semvec/pypi-packages huggingface dataset:
prvict-test 0.7.5
A library pioneering AI and computational based tech solutions.
Requires Python: >=3.11

<p align="center" style="margin-bottom: 0;"><img src="https://github.com/VaidikKhurana/prvict/blob/master/src/prvict/assets/icons/prvict-logo-sideways.png" width="230" alt="Prvict Logo"></p>
<div align="center" style="margin-top: 0;">

<div align="center">
<b>A library pioneering tech solutions.</b>
</div>
<br>

[![github](https://img.shields.io/badge/-grey?logo=github)](https://github.com/VaidikKhurana/prvict)
[![yt](https://img.shields.io/badge/-grey?logo=youtube)](https://www.youtube.com/@VaidikKhurana)
[![pypi](https://img.shields.io/badge/-grey?logo=python&logoColor=white)](https://google.com)
![Discord](https://img.shields.io/badge/-grey?logo=discord&logoColor=white)

</div>

***
An open-source project consisting of algorithms, programs, and tech solutions for future innovations and implications. The PRVICT project is a group of programmers building solutions through computational means by solving each and every problem through the medium of technology. **We have the goal of revolutionizing technology and using it for the betterment of the world. Our primary objective is to work, improvise, and optimize artificial intelligence for multiple use cases with both commercial and private implications**.

Our algorithms consist of expert logical and analytical functions, allowing them to be efficient in every use. We plan on developing the world's best AIs by changing today's architecture of creating AI. In the future, the project will scale to multiple AI models for many use cases. This may include LLM's, image recognition, computer vision, and many more.
***
partenaire-qualif 0.2.4
A simple tool that automates the qualification of a partner(reseller/integrator/editor) by finding its website, industries, business functions and services.

# Background

A simple tool that automates the qualification of a partner(reseller/integrator/editor) by finding its website, industries, business functions and services in a structured way.

* It provides browsing ability to reduce hallucination problem and improve the information precision.

* It provides single mode and batch mode so that you can integrate as an API or in a scheduler easily.

An example of qualification result:

* (industry column) Target your industry
* (job column) The jobs that can be targeted through their industry
* (offers column) The services they offer.

![alt text](samples/qualification_results_example.png)

# Install

```bash
pip install partenaire-qualif
```

# Usage
The tool provides browsing ability which can be activated by setting the parameter browsing to True. Sample code below.

```python
from qualif import CompanyQualificationTool
from openai import AzureOpenAI, OpenAI
import pandas as pd


# Necessary Variables
azure_endpoint = "https://..."  # azure openai endpoint
apikey = "..." # azure openai apikey
apiversion = "..." # azure openai api version
model = "..." # azure openai model name

# Setup OpenAI CLI
client = AzureOpenAI(
    azure_endpoint=azure_endpoint,
    api_key=apikey,
    api_version=apiversion,
)

# Use QualificationTool without browsing
browsing = False
tool = CompanyQualificationTool(client=client, model=model, temperature=0, browsing=browsing)
result = tool.qualify(partner_name="Your_Partner_Name")


# Use QualificationTool with browsing
browsing = True
browsing_key = '...' # azure bing search apikey
tool = CompanyQualificationTool(client=client, model=model, temperature=0, browsing=browsing, browsing_key=browsing_key)
result = tool.qualify(partner_name="Your_Partner_Name")
print(result)
```

The tool also allow to execute in batch mode by providing a panda dataframe. Sample code as below.

```python
# Construct panda dataframe from a excel file that has a column containing the name of partners
df_partenaires = pd.read_excel("Partenaires.xlsx",sheet_name="Partenaires")

# Use the batch method to execute the qualification in batch
final_df = tool.batch(df_partners=df_partenaires, col_name_partner="Nom_Partenaire")
final_df.to_excel("Partenaires_qualified.xlsx")
```


# Release
```bash
source release.sh {NEW_VERSION}  # example 1.1.1
```

# Maintainers
[@YMURONG](https://github.com/ymurong)


# License
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
pyQuadriFlow 0.0.4
Python QuadriFlow wrapper
Requires Python: >=3.8
Keywords: quad, quadrangulation, quadriflow, remeshing

This is a Python wrapper of quadriflow used in the Blender:

![image](https://github.com/satabol/QuadriFlow/assets/14288520/56dd4baf-284f-4cbb-b866-61b434e44b1b)

See https://github.com/satabol/pyQuadriFlow

## Usage

```
pip install pyQuadriFlow
```

```
import pyQuadriFlow
pyquadriflow(
        faces,  # How many faces you need
        seed,   # random seed
        mesh_vertices, # mesh vertices [ [x0,y0,z0], [x1,y1,z1], ...]
        face_indexes,  # list of face indices as triangles: [[0,1,2],[1,2,3],...]
        flag_preserve_sharp,
        flag_preserve_boundary,
        flag_adaptive_scale,
        flag_aggresive_sat,
        flag_minimum_cost_flow
    )
```
metastim-beta 0.0.1
MetaStim: python package for DBS, enables to experimentation for various input parameters for DBS
Requires Python: >=3.8

# MetaStim DBS in Python

This is Beta release of metastim package.

Usage:

Install using pip

```
pip install metastim
```

Using 

```
from metastim import field_ann, axon_ann
```
notkinopoiskapi 1.0.0
Неофициальная библиотека для Kinopoisk API Unofficial
Keywords: api kinopoisk python

# Неофициальная библиотека для Kinopoisk API Unofficial

Простая библиотека для взаимодействия с Kinopoisk API Unofficial. Полная документация для работы с библиотекой по этому
адресу: [NotKinoPoiskAPI (GitHub)](https://devcraftclub.github.io/NotKinoPoiskAPI/NotKinoPoiskAPI/)

## Установка

Устанавливается через pip

```python3
pip install notkinopoiskapi
```

## Использование

Пакет был разделён на четыре эндпоинта.

### Фильмы и сериалы
```python3
kp = KpFilms(API_USER_KEY)
```

### Персонал и сотрудники
```python3
kp = KpStaff(API_USER_KEY)
```

### Медия новости
```python3
kp = KpMedia(API_USER_KEY)
```

### Пользователи кинопоиска
```python3
kp = KpUser(API_USER_KEY)
```

## Окружение и переменные
Ключ к API можно указывать напрямую, либо при помощи переменных окружения в вашем файле .env. Пример в env.default.

| Переменная окружения | Описание                                                                                                                                                                           | Пример                                                                  |
|----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------|
|NKPA_API_KEY| Ключ API из панели профиля. Возможно использовать несколько разных ключей через двойной пайп (\|\|).                                                                               | key1\|\|key2\|\|key3                                                    |
|NKPA_PROXY| Прокси подключения. Возможно использовать несколько разных прокси через двойной пайп (\|\|). Нужно указать тип прокси через одиночный пайп (http \| или socks5 \|) и адрес прокси. | http\|user:pass@url.com:port \|\|socks5\|user:pass@ip:port\|\|https\|ip |
|NKPA_USER_AGENT|User-Agent. Возможно использовать несколько разных агентов через двойной пайп (\|\|)|Mozilla/5.0 (Linux; Android 10; K) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Mobile Safari/537.36\|\|Mozilla/5.0 (Linux; Android 13; SM-S901U) |
|NKPA_CACHE_DIR|Пользовательская папка с кешем|/home/user/.cache/notkinopoiskapi|
|NKPA_CACHE_LIFE|Продолжительность жизни кеша в днях, по умолчанию 7 дней|7|
nonebot-plugin-antimonkey 1.1.1
一个用于自动检测和撤回包含猴子图片的QQ机器人插件。
Requires Python: <4.0,>=3.7
Keywords: nonebot, plugin, antimonkey

# NoneBot-Plugin-AntiMonkey

基于cv2和预训练的机器学习模型（TensorFlow）实现检测🐒猴子图并撤回的nonebot插件

## 声明

本项目的开发旨在提供一个技术解决方案，以应对在线群聊中出现的特定类型的刷屏行为。重要的是要强调，此举并非反映出对任何动物，特别是猴子，的不喜欢或成见。作者深知动物在自然界和文化中的重要地位，并且尊重所有生命的多样性和独特性。此项目仅针对那些利用猴子图片进行刷屏、干扰正常交流的行为。作者的目标是促进在线交流的健康和秩序，同时保护用户免受持续的信息干扰和不适内容的影响。

## 介绍

你是否被某些人高频刷的猴子图恶心过？

🐒🐒🐒

本插件能在bot是管理员的群持续检测群聊人员所发图片，判断是否为🐒图并自动撤回，值得注意的是，**撤回功能并不受tx风控影响！**

## 安装

使用nb-cli进行安装

``nb plugin install nonebot-plugin-antimonkey``


使用pip进行安装

``pip install nonebot-plugin-antimonkey``



## 使用

安装运行后，插件将自动加载并开始监控管理的群消息。

## 存在的问题

不是百分百能识别准确！特别是抽象🐒图！

偶尔会有cv2报错，可能存在于检测表情包这类较小的图片，调整尺寸时发生了错误从而无法读取图片

~~与部分插件比如“今日运势”冲突，正在加紧修复~~ 已修复！

管理员的🐒图撤不了

群主的🐒图更撤不了

## 贡献
欢迎提交Pull Request或报告Issues。

## 许可
[MIT License](LICENSE)

## 致谢
感谢google公司开发的MobileNetV2模型，这是一个由Google开发的高效的轻量级深度学习模型。更多关于MobileNetV2的信息可以在[官方论文](https://arxiv.org/abs/1801.04381)中找到

感谢低嫩群友刷的恶心吗喽🐒🐒🐒，让这个项目得以实现。