semvec/pypi-packages huggingface dataset:
mobile-terminal 0.2.1
The terminal moves with you - Access your terminal from any mobile device
Requires Python: >=3.7
Keywords: terminal, broadcast, remote, mobile, websocket, cli, devtools

# mobile-terminal

The terminal moves with you. Access and control your terminal from any mobile device.

## What it does

mobile-terminal creates a local web server that streams your terminal output to your phone in real-time. When your terminal needs input (like Y/N prompts, passwords, or any user input), you get notified on your phone and can respond directly from there.

Perfect for:
- Long-running scripts that occasionally need input
- Remote deployments that ask for confirmation
- AI coding assistants (like Claude) that need permissions
- Any command that might interrupt your workflow

## Features

- 📱 **Real-time terminal streaming** to your phone
- 🔔 **Instant notifications** when input is needed
- ⌨️ **Send commands** from your phone
- 🚫 **Prevents laptop sleep** while running
- 🔒 **Local network only** - secure by default
- 🎯 **Zero configuration** - just run and go

## Installation

### 🐍 Using pip (Recommended)
```bash
pip install mobile-terminal
```

### 🍺 Using Homebrew (macOS/Linux)
```bash
# Coming soon!
brew tap josharsh/tap
brew install mobile-terminal
```

### 📦 Using pipx (Isolated Environment)
```bash
pipx install mobile-terminal
```

### 🔧 From Source
```bash
git clone https://github.com/josharsh/mobile-terminal.git
cd mobile-terminal
pip install -e .
```

### 🐳 Using Docker
```bash
# Coming soon!
docker run -p 8080:8080 josharsh/mobile-terminal
```

### Requirements
- Python 3.7 or higher
- Works on macOS, Linux, and Windows (WSL)

## Usage

### Basic usage
```bash
# Run any command with mobile-terminal
mobile-terminal npm install
# Or use the short alias
mterm npm install

# Or start an interactive shell
mobile-terminal
# Or simply
mterm

# Then open the URL on your phone:
# http://192.168.1.42:8080
```

### Examples

Monitor a deployment:
```bash
term-cast ./deploy-to-production.sh
```

Watch AI coding assistant:
```bash
term-cast claude-code
```

System updates:
```bash
term-cast sudo apt upgrade
```

## How it works

1. term-cast creates a pseudo-terminal (PTY) that captures all input/output
2. Starts a local web server on port 8080
3. Your phone connects via WebSocket for real-time streaming
4. When input is detected, your phone gets notified
5. You can type responses directly from your phone
6. Laptop stays awake while term-cast is running

## Requirements

- Python 3.7+
- Same WiFi network for laptop and phone
- Modern web browser on phone

## Security

- **Local network only** - no data leaves your network
- **No authentication** by default - only use on trusted networks
- **Optional encryption** - use `--ssl` flag for HTTPS
- **Session isolation** - each run gets a unique session

## License

MIT
smoothopt 0.1.0
Add your description here
Requires Python: >=3.13

Making a hyperparameter tuning / NAS library for deep learning.

Usage:
```python
import smoothopt as smo

study = smo.minimize(
    'val_loss',
    params={
        'num_epochs': smo.range(1, 100),
        'learning_rate': smo.range(1e-6, 0.02, log_scale=True),
        'batch_size': smo.ordinal([64, 128, 256, 512]),
        'activation': smo.choice([nn.ReLU(), nn.SiLU(), nn.GELU(), nn.Tanh()]),
    }
)

while True:
    trial = study.start_trial()
    metrics = train_model(trial.params) # Your model training loop
    trial.report(metrics)
    study.save('study.pkl')
```
hallo-enoch 0.0.1
Method to calculate Faculty
Requires Python: >=3.0

Great! Here's an enhanced version of the `README.md`, tailored for a **GitHub project**, with sections for badges, usage examples, optional features, and licensing. It assumes you're open-sourcing this under the MIT license.

---

```markdown
# 🧮 Recursive Factorial Calculator

[![Python Version](https://img.shields.io/badge/Python-3.6%2B-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)

A simple Python script to calculate the factorial of a number using recursion.  
This is a beginner-friendly example that demonstrates the use of recursive functions in Python.

---

## 🚀 Features

- Recursive implementation of factorial
- Clean and beginner-friendly code
- Easily modifiable
- No external dependencies

---

## 📂 File Structure
```

📁 factorial-recursive/
│
├── fakultaet.py # Main Python script
└── README.md # Project documentation

````

---

## 🧾 Code Overview

```python
def fakultaet(n):
    if n == 0 or n == 1:
        return 1
    else:
        return n * fakultaet(n - 1)

# Beispiel: Fakultät von 5 berechnen
zahl = 5
ergebnis = fakultaet(zahl)
print(f"Die Fakultät von {zahl} ist {ergebnis}")
````

---

## 🧪 Sample Output

```
Die Fakultät von 5 ist 120
```

---

## 🔧 How to Use

1. **Clone the repository**:

```bash
git clone https://github.com/Enoch1234455/test-repo/factorial-recursive.git
cd factorial-recursive
```

2. **Run the script**:

```bash
python fakultaet.py
```

3. **Change the input**:

Modify the `zahl` variable in the script to calculate a different factorial.

```python
zahl = 7  # Calculates 7!
```

---

## 💡 Enhancements (Optional Ideas)

You can expand this project by:

- Adding **input validation** for negative numbers
- Using an **iterative approach** for large numbers
- Adding **unit tests** with `unittest` or `pytest`
- Creating a **command-line interface (CLI)** using `argparse`

---

## 📜 License

This project is licensed under the [MIT License](LICENSE).

---

## 🙌 Contributing

Feel free to fork this repo, open issues, or submit pull requests! Contributions are welcome.

---

## 📬 Contact

Made with ❤️ by [Your Name](https://github.com/your-username)

```

---

### ✅ Next Steps for You:

- Replace `"your-username"` with your GitHub username.
- Add a `LICENSE` file if you haven’t already (MIT license is a common choice).
- Create a `fakultaet.py` file with your original code.
- Push it all to a GitHub repository.

Let me know if you'd like help generating the `LICENSE` file or setting up unit tests!
```
fast-json-to-csv 0.1.0
A fast and efficient JSON to CSV converter
Requires Python: >=3.6

# Fast JSON to CSV

Minimal dependencies fast and efficient JSON to CSV converter that doesn't rely on the standard CSV module. This package provides a simple way to convert JSON data to CSV format with support for nested JSON structures.

## Features

- Convert JSON data to CSV format
- Handle nested JSON structures
- No dependency on Python's CSV module
- Support for custom delimiters
- Efficient memory usage

## Installation

```bash
pip install fast-json-to-csv
```

## Usage

```python
from fast_json_to_csv import JsonToCSV

# Your JSON data
json_data = {
    "name": "John",
    "address": {
        "city": "New York",
        "zip": "10001"
    }
}

# Convert to CSV
converter = JsonToCSV(json_data)
headers, body = converter.json_to_csv()

# Write to file
with open('output.csv', 'w') as f:
    # Write headers
    f.write(', '.join(headers))
    f.write('\n')
    # Write body
    for row in body:
        f.write(', '.join(row))
        f.write('\n')
```

## API Reference

### JsonToCSV

The main class for converting JSON to CSV.

#### Methods

- `json_to_csv()`: Converts the JSON data to CSV format, returns a tuple of (headers, body)
- `flatten(d, prefix)`: Static method to flatten nested JSON structures
- `merge_d(a, b)`: Static method to merge dictionaries

## License

This project is licensed under the MIT License - see the LICENSE file for details.
json-dev-logger 0.0.15
json logger, very helpful for developers at developing stage
Requires Python: >=3.7

# py_logger
[![PyPI Downloads](https://static.pepy.tech/badge/json-dev-logger)](https://pepy.tech/projects/json-dev-logger)

Project homepage is https://github.com/Areso/py_logger

## Why?

I like to tinker things, and while I am writing the code, I am also the person who runs the code as well. This why I made this small library to gather enriched logs from my Python-powered projects. The enriched data includes name of the repo, branch, manually modified files (when debugging I could change something with my barehands right on a server, where my projects run)

## Installation through PyPi
`pip3 install json-dev-logger`  
in a project:  
```
from py_logger import setup_logger

logger = setup_logger()
logger.info("test")
```  

example of a record:  
```
{"level": "INFO", 
 "time": "2025-03-26 19:48:07,797", 
 "message": "test", 
 "name": "jsonLogger", 
 "filename": "1.py", 
 "lineno": 4, 
 "pathname": "/home/username/git/py_logger/1.py", 
 "hostname": "devserver-24",
 "reponame": "py_logger", 
 "branch": "master", 
 "modified_files": " M README.md"}
```
tkinter-gui 1.3.5
Create multilingual interfaces for your tkinter applications.
Requires Python: >=3.9

tkintergui
Create multilingual interfaces for your tkinter applications.

tkintergui leverages Tk's tkintergui to provide a per-instance message catalog which holds all the translations, while allowing them to be kept in separate files away from code.

⏬ Installation
tkintergui requires Python 3.7+

pip install tkintergui
✨ Getting Started
For storing the translation files I recommend this folder hierarchy:

  project (or src/project)
  ├── __init__.py
  └── gui
      ├── __init__.py
      ├── hi.msg
      └── mr.msg
This layout is recommended by Tcl.

Add some translations in the .msg files, in this case hi.msg:

::tkintergui::mcset hi "Hello"
Create a Tkinter window or instance, technically.

Let's put tkintergui into action!

from pathlib import Path


msgsdir = Path(__file__).parent / "msgs"
load(msgsdir)
locale("hi")
get("Hello")
🤝 Contributing
All contributions are welcome and acknowledged. Please read the contributor's guide.

© License
The code in this project is released under the 3-Clause BSD License.
datagem 0.1.1
Um pacote para extrair informações de PDFs usando a API do Google Gemini.
Requires Python: >=3.8

# Gemini PDF Extractor

Extrai informações estruturadas de arquivos PDF usando a API Gemini. Esta classe foi projetado para processar uma pasta de arquivos PDF usando prompts estruturados.

## Instalação

```bash
pip install datagem
```

## Chave da Gemini API

Para usar este pacote, você precisa ter uma chave da Gemini API. Caso você não tenha, você pode obtê-la no **Google AI Studio**: [aistudio.google.com/app/apikey](https://aistudio.google.com/app/apikey).

## Uso

Você pode extrair as informações de todos os pdfs e todos os prompts usando o método `extract_all`. O método retorna um dicionário, em que as chaves são _prompts_ e os valores são `DataFrame` (do pandas).

```python
from datagem.pdf import DataExtractorPdf

extractor = DataExtractorPdf(
    api_key="SUA_CHAVE_API",
    path_files="caminho/para/seus_pdfs/",
    path_prompts="caminho/para/seus_prompts/"
)
resultados_dict = extractor.extract_all()
primeiro_dataframe = list(results_dict.values())[0]
print(primeiro_dataframe.head())
```

Você pode extrair as informações de alguns pdfs e alguns prompts usando o método `extract_partial`. O método retorna um dicionário, em que as chaves são _prompts_ e os valores são `DataFrame` (do pandas).

```python
from datagem.pdf import DataExtractorPdf

extractor = DataExtractorPdf(
    api_key="SUA_CHAVE_API",
    path_files="caminho/para/seus_pdfs/",
    path_prompts="caminho/para/seus_prompts/"
)
resultados_dict = extractor.extract_partial(prompts = ['prompt1.txt', 'prompt2.txt'],file = ['file1.pdf', 'file2.pdf', 'file3.pdf'])
primeiro_dataframe = list(results_dict.values())[0]
print(primeiro_dataframe.head())
```