semvec/pypi-packages huggingface dataset:
prune-lib 2.1.5
Internal library for prune.sh
Requires Python: >=3.13
Keywords: captcha, django, code-quality

# prune_lib

prune_lib est une bibliothèque Python regroupant des fonctions utilitaires réutilisables.  
L’objectif est de faciliter le développement de projets en centralisant des outils communs.

Le projet inclut aussi prune_captcha, un module dédié à l’ajout d’un CAPTCHA personnalisé dans les formulaires de contact.

## Projet UV

### Installation

Lancez la commande suivante dans le terminal :

```bash
uv add prune_lib
```

### Mise à jour de la bilbiothèque

N’hésitez pas à exécuter régulièrement uv sync --upgrade, car la bibliothèque évolue avec le temps et nos pratiques !

## Projet Poetry

### Installation

Lancez la commande suivante :

```bash
poetry add prune_lib
```

### Mise à jour de la bibliothèque

N’hésitez pas à exécuter régulièrement poetry update, car la bibliothèque évolue avec le temps et nos pratiques !

## Objectifs

-   Réutiliser facilement des fonctions communes.
-   Fournir une alternative simple à reCAPTCHA.
-   Ne dépendre d’aucun service externe.

## Intégration du captcha

### Configuration

Dans `settings.py`, définis le chemin des images utilisées pour le puzzle :

```python
PUZZLE_IMAGE_STATIC_PATH = "website/static/website/images/puzzles/"
```

Important : Tu dois importer les fichiers statiques (css, js) présents dans "prune_captcha/static/".

```html
<header>
    <link
        rel="stylesheet"
        href="{% static 'prune_captcha/css/captcha.css' %}"
    />
    <script defer src="{% static 'prune_captcha/js/captcha.js' %}"></script>
</header>
```

### Utilisation

-   Requête GET (affichage du formulaire)

    -   Utilise create_and_get_captcha pour générer les données du captcha :

        ```python
        from prune_captcha.utils import create_and_get_captcha
        ```

        ```python
        puzzle = create_and_get_captcha(request)
        ```

    -   Passe les données dans le contexte sous la variable puzzle :

        ```python
        return render(
            request,
            "website/pages/contact/page.html",
            {"form": form, "puzzle": puzzle},
        )
        ```

    -   Inclus le composant dans ton template :

        ```
        {% include "prune_captcha/captcha.html" %}
        ```

-   Requête POST (soumission du formulaire)

    -   Utilise verify_captcha pour valider le captcha :

        ```python
        from prune_captcha.utils import verify_captcha
        ```

        ```python
        response = verify_captcha(request)
        ```

    -   True si le captcha est correct, sinon False.

    -   Redirige en cas de captcha incorrect.
paradime-io 4.18.0
Paradime - Python SDK
Requires Python: >=3.8.1

<p align="center">
  <a href="https://www.paradime.io">
        <img alt="Paradime" src="https://app.paradime.io/logo192.png" width="60" />
    </a>
</p>

<h1 align="center">
  Paradime - Python SDK
</h1>

## Installation

```sh
pip install paradime-io
```

## SDK Usage

Generate your API key, secret and endpoint from Paradime workspace settings.

```python
from paradime import Paradime

paradime = Paradime(
    api_endpoint="API_ENDPOINT", 
    api_key="API_KEY", 
    api_secret="API_SECRET",
)

# Use the paradime client to interact with the API
```

## CLI Usage

For the full specification of the CLI, run:
```bash
paradime --help
```

Generate your API key, secret and endpoint from Paradime workspace settings. Then set the environment variables:

```bash
export PARADIME_API_ENDPOINT="YOUR_API_ENDPOINT"
export PARADIME_API_KEY="YOUR_API_KEY"
export PARADIME_API_SECRET="YOUR_API_SECRET
```

## Examples

Find usage examples [here](https://github.com/paradime-io/paradime-python-sdk/tree/main/examples) to get started with the Paradime Python SDK.
mlgame3d-envs 0.2.0
Unity Machine Learning Agents Interface
Requires Python: >=3.11

# Unity ML-Agents Python Interface

The `mlagents_envs` Python package is part of the
[ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents).
`mlagents_envs` provides three Python APIs that allows direct interaction with the
Unity game engine:
- A single agent API (Gym API)
- A gym-like multi-agent API (PettingZoo API)
- A low-level API (LLAPI)

The LLAPI is used by the trainer implementation in `mlagents`.
`mlagents_envs` can be used independently of `mlagents` for Python
communication.

## Installation

Install the `mlagents_envs` package with:

```sh
python -m pip install mlagents_envs==1.1.0
```

## Usage & More Information

See
- [Gym API Guide](../docs/Python-Gym-API.md)
- [PettingZoo API Guide](../docs/Python-PettingZoo-API.md)
- [Python API Guide](../docs/Python-LLAPI.md)

for more information on how to use the API to interact with a Unity environment.

For more information on the ML-Agents Toolkit and how to instrument a Unity
scene with the ML-Agents SDK, check out the main
[ML-Agents Toolkit documentation](../docs/Readme.md).

## Limitations

- `mlagents_envs` uses localhost ports to exchange data between Unity and
  Python. As such, multiple instances can have their ports collide, leading to
  errors. Make sure to use a different port if you are using multiple instances
  of `UnityEnvironment`.
- Communication between Unity and the Python `UnityEnvironment` is not secure.
- On Linux, ports are not released immediately after the communication closes.
  As such, you cannot reuse ports right after closing a `UnityEnvironment`.
iflow-mcp_buildmcpserver 0.1.0
Add your description here
Requires Python: >=3.11

# Build a MCP Server 
A complete walkthrough on how to build a MCP server to serve a trained Random Forest model and integrate it with Bee Framework for ReAct interactivity.

## See it live and in action 📺
<a href="https://www.linkedin.com/posts/nicholasrenotte_mcp-servers-make-tools-a-bunch-easier-for-activity-7305748751162163200-dIEn?utm_source=share&utm_medium=member_desktop&rcm=ACoAABbxZgUBrud9C531KZPQHCs2riXCiv9Av2A"><img src="https://i.imgur.com/Y2LN9dd.png"/></a>

# Startup MCP Server 🚀
1. Clone this repo `git clone https://github.com/nicknochnack/BuildMCPServer`
2. To run the MCP server\
`cd BuildMCPServer`\
`uv venv`\
`source .venv/bin/activate`\
`uv add .`\
`uv add ".[dev]"`\
`uv run mcp dev server.py`
3. To run the agent, in a separate terminal, run:\
`source .venv/bin/activate`\
`uv run singleflowagent.py`

# Startup FastAPI Hosted ML Server 
`git clone https://github.com/nicknochnack/CodeThat-FastML`\
`cd CodeThat-FastML`\
`pip install -r requirements.txt`\
`uvicorn mlapi:app --reload`\
Detailed instructions on how to build it can also be found <a href="https://youtu.be/C82lT9cWQiA?si=dIsL6eM1lUMAVcf0">here</a></br>


# Other References 🔗 </br>
- <a href="https://github.com/RGGH/mcp-client-x/blob/main/src/client/mcp_client.py">Building MCP Clients (used in singleflow agent)</a></br>
- <a href="https://www.youtube.com/watch?v=C82lT9cWQiA&t=1003s ">Original Video where I build the ML server</a>


# Who, When, Why?
👨🏾‍💻 Author: Nick Renotte <br />
📅 Version: 1.x<br />
📜 License: This project is licensed under the MIT License </br>
pypjt 2.1.0
A project to create python project template.
Requires Python: >=3.10

Pypjt - Python Project Template
===============================

Pypjt is a project to create python project template.

Docs: https://pypjt.readthedocs.io/en/latest/

.. image:: https://img.shields.io/pypi/v/pypjt.svg
   :target: https://pypi.org/project/pypjt/
   :alt: PyPI version

.. image:: https://static.pepy.tech/badge/pypjt
   :target: https://pepy.tech/projects/pypjt
   :alt: PyPI downloads

.. image:: https://github.com/sleeping-in-bed/pypjt/actions/workflows/test.yml/badge.svg?branch=main
   :target: https://github.com/sleeping-in-bed/pypjt/actions/workflows/test.yml
   :alt: Test status

.. image:: https://codecov.io/github/sleeping-in-bed/pypjt/graph/badge.svg?token=HEIMHMX0PK
   :target: https://codecov.io/github/sleeping-in-bed/pypjt
   :alt: Codecov

Installation
------------

To install **pypjt**, use pip:

.. code-block:: console

   $ python -m pip install pypjt

Quickstart
----------

To get started, simply run:

.. code-block:: console

   $ pypjt

If running it as a script doesn't work, you can run it as a module instead:

.. code-block:: console

   $ python -m pypjt

It's safe to run—**it will not overwrite any existing directories**.

Once launched, you’ll be prompted to answer a few questions about the project you want to create.

The **project name** is the most important and required field.
You can leave the other fields empty or accept their default values.
All of these settings can be changed later in ``pyproject.toml`` or other configuration files.

After you’ve completed the prompts, your project will be generated
and saved under the current working directory.

Great! You can now start coding within the newly created folder,
or further customize the structure to fit your needs.