:Source: http://github.com/fireantology/django-logtailer/


Allows the viewing of any log file entries in real time directly from the Django admin interface.
It allows you to filter on logs with regex and offer also a log clipboard for save desired log lines to the django db.

Demos
========
- Demo `Video`_

.. _`Video`: http://www.vimeo.com/28891014

Requirements
=============

- Django > 4.0
- Python 3.x
- Sessions enabled

Installation
============

- Install the package with pip install django-logtailer
- Add it to the INSTALLED_APPS in your SETTINGS
- add to urls.py: url(r'^logs/', include('logtailer.urls')),
- Run manage.py migrate for create the required tables
- Run manage.py collectstatic
crimeserieslinkage 1.1.0
Statistical methods for identifying serial crimes and related offenders
Requires Python: >=3.6

# crimeserieslinkage
Statistical methods for identifying serial crimes and related offenders

The module «crimeserieslinkage» for Python is designed to automate research related to the study of signs of serial crimes of various types, allowing to identify those crimes that are committed by the same person (group of persons), as well as to establish the most similar suspect, information about which has already existed in the database of persons previously committed similar crimes.

The functions from this module allow to solve the following problems: the establishment of signs that allow identifying serial (linkage) crimes and serial offenders; determine in the array of unsolved criminal cases those of them that are serial and committed by the same suspect; identification of the most likely suspect from among persons accounted for in the database of persons who have committed similar crimes.
evlog 0.1.7
A utility for logging daily events and tasks.
Requires Python: >=3.10

# evlog

This is a little utility I use for logging my daily activities and events. It is written in Python.

## Install

```bash
pipx install evlog
```

## Usage

To change the directory where evlogs are stored, set a shell environment variable EVLOG_DIR. To make this change permament, set the following in your shell configuration:

```bash
export EVLOG_DIR="/path/to/evlog/dir"
```

Otherwise, the default evlog directory will be `~/evlogs`.

To get started, add your first evlog entry! This will create a JSON file under your evlog directory for the day and ensure the evlog directory exists. E.g.:

```bash
evlog add -m "Started new evlog. Yay!"
```

```bash
usage: evlog [-h] [-v] {add,edit,rm,ls,lsfiles,search} ...

positional arguments:
  {add,edit,rm,ls,lsfiles,search}

options:
  -h, --help            show this help message and exit
  -v, --version         Print version information
```

### Example list output
![screenshot.png](/screenshot.png)
VereshchaginPy 0.1.0
Graph-based integration for structural mechanics using the Vereshchagin method
Requires Python: >=3.7

# VereshchaginPy

**VereshchaginPy** is a Python library for performing graph multiplication —
particularly useful in structural mechanics when applying the **flexibility method**.

The integration is based on the principle that the integral from two graphs can be expressed as:

∫ Mi(x) * Mj(x) dx = A * y


Where:
- `A` is the area under the first moment diagram `Mi(x)`.
- `y` is the corresponding ordinate (value) from the second moment diagram `Mj(x)`, evaluated at the centroid of the area `A`.
- `Mi`, `Mj` are continuous functions (e.g., moments) over a shared length `L`.

This library helps formalize that process by abstracting common shape-based 
moment diagrams (rectangles, triangles, trapezoids, parabolas) and automating their 
integration and visualization.

---

## Key Features

- Support for standard structural figure types:
  - `Rectangle`
  - `TriangleLeft`, `TriangleRight`
  - `Trapezoid`
  - `Parabola`
  - `ParabolicTrapezoid` (combining a parabola and trapezoid)
- Clean and extendable figure model
- Visualizer to plot and label diagrams
- Operator overloading for quick integration via `+`
- Strict input validation for type safety

---

## Installation

This is a pure Python module. You can simply fork and clone the repository:

```bash
git clone https://github.com/yourusername/VereshchaginPy.git
#\x00 \x00T\x00e\x00m\x00p\x00o\x00r\x00a\x00r\x00y\x00 \x00c\x00h\x00a\x00n\x00g\x00e\x00 \x00f\x00o\x00r\x00 \x00P\x00R\x00
\x00
\x00
starfruit 0.0.1
computer decor
Requires Python: >=3.11
Keywords: AI, menu bar, tamagotchi

# starfruit 

> like alien seeds that landed on earth,
> growing into strange plants.
> soon they may grow into trees, towering above us.
> some think they will bear fruit,
> a strange new abundance.
> for now, we admire their strange leaves.

try it:

```sh
$ pipx install starfruit
$ starfruit
```