semvec/pypi-packages huggingface dataset:
easyui-python 1.2.0
Easy Python GUI Designers and Libraries
Requires Python: >=3.6


🚀EasyUI-python
EasyUI is the most intuitive and lightweight library for Python Tkinter development. It is designed to save you from tedious coordinate calculations and cluttered widget configuration code by providing a visual designer and a fluent chaining API.

✨ Key Features
Visual Designer: Launch the EasyUI-designer from your terminal to design your UI with drag-and-drop ease.

Fluent Chaining API: Configure widgets in a single line: .set.pos(x, y).size(w, h).color(bg="blue").

Smart Form Validation: Built-in logic for mandatory fields using .required() and simple .submit() handlers.

Modern Enhancements: Supports Entry Placeholders, Image Drag-and-Drop, and interactive Hover Animations out of the box.

📦 Installation
You can install EasyUI directly from PyPI:

_______________________________________________________
pip install EasyUI-python
🛠 Using the Visual Designer
_______________________________________________________
After installation, you can launch the designer directly from your terminal to create and save styles:
_______________________________________________________

EasyUI-designer
_______________________________________________________
Design: Drag and drop widgets to your preferred locations.

Save: Save the layout as a style JSON file.

Apply: Use the style name in your code to instantly apply coordinates and aesthetics.

💻 Quick Start Example
The syntax is designed to be expressive, clean, and fast.

_______________________________________________________
from EasyUI import create

def on_success():
    name = name_input.text
    create.alert("Success", f"Welcome to EasyUI, {name}!")

# 1. Initialize Window
create.window("EasyUI Quickstart", "400x500")

# 2. Create and Place Widgets with Chaining API
create.label("Username").set.pos(50, 50).bold()

name_input = create.textbox().required()
name_input.set.pos(50, 80).size(300, 35)
name_input.set_placeholder("Enter your name here...")

# 3. Add Interactive Buttons
submit_btn = create.button("Submit")
submit_btn.set.pos(50, 150).size(300, 45).color(bg="#28a745", fg="white")
submit_btn.set.hover(bg="#218838") # Darker green on hover

# 4. Connect Logic via Form
form = create.form(name_input)
form.submit(submit_btn, on_success)

create.start()

_______________________________________________________
📄 License
This project is licensed under the MIT License.

🤝 Contributing
Contributions, issues, and feature requests are welcome! Feel free to check the issues page on our GitHub repository.
iflow-mcp-remyguercio-tailscale-mcp-echo 0.1.1
This MCP returns the identity of the currently logged in user.
Requires Python: >=3.11

# Identity Aware MCP Server for Tailscale

<img width="1125" alt="TailscaleMCPIdentityHero" src="https://github.com/user-attachments/assets/1e05e3a6-019d-4e16-b591-3691bcee16e6" />

Create an identiy aware MCP server that runs inside your private Tailscale network (Tailnet). This example leverages identity headers that are passed through to applications running behind `tailscale serve`.

Using this as starting point you can create MCP servers that are identity aware (with access to the logged in user's email) and can access internal APIs or services on thier behalf.

## Instructions

### Starting the Server

1. If you don't already have a Tailnet setup you'll need to [signup for one](https://tailscale.com).
2. Create an [API auth key](https://login.tailscale.com/admin/settings/keys) and save it into a `.env` file in the root of this project with the following format: `TS_AUTHKEY=tskey-auth-...`
3. With Docker already installed, run `docker compose up` to start the server.

This will spin up two containers. The MCP server and a Tailscale container running `tailscale serve` as a proxy to your tailnet.

### Using the Server

If you have an MCP Client that supports direct access to Streaming HTTP MCP servers, then you should be able to connect to the server by pointing it to `https://ts-mcp-echo.yourtailnetname.ts.net/mcp`.

#### Claude Desktop

Claude desktop does not currently support remote MCP servers (only stdio), but you can use the [mcp-remote](https://github.com/geelen/mcp-remote) tool (or any other proxy) to connect to it.

1. Install mcp-remote with `npm install -g mcp-remote`
2. Add the following configuration to your `claude_desktop_config.json` file:

    ```json
        {
            "mcpServers": {
                "tailscale-remote-echo-example": {
                    "command": "npx",
                    "args": [
                        "mcp-remote",
                        "https://ts-mcp-echo.yourtailnetname.ts.net/mcp"
                    ]
                }
            }
        }
    ```

    You can find your tailnet name by visiting the [Tailscale admin console DNS page](https://login.tailscale.com/admin/dns).
3. Restart Claude Desktop.
4. You should now see a new MCP server called `tailscale-remote-echo-example` with a `greet` tool.
5. Ask Claude `Who am I logged into my tailnet as?` allow the tool, and wait for the response!
pyclientutils 0.0.5
nicegui based Scholia
Requires Python: >=3.10

# pyclientutils
python MediaWiki Client Utilities

| | |
| :--- | :--- |
| **PyPi** | [![PyPI Status](https://img.shields.io/pypi/v/pyclientutils.svg)](https://pypi.python.org/pypi/pyclientutils/) [![License](https://img.shields.io/github/license/WolfgangFahl/pyclientutils.svg)](https://www.apache.org/licenses/LICENSE-2.0) [![pypi](https://img.shields.io/pypi/pyversions/pyclientutils)](https://pypi.org/project/pyclientutils/) [![format](https://img.shields.io/pypi/format/pyclientutils)](https://pypi.org/project/pyclientutils/) [![downloads](https://img.shields.io/pypi/dd/pyclientutils)](https://pypi.org/project/pyclientutils/) |
| **GitHub** | [![Github Actions Build](https://github.com/WolfgangFahl/pyclientutils/actions/workflows/build.yml/badge.svg)](https://github.com/WolfgangFahl/pyclientutils/actions/workflows/build.yml) [![Release](https://img.shields.io/github/v/release/WolfgangFahl/pyclientutils)](https://github.com/WolfgangFahl/pyclientutils/releases) [![Contributors](https://img.shields.io/github/contributors/WolfgangFahl/pyclientutils)](https://github.com/WolfgangFahl/pyclientutils/graphs/contributors) [![Last Commit](https://img.shields.io/github/last-commit/WolfgangFahl/pyclientutils)](https://github.com/WolfgangFahl/pyclientutils/commits/) [![GitHub issues](https://img.shields.io/github/issues/WolfgangFahl/pyclientutils.svg)](https://github.com/WolfgangFahl/pyclientutils/issues) [![GitHub closed issues](https://img.shields.io/github/issues-closed/WolfgangFahl/pyclientutils.svg)](https://github.com/WolfgangFahl/pyclientutils/issues/?q=is%3Aissue+is%3Aclosed) |
| **Code** | [![style-black](https://img.shields.io/badge/%20style-black-000000.svg)](https://github.com/psf/black) [![imports-isort](https://img.shields.io/badge/%20imports-isort-%231674b1)](https://pycqa.github.io/isort/) |
| **Docs** | [![API Docs](https://img.shields.io/badge/API-Documentation-blue)](https://WolfgangFahl.github.io/pyclientutils/) [![formatter-docformatter](https://img.shields.io/badge/%20formatter-docformatter-fedcba.svg)](https://github.com/PyCQA/docformatter) [![style-google](https://img.shields.io/badge/%20style-google-3666d6.svg)](https://google.github.io/styleguide/pyguide.html#s3.8-comments-and-docstrings) |

## Docs and Tutorials
[Wiki](https://wiki.bitplan.com/index.php/pyclientutils)
cobald-tardis 0.9.0
Transparent Adaptive Resource Dynamic Integration System
Requires Python: >=3.10
Keywords: asyncio tardis cloud scheduler

[![Build Status](https://github.com/MatterMiners/tardis/actions/workflows/unittests.yml/badge.svg)](https://github.com/MatterMiners/tardis/actions/workflows/unittests.yml)
[![Verification](https://github.com/MatterMiners/tardis/actions/workflows/verification.yml/badge.svg)](https://github.com/MatterMiners/tardis/actions/workflows/verification.yml)
[![Deployment Tests](https://github.com/MatterMiners/tardis/actions/workflows/deployment-tests.yml/badge.svg)](https://github.com/MatterMiners/tardis/actions/workflows/deployment-tests.yml)
[![codecov](https://codecov.io/gh/MatterMiners/tardis/branch/master/graph/badge.svg)](https://codecov.io/gh/MatterMiners/tardis)
[![Documentation Status](https://readthedocs.org/projects/cobald-tardis/badge/?version=latest)](https://cobald-tardis.readthedocs.io/en/latest/?badge=latest)
[![Development and Help Chat](https://badges.gitter.im/MatterMiners.png)](https://gitter.im/MatterMiners/community)
[![PyPI version](https://badge.fury.io/py/cobald-tardis.svg)](https://badge.fury.io/py/cobald-tardis)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/cobald-tardis.svg?style=flat-square)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://github.com/MatterMiners/tardis/blob/master/LICENSE.txt)
[![DOI](https://zenodo.org/badge/132791417.svg)](https://zenodo.org/badge/latestdoi/132791417)
[![Black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

# TARDIS Resourcemanager

![](docs/pics/TARDIS_logo.svg)

**T**ransparent **A**daptive **R**esource **D**ynamic **I**ntegration **S**ystem 
enables the dynamic integration of resources provided by different resource
providers into one overlay batch system.
tinysvm 0.1.0
A lightweight, dependency-free (NumPy only) SVM implementation.
Requires Python: >=3.7

# TinySVM 🚀

A lightweight, dependency-free (NumPy only), and educational implementation of Support Vector Machines (SVM) in pure Python.

**TinySVM** implements both Classification (SVC) and Regression (SVR) with a simplified SMO (Sequential Minimal Optimization) and Coordinate Descent algorithm. It serves as a great learning resource or a drop-in replacement for heavy libraries when you only need basic SVM functionality.

## ✨ Features

- **Zero Dependencies**: Only requires `numpy`. No `scikit-learn` or `scipy` needed.
- **Full-featured**:
  - Binary & Multi-class Classification (One-vs-Rest).
  - Regression (Single & Multi-output).
  - Kernel Support: Linear & RBF (Gaussian).
- **Production Ready-ish**:
  - Built-in **Auto-Scaling** (StandardScaler).
  - Probability estimates (`predict_proba`).
  - Scikit-learn compatible API (`fit`, `predict`, `score`).
- **Tiny**: Less than 300 lines of code.

## 📦 Installation

Just copy `tinysvm.py` to your project folder. Yes, it's that simple!

Dependencies:
```bash
pip install numpy
```

## ⚡ Quick Start

### Classification (XOR Problem)

```python
from tinysvm import TinySVM
import numpy as np

# XOR data (Linear Separable? No.)
X = [[0, 0], [1, 1], [1, 0], [0, 1]]
y = [0, 0, 1, 1]

# Initialize with RBF kernel and Auto-Scaling
clf = TinySVM(mode='classification', kernel='rbf', gamma=2.0, C=10.0, scaling=True)
clf.fit(X, y)

print(f"Prediction: {clf.predict([[0, 1]])}") # Output: [1]
print(f"Accuracy:   {clf.score(X, y)}")       # Output: 1.0
```

### Regression

```python
# Simple Linear Regression
X = [[1], [2], [3], [4], [5]]
y = [3, 5, 7, 9, 11] # y = 2x + 1

reg = TinySVM(mode='regression', kernel='linear', C=50.0)
reg.fit(X, y)

print(f"Prediction for x=6: {reg.predict([[6]])}") # Should be close to 13
```

## ⚙️ API Reference

### `TinySVM(mode, C, kernel, gamma, scaling, ...)`

- `mode`: `'classification'` or `'regression'`.
- `C`: Regularization parameter (default `1.0`).
- `kernel`: `'rbf'` (default) or `'linear'`.
- `gamma`: Kernel coefficient for RBF.
- `scaling`: Boolean. If `True` (default), automatically scales data using Z-score.

### Methods
- `fit(X, y)`: Train the model.
- `predict(X)`: Predict class or value.
- `predict_proba(X)`: (Classification only) Estimate class probabilities.
- `score(X, y)`: Returns Accuracy (Classification) or R² (Regression).
- `save(path) / load(path)`: Save/Load model state.

## 📜 License

MIT License. Feel free to use it in your own projects!