semvec/pypi-packages huggingface dataset:
sasdescargezero 0.1.2
Una librería Python para descargar archivos desde un host específico
Requires Python: >=3.6

```markdown
# SAS

Una librería Python para descargar archivos desde un host específico con autenticación.

## Instalación

```bash
pip install sas
```

## Uso

```python
import sas
# Se mostrará un mensaje pidiéndote que ingreses el enlace, por ejemplo:
# • >> sas https://sas.techdev.cu/40250728-171/2988-2989-2990/1/aHR0cHM6Ly9jaWVuY2ltZWQuc2xkLmN1L2luZGV4LnBhcC9jaWVuY2ltZWQ#-cmV2aXQwMDEw-w5dHZXJvMTIz-MTcx/S01E001_El_ascenso_del_lobo_compressed.mkv
```

## Requisitos

- Python 3.6+
- requests
- beautifulsoup4
- urllib3

## Licencia

MIT
```
constrafor-sample 0.0.4
Sample Package from Constrafor
Requires Python: >=3.10

# Constafor Packages

This is a simple example package. You can use
[GitHub-flavored Markdown](https://guides.github.com/features/mastering-markdown/)
to write your content.

### Initialize
```sh
pip install -e .
```

### Manual upload to GitHub
```sh
python -m twine upload --repository-url https://api.github.com/orgs/Constrafor/packages/pypi/upload sample_package/dist/*
```
cpf-playground 0.0.1
A Brazilian CPF manipulator package
Requires Python: >=3.5

# cpf_playground

## Description

The package cpf_playground is used to:
    - Generate CPFs (randomly or from staring first 9 digits)
    - Formatting, adding or removing "-" and "." characters
    - Validates formatted or raw CPF numbers

## installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install cpf_playground

```bash
pip install cpf_playground
```

## Usage

### Validating a raw CPF number
```python
    from cpf_playground.validation import validate_cpf

    cpf = "12345678909"
    validate_cpf(cpf) # True or false, according to the number
```

### Validating a formatted CPF number
```python
    from cpf_playground.validation import validate_cpf
    
    cpf = "123.456.789-09"
    validate_cpf(cpf) # True or false, according to the number
```

### Creating a new random and valid CPF number
```python
    from cpf_playground.generation import create_cpf

    cpf = create_cpf() # returns a valid CPF number
```

### Creating a valid CPF number from 9 initial digits
```python
    from cpf_playground.generation import create_from_9_digits

    first_9_digits = "123456789"
    cpf = create_from_9_digits(first_9_digits) # returns a valid CPF number
```

### Formatting a raw CPF number into a string with "-" and "." characters
```python
    from cpf_playground.formatting import fromat_cpf_from_raw

    raw_cpf = "12345678909"
    cpf = fromat_cpf_from_raw(raw_cpf) # Returns a formatted CPF, like 123.456.789-09
```

### Formatting a raw CPF number into a string with "-" and "." characters
```python
    from cpf_playground.formatting import fromat_cpf_to_raw

    cpf = "123.456.789-09"
    raw_cpf = fromat_cpf_to_raw(cpf) # Returns a raw CPF, like 12345678909
```

## Author

bernard_clint
constrafor-sample-javidkeskin 0.0.3
Sample Package from Constrafor
Requires Python: >=3.10

# Constafor Packages

This is a simple example package. You can use
[GitHub-flavored Markdown](https://guides.github.com/features/mastering-markdown/)
to write your content.

### Initialize
```sh
pip install -e .
```

### Manual upload to GitHub
```sh
python -m twine upload --repository-url https://api.github.com/orgs/Constrafor/packages/pypi/upload sample_package/dist/*
```
pywrapture 0.2.0
A simple greeting module
Requires Python: >=3.6

<div align="center">
  <img src="github/logo/pywrapture.png" alt="logo" vspace="5" hspace="5">
  <h1>PYWRAPTURE</h1>
</div>

Python utilities and decorators to simplify your code â€” elegant, readable, and pythonic.

`pywrapture` is a lightweight module that provides decorators and utility functions for streamlining common Python patterns. It helps make your code cleaner, more readable, and easier to maintain.
This module is particularly useful in production pipelines, testing environments, CLI tooling,
API layers, and any scenario where function behavior needs to be modified, extended,
monitored, or controlled without changing the original function logic.

## Features

- `@retry`         Automatically retry failed functions a configurable number of times.
- `@delay`         Postpone function execution by a given number of seconds.
- `@timer`         Measure and report function execution time.
- `@handle`        Catch and log exceptions to a plain-text file.
- `@JSONhandle`    Log exception details (with context) into a structured JSON file.

- Runtime Flags  Many decorators expose runtime parameters for greater control (e.g., toggle debug).
- Utility functions for common patterns
- Lightweight, no external dependencies
- Python 3.7+

## Installation

```bash
pip install pywrapture
```

Or install from source:

```bash
git clone https://github.com/yourusername/pywrapture.git
cd pywrapture
pip install .
```

## Usage

```python
from pywrapture import safe, retry, timed

@retry(attempts=3, delay=1)
def flaky_function():
    # Example: network call
    ...

@delay(seconds=2)
def slow_function():
    import time
    time.sleep(2)
    return "Done"
```

## License

GPL-3.0 License Â© 2025 Tkemza
m365-extra 1.0.1
Extra tools to interact with Microsoft M365 tenants.
Requires Python: >=3.11.2

M365 extra
==========

Extra tools to interact with Microsoft M365 tenants.


## Quick start guide

Prepare Python environment:

    python -m venv .venv            # Linux/Debian: python3 -m venv .venv
    .\.venv\Scripts\activate        # Linux/Bash:   source .venv/bin/activate
    pip install wheel
    pip install -r requirements.txt

Create `.env` config file. Example:

```ini
M365_TENANT_ID = mytenant_uuid
M365_APP_ID = myapp_uuid
M365_CLIENT_SECRET_KEY_FILE=pass:m365/myapp_uuid
```

Run command-line interface:

    python -m m365_extra -h


## Resources

- [Cheat sheet](docs/cheatsheet.md)
- [Microsoft Authentication Library](https://learn.microsoft.com/en-us/entra/msal/python/)
- [OneDrive API root resources](https://learn.microsoft.com/en-us/onedrive/developer/rest-api/?view=odsp-graph-online#onedrive-root-resources)
- [Public vs confidential application - OAuth standard](https://datatracker.ietf.org/doc/html/rfc6749#section-2.1)
- [Public vs confidential application - Entra ID](https://learn.microsoft.com/en-us/entra/identity-platform/msal-client-applications)


## Credits

Icon by [Swifticons - Flaticon](https://www.flaticon.com/free-icon/office_214527).

This project is licensed under the terms of the [MIT license](https://gitlab.com/ipamo/m365-extra/-/raw/main/LICENSE.txt).

This project is not affiliated in any way with Microsoft.
llmdatacleaner 0.1.0
Add your description here
Requires Python: >=3.10

# LLM Data Cleaner

A package for cleaning data using Large Language Models (LLMs).

```toml
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
```

## Installation

```bash
pip install llmdatacleaner
```

## Usage

### Basic Usage

```python
from llmdatacleaner import OpenAIDataCleaner

# Define your data cleaning prompt
prompt = """
Clean the following text data:
1. Fix spelling errors
2. Correct grammar
3. Remove any inappropriate content
4. Return the cleaned text
"""

# Initialize the cleaner
cleaner = OpenAIDataCleaner(
    data_cleaning_prompt=prompt,
    openai_api_key="your-api-key",  # Optional: Can also use environment variable OPENAI_API_KEY
    model_name="gpt-4o-mini"  # Default: "gpt-4o-mini"
)

# Clean a batch of data
data_to_clean = [
    "This is a smple text with speling errors.",
    "Another example with gramatical error.",
    "This one has no errors but could be improved."
]

# Process the batch with concurrent threading
results = cleaner.clean_batch(data_to_clean, max_workers=3)

# Print results
for result in results:
    print(f"Original: {result['original']}")
    print(f"Cleaned: {result['cleaned']}")
    print("---")
```

### Advanced Usage

```python
# With preprocessing and postprocessing functions
def preprocess(item):
    # For example, extract text from a JSON object
    if isinstance(item, dict) and 'text' in item:
        return item['text']
    return item

def postprocess(result):
    # Add additional metadata or transformations
    result['word_count'] = len(result['cleaned'].split()) if result['cleaned'] else 0
    return result

results = cleaner.clean_batch(
    data_to_clean,
    max_workers=5,
    show_progress=True,
    preprocess_fn=preprocess,
    postprocess_fn=postprocess
)
```

## Features

- Concurrent multithreading for improved performance
- Configurable OpenAI model selection
- Customizable data cleaning prompts
- Built-in logging
- Progress tracking with tqdm
- Optional preprocessing and postprocessing hooks
bank-management-fc 0.0.1
A bank management package
Requires Python: >=3.12.1

# bank_management_fc

Description. 
The package bank_management_fc is used to:

  - account: Create a Account and modify their transactions
  - balance: Transactions methods
  - user: Create a User and modify his accounts

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install bank_management_fc

```bash
pip install bank_management_fc
```

## Example

```python
from datetime import date
from bank_management_fc import NaturalPerson, CheckingAccount

user = NaturalPerson("Rua A, 123", "12345678900", "JoÃ£o Silva", date(1990, 1, 1))
acc = CheckingAccount.new_account(1, user) 
# acc = CheckingAccount(1, user) 
# user.add_account(acc)

acc.deposit(1000000000)
acc.withdraw(200)
acc.withdraw(300)
acc.withdraw(50)
print(acc.string_statement()) # String statement
print(acc.statement) # List statement
```

## Author
FlowCreeper

## License
[MIT](https://choosealicense.com/licenses/mit/)