semvec/pypi-packages huggingface dataset:
eat 1.0.0
A funny execute module, enjoy.

<p align="center">
  <img src="https://via.placeholder.com/300x100?text=EAT" alt="EAT Logo" />
</p>

<h1 align="center">🍽️ <span style="color:#d35400;">EAT</span> - Execute Analyze Transform</h1>

<p align="center" style="font-size: 1.1em;">
  <strong>مكتبة ذكية لتحليل، فحص، تشويش وتنفيذ كود بايثون بأمان</strong><br>
</p>

<hr>

<h2 align="center">✨ الميزات</h2>

<p align="center">
  🔥 <strong>burn(code)</strong> - تشويش الكود وجعله غير قابل للفهم بسهولة<br>
  📊 <strong>chew(code)</strong> - تحليل الكود وعدّ الأسطر والدوال والكلمات<br>
  🛡️ <strong>scan(code)</strong> - كشف أوامر مشبوهة مثل <code>exec</code> أو <code>import os</code><br>
  ⚙️ <strong>digest(code)</strong> - تنفيذ الكود داخل بيئة محمية (Sandbox)<br>
  ♻️ <strong>full(code)</strong> - منع تكرار تنفيذ نفس الكود<br>
  🧠 <strong>throwup()</strong> - إرجاع نتيجة آخر تنفيذ للكود<br>
</p>

<hr>

<h2 align="center">🚀 الاستخدام</h2>

```python
from eat import burn, chew, digest, scan, throwup, full
code = '''
def hello():
    print("Hello from EAT")
hello()
'''

print(burn(code))
print(chew(code))
print(scan(code))

if full(code):
    digest(code)
    print(throwup())
else:
    print("الكود تم تنفيذه مسبقاً.")
```
<hr><h2 align="center">🧪 مثال عملي</h2>code = "print('Welcome to EAT!')"
if not scan(code):
    if full(code):
        digest(code)
        print("✅ تم التنفيذ!")
    else:
        print("⚠️ تم تنفيذ الكود مسبقاً.")
else:
    print("❌ تم الكشف عن كود ضار!")

<hr><h2 align="center">📦 التثبيت</h2>pip install eat

<hr><h2 align="center">🧑‍💻 المؤلف</h2><p align="center">
  d9c.<br>
  هذه المكتبة تعليمية وقابلة للتوسّع في المستقبل القريب.
</p>
qink 0.1.4
A real-time data processing framework
Requires Python: >=3.7

<img src="./logo.jpg" />

# Qink

Qink is a powerful distributed data processing framework designed for efficiently consuming and processing partitioned data streams.

## Features

- **Partition-based Processing** 📊: Handles partitioned data sources with exceptional efficiency, improving performance metrics.
- **Parallel Processing** ⚡: Processes multiple partitions simultaneously with configurable workers per partition.
- **State Management** 🛡️: Maintains processing state with reliability, ensuring fault tolerance.
- **Checkpointing**: Saves state at regular intervals to ensure data durability.
- **Key-based Distribution**: Distributes keys to workers with consistent precision for optimized processing.

## Usage

```python
import logging
from datetime import timedelta
from qink.lib.qink import Qink
from qink.lib.qink_storage_provider import YourStorageProvider
from qink.lib.qink_source import YourDataSource

# Configure Qink
logger = logging.getLogger("qink")
storage_provider = YourStorageProvider()
workers_per_partition = 4  # Customize for optimal performance
checkpoint_interval = timedelta(minutes=5)

# Initialize and start Qink
qink = Qink(
    logger=logger,
    storage_provider=storage_provider,
    workers_per_partition=workers_per_partition,
    checkpoint_interval=checkpoint_interval
)

# Connect to your data source
qink.source(YourDataSource()).start()
```

## Testimonials

"Qink revolutionized our data pipeline efficiency." - Data Engineering Team Lead

"After implementing Qink, our processing speeds improved dramatically." - Enterprise Architect

<hr/>

Copyright 2024 Quadible

This software is the exclusive property of Quadible and is protected under copyright law. Unauthorized copying, distribution, or use of this software, in whole or in part, without express permission from Quadible is strictly prohibited.

This repository and its contents are for authorized internal use only. External sharing or modification is not permitted unless written consent is obtained from Quadible.

For inquiries about permitted usage or licensing, please contact [info@quadible.co.uk](mailto:info@quadible.co.uk).
streamlit-indexed-db 0.1.2
A streamlit componente that allows you to request and receive data from indexedDB
Requires Python: >=3.7

# streamlit-indexed-db

A streamlit componente that allows you to request and receive data from indexedDB

## Installation instructions 

```sh
pip install streamlit-indexed-db
```

## Usage instructions

```python
import streamlit as st

from streamlit_indexed_db import streamlit_indexed_db

value = streamlit_indexed_db()

st.write(value)