semvec/pypi-packages huggingface dataset:
askyai 0.1.0
Libary for Making Ai
Requires Python: >=3.7


# skai

**skai** is a simple and easy-to-use AI library for Python.  
It allows you to interact with AI models with minimal code.

## Installation

You can install `skai` directly from PyPI:

```bash
pip install skai

Make sure you have Python 3.7 or higher installed.

Install Dependencies

skai depends on the openai library. It will be installed automatically with pip.
If needed, you can install it manually:

pip install openai


---

Setup

Before using the library, set your API key and choose a model:


import skai

# Set your API key
skai.setapi("YOUR_API_KEY")

# Set the AI model (examples: "gpt-3.5-turbo", "gpt-4")
skai.setmodel("gpt-4")

---

Usage

Ask AI

# Ask a question
user_input = skai.askai(input("You: "))

Print AI Response

# Display AI response
skai.awnser("bot: ", user_input)


---

Example Script

import skai

skai.setapi("YOUR_API_KEY")
skai.setmodel("gpt-4")

question = skai.askai(input("You: "))
skai.awnser("bot: ", question)

Sample Run:

You: What is the capital of France?
bot: Paris


---

License

This project is licensed under the MIT License.
ezioaltair 0.1.2
A simple, opinionated wrapper for Altair visualizations.
Requires Python: >=3.10
Keywords: altair data-visualization plotting wrapper



# ezio (just an altair wrapper)

> I will dedicate my life to make people stop using plotly.
>
> -- <cite>Benjamin Franklin</cite>


The idea behind this package is quite simple: create a wrapper of [altair](https://altair-viz.github.io/) similar to what [plotly-express](https://plotly.com/python/plotly-express/) did for plotly.

My hope is to remove friction while creating simple plots. Big focus on interaction between subplots that I think is the killing feature of `altair`.

The motto of this packae is modularity. No fuctions with 1 billion parameters (I'm looking at you plotly) but rather multiple methods to concatenate at will.



## Demo

In this marimo you will find few examples.

Here a tiny demo:


```
import polars as pl
import ezioaltair as ez
import numpy as np
from datetime import date

N = 100
data = pl.DataFrame({
    'time': pl.date_range(start=date(2025, 1, 1), 
                          end=date(2025, 1, 1) + pl.duration(days=N-1), 
                          interval='1d',
                         eager=True),
    'var1': (np.random.randn(N) + np.linspace(0, 10, N)).tolist(),
    'var2': (np.random.randn(N)*0.2 + np.sin(np.linspace(0, 10, N))).tolist(),
})

(
    ez.LineChart(data=data, 
                    x='time', 
                    y=['var1', 'var2'],
                   colors=['red', 'black'], ytitle='y axis title', xtitle='x axis')
    .add_view(y=['var1'])
    .add_scatter(x='var1', y='var2', highlight_color='green', color='blue')
    .add_title('This is a title')
    .render(height=300, width=500)
)
```
github-mcp-server-unofficial 0.12.1
GitHub's official MCP Server packaged unofficially for Python
Requires Python: >=3.9
Keywords: MCP, github

# github-mcp-server-unofficial

Packages https://github.com/github/github-mcp-server as wheels.

## Usage

This package can be installed using:

```bash
pip install github-mcp-server-unofficial
```

If you are invoking it from a Python program, you can get a `Path` to this
binary using:

```python
import subprocess

from github_mcp_server import find_github_mcp_server_bin

subprocess.run([find_github_mcp_server_bin()])
```