semvec/pypi-packages huggingface dataset:
mlpro-int-river 0.5.0
MLPro: Integration River
Requires Python: >=3.11

[![CI](https://github.com/fhswf/MLPro-Int-River/actions/workflows/ci.yml/badge.svg)](https://github.com/fhswf/MLPro-Int-River/actions/workflows/ci.yml)
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[![PyPI version](https://badge.fury.io/py/mlpro-int-river.svg)](https://badge.fury.io/py/mlpro-int-river)
[![PyPI Total Downloads](https://static.pepy.tech/personalized-badge/mlpro-int-river?period=total&units=international_system&left_color=blue&right_color=orange&left_text=PyPI%20Total%20Downloads)](https://pepy.tech/project/mlpro-int-river)
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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.11130004.svg)](https://doi.org/10.5281/zenodo.11130004)


<img src="https://github.com/fhswf/MLPro-Int-River/blob/main/doc/logo/original/logo.png?raw=True" align="right" width="40%"/>

# MLPro-Int-River - Integration of River into MLPro
Welcome to MLPro-Int-River, an extension to MLPro to integrate the River package. MLPro is a middleware framework for standardized machine learning in Python. It is developed by the South Westphalia University of Applied Sciences, Germany, and provides standards, templates, and processes for hybrid machine learning applications. River, in turn, provides numerous state-of-the-art algorithms for several topics of online machine learning.

MLPro-Int-River provides wrapper classes that enable the use of selected River functionalities in your MLPro applications. The use of these wrappers is illustrated in numerous example programs.

### Learn more
[MLPro - The integrative middleware framework for standardized machine learning in Python](https://mlpro.readthedocs.io)   
[MLPro-Int-River - Integration of River into MLPro](https://mlpro-int-river.readthedocs.io)   
[River - Online machine learning in Python](https://riverml.xyz)
django-app-generator 0.1.0
Un outil pour créer des applications Django similaires à django-admin startapp
Requires Python: >=3.6

Django App Generator
Un outil en ligne de commande pour créer une structure d'application Django similaire à celle générée par django-admin startapp.
Installation
pip install django-app-generator

Utilisation
Pour créer une nouvelle application Django, exécutez :
django-create-app nom_application

Pour spécifier un répertoire cible :
django-create-app nom_application --target-dir chemin/vers/repertoire

Structure générée
L'outil crée les fichiers suivants :

__init__.py
admin.py
apps.py
models.py
tests.py
urls.py
views.py
migrations/__init__.py

Prérequis

Python 3.6 ou supérieur

Licence
MIT
qiye-wechat-mcp 0.1.5
企业微信群机器人 MCP 服务器
Requires Python: >=3.8

# 企业微信消息 MCP 服务器

简单的企业微信群机器人 MCP 服务器，让 AI 助手能发送消息到企业微信群。

## 安装

```bash
uvx install qiye-wechat-mcp
```

## 快速开始

### 1. 配置企业微信群机器人
1. 在企业微信群中添加群机器人
2. 复制机器人的 Webhook URL

### 4. 配置 mcpServers
```json
{
  "mcpServers": {
    "企业微信": {
      "command": "uvx",
      "args": ["qiye-wechat-mcp@latest"],
      "env": {
        "WECHAT_WEBHOOK_URL": "https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=your-webhook-key"
      }
    }
  }
}
```

## 功能
- `send_text_message(content)` - 发送文本消息
- `send_markdown_message(content)` - 发送Markdown消息

## AI提示词
```markdown
1. 使用中文回复
2. 在完成任务得最后，使用`qiye_wechat_mcp.send_text_message(content)`发送完成通知。
```
---


就这么简单！