semvec/pypi-packages huggingface dataset:
spellcure 1.0.4
SpellCure — correction engine for highly scrambled text
License: MIT
Requires Python: >=3.8

# SpellCure

**SpellCure** is a mathematical correction engine for highly scrambled or distorted text, created by **Saheban Khan** *(GitHub: Lsaheban)* and maintained by **Tohid Khan** *(GitHub: Tohid096)*.

Rather than using machine learning, SpellCure applies a **position-weighted ratio algorithm** to match noisy tokens with valid dictionary words — enabling high-accuracy recovery even from severely jumbled text.

---

## ✨ Features

- Corrects **heavily scrambled** or distorted words  
- Pure **mathematical algorithm** (no ML required)  
- Supports:
  - **Small built-in vocabulary** (~10k curated words)
  - **Large NLTK vocabulary** (~200k+ words)
- Works with single words, sentences, or mixed noisy text  
- Fast, deterministic, and lightweight  
- Extensible word bank (users may request custom additions)

---

## 🧠 How SpellCure Works

SpellCure analyzes each token using:

- Position-based character similarity  
- Ratio scoring  
- Multi-stage refinement  
- Optional large NLTK dataset  

Examples:

| Input                  | Output                 |
|------------------------|------------------------|
| `aplpe`               | `apple`                |
| `tihs wrod is msesdy` | `this word is messy`   |
| `olve is evryetign`   | `love is everything`   |

---

## 🚀 Quick Start

### Importing SpellCure

```python
from spellcure import corrector
---
## 🧪 Example Usage

Here is a minimal working example using the **small** vocabulary mode:

```python
from spellcure import corrector

def test_small():
    model = corrector(mode="small")   # Use small curated word bank
    output = model.correct("olve is evryetign")
    print(output)

test_small()
#Outpu: love is everything


# small = ~10k curated words
# large = ~200k NLTK words
model = corrector(mode="large")
----
# bash pip install spellcure
keyboard-crossplatform 0.2.0
Cross-platform keyboard event handling for macOS, Windows, and Linux.
License: MIT
Requires Python: >=3.6
Keywords: keyboard, hotkeys, input, accessibility, automation, cross-platform

# Keyboard Cross-Platform Keylogger

This project is a cross-platform keylogger implemented in Python. It supports macOS, Windows, and Linux operating systems, allowing users to capture keyboard events seamlessly across different environments.

## Features

- Cross-platform support for macOS, Windows, and Linux.
- Real-time key logging with minimal performance impact.
- Easy to integrate and extend for additional functionalities.

## Installation

To install the required dependencies, you can use pip. Make sure you have Python installed on your system, then run:

```
pip install -r requirements.txt
```

## Usage

To run the keylogger, execute the following command in your terminal:

```
python -m keyboard_crossplatform
```

The keylogger will start listening for key presses. To stop the keylogger, press the `esc` key.

## Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue for any enhancements or bug fixes.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
tungsten-ui 0.1.1
Tungsten UI - A Django component library with Cotton, HTMX, JavaScript, Tailwind CSS and DaisyUI
License: MIT
Requires Python: >=3.8
Keywords: components, daisyui, django, htmx, javascript, tailwind, ui

# Tungsten UI

A modern Django component library built with Django Cotton, Tailwind CSS, and DaisyUI.

## 🚀 Quick Installation

### For End Users (Recommended)

#### 1. Install Package

```bash
pip install tungsten-ui
```

This automatically installs Django Cotton as a dependency.

#### 2. Configure Django Settings

Add to your `settings.py`:

```python
INSTALLED_APPS = [
    'django.contrib.admin',
    'django.contrib.auth',
    'django.contrib.contenttypes',
    'django.contrib.sessions',
    'django.contrib.messages',
    'django.contrib.staticfiles',
    
    # Third-party apps
    'django_cotton',           # Required: Django Cotton
    'tungsten_ui',    # Tungsten UI
    
    # Your apps
    'your_app',
]
```

#### 3. Include Required Assets

**Simple Method (Recommended):**
```html
<!-- In your base.html head section -->
{% include "init-components.html" %}
```

**Manual Method (Optional):**
```html
<!-- In your base.html -->
<head>
    {% load static %}
    
    <!-- Tungsten UI CSS (pre-built) -->
    <link rel="stylesheet" href="{% static 'tungsten_ui/css/components.css' %}">
    
    <!-- HTMX -->
    <script src="https://cdn.jsdelivr.net/npm/htmx.org@2.0.6/dist/htmx.min.js"></script>
    
    <!-- Alpine.js (for reactive components) -->
    <script defer src="https://cdn.jsdelivr.net/npm/alpinejs@3.14.8/dist/cdn.min.js"></script>
    
    <!-- Tungsten UI JavaScript -->
    <script src="{% static 'tungsten_ui/js/components.js' %}"></script>
</head>
```

#### 4. Start Using Components

```html
<!-- Button component -->
<c-button variant="primary" size="lg">
    Click me!
</c-button>

<!-- Card component -->
<c-card class="max-w-sm">
    <c-slot name="title">Card Title</c-slot>
    <c-slot name="content">
        <p>This is a card content.</p>
    </c-slot>
</c-card>

<!-- Table with filters -->
<c-table :data="employees">
    <c-table.column field="name" label="Name" />
    <c-table.column field="department" label="Department" filterable />
    <c-table.column field="salary" label="Salary" filterable format="currency" />
</c-table>
```


## 📄 License

MIT License

---

**Tungsten UI - Built with Django-cotton, Tailwind CSS, DaisyUI, HTMX, and Alpine.js**