semvec/pypi-packages huggingface dataset:
whist-server 0.8.0
Whist server implementation
License: MIT
Requires Python: >=3.9,<4.0
Keywords: game,server,whist

[![codecov](https://codecov.io/gh/Whist-Team/Whist-Server/branch/main/graph/badge.svg)](https://codecov.io/gh/Whist-Team/Whist-Server)
[![security: bandit](https://img.shields.io/badge/security-bandit-yellow.svg)](https://github.com/PyCQA/bandit)
![PyPI](https://img.shields.io/pypi/v/whist-server)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/whist-server)
![PyPI - Wheel](https://img.shields.io/pypi/wheel/whist-server)
![GitHub repo size](https://img.shields.io/github/repo-size/whist-team/whist-server)
![Lines of code](https://img.shields.io/tokei/lines/github/whist-team/whist-server)
![PyPI - Downloads](https://img.shields.io/pypi/dm/whist-server)
![PyPI - License](https://img.shields.io/pypi/l/whist-server)

# Whist-Server

This is the REST API server of a Whist game. It provides user management, session organization and
a convenient interface for the rules' implementation of
[Whist-Core](https://github.com/Whist-Team/Whist-Core).

## Development

### Setup
You need [Poetry](https://python-poetry.org/) for development.
```bash
# Create venv and install deps
poetry install
```
The Python virtual environment will be created in the `.venv` directory.

### Run tests/lint
```bash
# Run tests (in venv)
python -m pytest # or pylint...
# OR
poetry run python -m pytest
```

### Build
Generates `sdist` and `bdist_wheel`.
```bash
poetry build
```

### Publish

You need the environment variable `POETRY_PYPI_TOKEN_PYPI` filled with a PyPI token.

```bash
poetry build
poetry publish
# OR
poetry publish --build
```

### Run

In order to use GitHub SSO you need to set two environment variables. At the moment they are
mandatory.

```dotenv
GITHUB_CLIENT_ID # This is the GitHub Identifier
GITHUB_CLIENT_SECRET # During creation this secret is generated.
GITHUB_REDIRECT_URL=http://HOST:PORT/oauth2/github/ # Only required for Browser Application with the ability to redirect.
```

---

#### Splunk

If you want to use Splunk you require an environment variable with the authentication token: 
`SPLUNK_TOKEN` and you have to start the server with optional arguments `--splunk_host` and 
`--splunk-port`.

Splunk support is currently disabled.

---

In order to run the application it must be started like this:

```shell
python -m whist_server --reload --admin_name=root --admin_pwd=password 0.0.0.0 8080
```

:warning: A mongodb instance is required to run before launching the `Whist-Server`.
Pixeldrain 1.1.2
Pixeldrain API
License: MIT
Requires Python: >=3.6

# Pixeldrain

A pixeldrain uploader and downloader made with pixeldrain api

## Installation

```sh
pip3 install Pixeldrain
```

## Usage

```py
import pixeldrain


# To get information about the file
info = pixeldrain.info(file_id)
# returns information as raw data

# To get file link
file = pixeldrain.file(file_id)
# returns file link

# To upload file
pixeldrain.upload_file(file_path)
# returns upload details

# To download file
pixeldrain.download_file(file_id, file_name, file_path)
# returns file name
# "file_name" and "file_path" are optional

# To get thumbnail
thumbnail = pixeldrain.thumbnail(file_id, width, height)
# returns thumbnail link
# width and height are optional
```

---

## Thanks to

- [Pixeldrain API](https://pixeldrain.com/api)
- [Contributors](https://github.com/FayasNoushad/Pixeldrain/graphs/contributors)
ProGPT 1.0.6
❄️ Python Package To Access GPT-3.5 Free Model
License: MIT
Keywords: progpt,chatgpt,gpt-3.5,python-gpt,python-chatgpt,ai,generative-ai,chatgpt-ai,python-ai,python-generative-ai

# ProGPT - Free ChatGPT API

[![PyPI](https://img.shields.io/pypi/v/progpt)](https://pypi.org/project/progpt)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/progpt)]()
[![Downloads](https://static.pepy.tech/badge/progpt/month)](https://pepy.tech/project/progpt)

![ChatGPT](https://img.shields.io/badge/chatGPT-74aa9c?style=for-the-badge&logo=openai&logoColor=white)

I reverse engineered **ChatGPT's Free Web API** and made this simple python package.

Both **Generative** & **Conversation** modes are supported.

[<img style="margin-top: 10px" src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" width="160"/>](https://buymeacoffee.com/diezo)

## Installation
```python
$ pip install progpt
```

### How to get *access_token*?
In your browser:
1. Login to [**chat.openai.com**](https://chat.openai.com)
2. Open [**this page**](https://chat.openai.com/api/auth/session), and you'll see **JSON** data
3. Copy value of **accessToken**


### Generative Mode
Answers individual prompts, doesn't remember past messages.

```python
from ProGPT import Generative

bot = Generative(access_token)

print(bot.prompt("who invented electricity?"))
```

### Conversation Mode
Creates a conversation thread and remembers your chat history.

```python
from ProGPT import Conversation

bot = Conversation(access_token)

print(bot.prompt("hello"))
print(bot.prompt("how are you?"))
```

## Rate Limits
To overcome the free tier's rate limits:
- Add time gap between prompts
- Use multiple accounts

## Donate
Wish to support this project? Please consider donating here:

**PayPal:** [**@gitdiezo**](https://www.paypal.com/paypalme/gitdiezo)

[<img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" width="150"/>](https://buymeacoffee.com/diezo)

## Legal
This is a third party library and not associated with OpenAI or ChatGPT. It's strictly for educational purposes. You are liable for all the actions you take.
XAccess 1.0
Communication with the server using server and client
License: MIT

# X-Access
X-Access is a Python package designed to facilitate remote server management and interaction. It consists of a client-side module for executing commands and managing files on a remote server and a server-side module for handling these commands and providing server information.
For get more information see my <a href="https://github.com/Sepehr0Day/X-Access/">gihtub</a> page
database-mongodbconnectorpkg 0.0.1
A python package for connecting with database.
License: MIT
Requires Python: >=3.7

# requirements_dev.txt we use for the testing
It makes it easier to install and manage dependencies for development and testing, separate from the dependencies required for production.

# difference between requirements_dev.txt and requirements.txt

requirements.txt is used to specify the dependencies required to run the production code of a Python project, while requirements_dev.txt is used to specify the dependencies required for development and testing purposes.

# tox.ini
We use if for the testing in the python package testing against different version of the python 

## how tox works tox enviornment creation
1. Install depedencies and packages 
2. Run commands
3. Its a combination of the (virtualenvwrapper and makefile)
4. It creates a .tox


# pyproject.toml
it is being used for configuration the python project it is a alternative of the setup.cfg file. its containts configuration related to the build system
such as the build tool used package name version author license and dependencies

# setup.cfg
In summary, setup.cfg is used by setuptools to configure the packaging and installation of a Python projec

# Testing python application
*types of testing*
1. Automated testing 
2. Manual testing

*Mode of testing*
1. Unit testing
2. Integration tests

*Testing frameworks*

1. pytest
2. unittest
3. robotframework
4. selenium
5. behave
6. doctest

# check with the code style formatting and syntax(coding standard)

1. pylint
2. flake8(it is best because it containt 3 library pylint pycodestyle mccabe)
3. pycodestyle
sen2venus-pytorch-dataset 0.0.4
Unofficial pytorch dataloader for the Sen2Venµs Super-Resolution dataset.
License: MIT

# sen2venus-pytorch-dataset
Unofficial dataloader for the [Sen2Venµs dataset](https://zenodo.org/record/6514159), baked at [CESBIO](https://www.cesbio.cnrs.fr/) by [Julien Michel, Juan Vinasco-Salinas, Jordi Inglada and Olivier Hagolle](https://doi.org/10.3390/data7070096).

## 🔍 Overview
Install with: 

```bash
pip install sen2venus-pytorch-dataset
```

This package provides a simple way to download and use the [Sen2Venµs dataset](https://zenodo.org/record/6514159) within the pytorch and Xarray ecosystems.


```python
from sen2venus import Sen2VenusSite
import matplotlib.pyplot as plt

dataset = Sen2VenusSite(root='./', site_name='SUDOUE-4', load_geometry=True, subset='all')
input, target = dataset.getitem_xarray(0)
input.plot.imshow(col='band')
target.plot.imshow(col='band')
plt.show()
```
![Matching Sentinel 2 and Venus samples](examples/samples_sentinel_venus.png)

## ✅ Features

- [x] **Automatic download from zenodo**: The Zenodo URLs and hashes are included. From a region name ([see the list](https://zenodo.org/record/6514159)), the corresponding subset is downloaded and decompressed. 

- [x] **x2, x4 or multi-resolution dataset loading**: you can pick the `rgbnir` or the `rededge` subset to load the x2 or the x4 low (Sentinel-2) and high (Venus) resolution patches, or specify `all` to concatenate Sentinel-2 multi-resolution bands, as in [this paper](https://hal.science/hal-04218629).

- [x] **inspired from existing frameworks**: the Sen2Venus class is inspired from [the torchsr dataset definition style](https://github.com/Coloquinte/torchSR/tree/main/torchsr/datasets) and the torchvision download utility are used.

- [x] **automatically retrieve geospatial information**: includes method to convert the dataset samples to Xarray `DataArray`s

- [x] **compatible with [sr-pytorch-lightning](https://github.com/george-gca/sr-pytorch-lightning/tree/main)**: you can train your own SR model using a [fork of sr-pytorch-lightning](https://github.com/piclem/sr-pytorch-lightning).

## 📋 TODO / WIP

- [x] better integration of download within class instantiation - currently needs to be reinstantiated
- [x] multiple regions download
- [x] pypi publishing
- [ ] parallel downloads
- [x] integration with [sr-pytorch-lightning](https://github.com/george-gca/sr-pytorch-lightning/tree/main)

## ▶️ Installation

You can install the package using `pip`:

```
pip install sen2venus-pytorch-dataset
```

or if you want to clone it first (e.g. to try out the examples):

```bash
git clone https://github.com/piclem/sen2venus-pytorch-dataset.git
cd sen2venus
pip install . 
```

## 📖 Documentation (WIP)

For more detailed information on the available parameters, methods, and best practices, please refer to the documentation.

## 🆘 Support and Issues (WIP)

If you encounter any issues, bugs, or have questions about the package, please feel free to open an issue on the GitHub repository. We appreciate your feedback!

## 🖊️ License

This package is released under the MIT License.
minder 0.0.2
Exception guard capture utility library
License: MIT
Requires Python: <=3.12,>=3.9

# Minder

[![Vercel deployment](https://img.shields.io/github/deployments/lmmx/minder/Production?label=vercel&logo=vercel&logoColor=white)](https://vercel.com/deployments/Production)
[![PyPI](https://img.shields.io/pypi/v/minder?logo=python&logoColor=%23cccccc)](https://pypi.org/project/minder)
[![pdm-managed](https://img.shields.io/badge/pdm-managed-blueviolet)](https://pdm.fming.dev)
[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/lmmx/minder/master.svg)](https://results.pre-commit.ci/latest/github/lmmx/minder/master)
[![Supported Python versions](https://img.shields.io/pypi/pyversions/minder.svg)](https://pypi.org/project/minder)

<!-- [![build status](https://github.com/lmmx/minder/actions/workflows/master.yml/badge.svg)](https://github.com/lmmx/minder/actions/workflows/master.yml) -->

Exception guard capture utility library.

## Installation

```sh
pip install minder
```

## Demo

The `Minder` context manager keeps failure modes **contained** with minimal **legibility** cost:

```py
from minder import Minder


def succeed() -> dict:
    with Minder() as guard:
        with guard.duty("winning"):
            guard.result = 100
    return guard.report()


response = succeed()
print(response)
```

```py
{'result': 100, 'success': True}
```

When an error is encountered, we get the same interface.

```py
from minder import Minder


def fail() -> dict:
    with Minder() as guard:
        with guard.duty("greet"):
            print("Hello world")
        with guard.duty("division"):
            guard.result = 1 / 0
    return guard.report()


response = fail()
print(response)
```

```py
Hello world
{'result': {'error': 'division by zero', 'where': 'division'}, 'success': False}
```

In this example we expose a reliable interface of a `result` and `success` boolean.

We could also return `guard` (the `Minder` instance) and handle success/failure at the call site,
but the assumption is we would rather have this prepared for us.