semvec/pypi-packages huggingface dataset:
fastapi_sqlmodel_crud 0.1.0
fastapi-sqlmodel-crud is a program which is based on fastapi+sqlmodel and used to quickly build the Create, Read, Update, Delete generic API interface.
Requires Python: >=3.6.1
Keywords: sqlmodel,fastapi-sqlmodel-crud,fastapi-amis-admin,fastapi-crud,fastapi-sqlmodel

[## 项目介绍

`fastapi-sqlmodel-crud`是一个基于`FastAPI`+`SQLModel`, 用于快速构建Create,Read,Update,Delete通用API接口的项目.


## 安装

```bash
pip install fastapi-sqlmodel-crud 
```

## 简单示例

**main.py**:

```python
from typing import Optional, Generator, Any
from fastapi import FastAPI
from sqlalchemy.ext.asyncio import AsyncEngine, create_async_engine
from sqlalchemy.orm import sessionmaker
from sqlmodel import SQLModel, Field
from sqlmodel.ext.asyncio.session import AsyncSession
from fastapi_amis_admin.crud import SQLModelCrud


#  1.创建SQLModel模型
class Article(SQLModel, table=True):
    id: int = Field(default=None, primary_key=True, nullable=False)
    title: str = Field(title='ArticleTitle', max_length=200)
    description: Optional[str] = Field(default='', title='ArticleDescription', max_length=400)
    status: bool = Field(None, title='status')
    content: str = Field(title='ArticleContent')


# 2.创建 AsyncSession
database_url = 'sqlite+aiosqlite:///admisadmin.db'
engine: AsyncEngine = create_async_engine(database_url, future=True, pool_recycle=1200)
session_maker: sessionmaker = sessionmaker(engine, class_=AsyncSession,
                                           expire_on_commit=False, autocommit=False, autoflush=False)


async def session_factory() -> Generator[AsyncSession, Any, None]:
    async with session_maker() as session:
        yield session


# 3. 注册crud路由
article_crud = SQLModelCrud(model=Article, session_factory=session_factory).register_crud()

app = FastAPI(debug=True)

# 4. 包含路由器
app.include_router(article_crud.router)


# 5. 创建模型数据库表(首次运行)
@app.on_event("startup")
async def startup():
    async with engine.begin() as conn:
        await conn.run_sync(SQLModel.metadata.create_all)

```


## 开发文档

- https://docs.gh.amis.work/crud/SQLModelCrud/



## 依赖项目

- [Fastapi](https://fastapi.tiangolo.com)

- [SQLModel](https://sqlmodel.tiangolo.com/)


## 许可协议

该项目遵循 Apache2.0 许可协议。
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amis 0.1.1
amis是Baidu团队开发的一个低代码前端框架，它使用JSON配置来生成页面，可以减少页面开发工作量，极大提升效率。python amis基于baidu amis,对amis数据结构通过pydantic转换为对应的python数据模型,并添加部分常用方法.
Requires Python: >=3.6.1
Keywords: amis,fastapi-amis-admin

## Amis介绍

[amis](https://github.com/baidu/amis) 是`Baidu`团队开发的一个低代码前端框架，它使用 `JSON` 配置来生成页面，可以减少页面开发工作量，极大提升效率。`python amis`基于`baidu amis` , 对`amis`数据结构通过[pydantic](https://pydantic-docs.helpmanual.io) 转换为对应的python数据模型,并添加部分常用方法.



## Amis亮点

- **不需要懂前端**：在百度内部，大部分 amis 用户之前从来没写过前端页面，也不会 `JavaScript`，却能做出专业且复杂的后台界面，这是所有其他前端 UI 库都无法做到的；
- **不受前端技术更新的影响**：百度内部最老的 amis 页面是 6 年多前创建的，至今还在使用，而当年的 `Angular/Vue/React` 版本现在都废弃了，当年流行的 `Gulp` 也被 `Webpack` 取代了，如果这些页面不是用 amis，现在的维护成本会很高；
- **享受 amis 的不断升级**：amis 一直在提升细节交互体验，比如表格首行冻结、下拉框大数据下不卡顿等，之前的 JSON 配置完全不需要修改；
- 可以 **完全** 使用 [可视化页面编辑器](https://aisuda.github.io/amis-editor-demo/) 来制作页面：一般前端可视化编辑器只能用来做静态原型，而 amis 可视化编辑器做出的页面是可以直接上线的。
- **提供完整的界面解决方案**：其它 UI 框架必须使用 JavaScript 来组装业务逻辑，而 amis 只需 JSON 配置就能完成完整功能开发，包括数据获取、表单提交及验证等功能，做出来的页面不需要经过二次开发就能直接上线；
- **大量内置组件（120+），一站式解决**：其它 UI 框架大部分都只有最通用的组件，如果遇到一些稍微不常用的组件就得自己找第三方，而这些第三方组件往往在展现和交互上不一致，整合起来效果不好，而 amis 则内置大量组件，包括了富文本编辑器、代码编辑器、diff、条件组合、实时日志等业务组件，绝大部分中后台页面开发只需要了解 amis 就足够了；
- **支持扩展**：除了低代码模式，还可以通过 [自定义组件](https://baidu.gitee.io/amis/zh-CN/docs/extend/internal) 来扩充组件，实际上 amis 可以当成普通 UI 库来使用，实现 90% 低代码，10% 代码开发的混合模式，既提升了效率，又不失灵活性；
- **容器支持无限级嵌套**：可以通过嵌套来满足各种布局及展现需求；
- **经历了长时间的实战考验**：amis 在百度内部得到了广泛使用，**在 6 年多的时间里创建了 5 万页面**，从内容审核到机器管理，从数据分析到模型训练，amis 满足了各种各样的页面需求，最复杂的页面有超过 1 万行 JSON 配置。



## 安装

```bash
pip install amis 
```



## 简单示例

**main.py**:

```python
from amis.components import Page

page = Page(title='标题', body='Hello World!')
# 输出为json
print(page.amis_json())
# 输出为dict
print(page.amis_dict())
# 输出页面html
print(page.amis_html())
```



## 开发文档

参考: [Amis官方文档](https://baidu.gitee.io/amis/zh-CN/docs/index)



## 依赖项目

- [pydantic](https://pydantic-docs.helpmanual.io/) 

- [amis](https://baidu.gitee.io/amis) 

  

## 许可协议

该项目遵循 Apache2.0 许可协议。
Transkit 1.0.0
Transkit API library
Requires Python: >=3.6

# Transkit API libraries

In this repository, you can find example ready-to-use libraries. For more technical information, please visit https://api.transkit.vicomtech.org/doc/

### Functions in "Transkit" package

- getOnlineTranscription(audioBase64, pipeline). Returns a JSON
- getOfflineTranscription(audioURL, pipeline, config = None). Returns a JSON
- getOfflineTranscriptionStatus(jobid). Returns a JSON
- getQuota(). Returns a JSON
- addTag(tag).
- setTags(tags).

An exception will be thrown if any error detected, so we recommend to use try/except statements.

### Example

Install the package

> pip install Transkit

Import:
`from Transkit import Transkit`

Initialize with your api key:

`transkitapi = Transkit("apikey")`

Make an online transcription:
```
with open("file.mp3", "rb") as f:
    encodedFile = base64.b64encode(f.read()).decode()
    transcriptionResult = transkit.getOnlineTranscription(encodedFile, "myPipeline")
    print("Transcription result: ", transcriptionResult)
```

Make an offline transcription:
```
transcriptionResult = transkit.getOfflineTranscription("https://myvideo.url/path/here", "myPipeline")
```

Make an offline transcription status request:
```
transcriptionResult = transkit.getOfflineTranscriptionStatus("my-job-id")
```
bitbanker-sdk 0.0.2
Bitbanker.org API client
Requires Python: >=3.7.0,<4.0.0

bitbanker-sdk
-----------------

.. image:: https://img.shields.io/pypi/v/bitbanker-sdk.svg
    :target: https://pypi.python.org/pypi/bitbanker-sdk

.. image:: https://img.shields.io/pypi/pyversions/bitbanker-sdk.svg
    :target: https://pypi.python.org/pypi/bitbanker-sdk

.. image:: https://codecov.io/gh/melnikovsa/python-bitbanker-sdk/branch/main/graph/badge.svg
    :target: https://app.codecov.io/gh/melnikovsa/python-bitbanker-sdk

.. image:: https://github.com/melnikovsa/python-bitbanker-sdk/actions/workflows/tests.yml/badge.svg
    :target: https://github.com/melnikovsa/python-bitbanker-sdk/actions/workflows/tests.yml

.. image:: https://github.com/melnikovsa/python-bitbanker-sdk/actions/workflows/pypi.yml/badge.svg
    :target: https://github.com/melnikovsa/python-bitbanker-sdk/actions/workflows/pypi.yml

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
    :target: https://github.com/python/black


This is an sync/async Python `Bitbanker`__ API client.

.. _Bitbanker: https://bitbanker.org/

__ Bitbanker_


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

The project is available on PyPI. Simply run::

    $ pip install bitbanker-sdk


Usage
-----
With sync python application::

    from bitbanker_sdk import BitbankerClient
    from bitbanker_sdk import InvoiceData
    from bitbanker_sdk import Currency

    client = BitbankerClient(api_key="<your bitbanker api key>")
    invoice_data = InvoiceData(
            amount=1000,
            payment_currencies=[Currency.ETH, Currency.BTC],
            description='invoice description',
            header='invoice header'
        )

    response = client.create_invoice(invoice_data=invoice_data)
    print(response.link)

With async python application::

    from bitbanker_sdk import AsyncBitbankerClient
    from bitbanker_sdk import InvoiceData
    from bitbanker_sdk import Currency

    client = AsyncBitbankerClient(api_key="<your bitbanker api key>")
    invoice_data = InvoiceData(
            amount=1000,
            payment_currencies=[Currency.ETH, Currency.BTC],
            description='invoice description',
            header='invoice header'
        )

    response = await client.create_invoice(invoice_data=invoice_data)
    print(response.link)
mhealth-dataviz 0.6.3
A package to visualise data generated from cluster script for MHealth Lab
Requires Python: >=3.7

Visualizing a year of data obtained from a smartwatch. Project for Northeastern MHealth Research group.

version 0.6.0 

# Installation 
**Python >= 3.7**

```
pip install mhealthlab-dataviz 
```

#Usage (CLI Application)

#### Linux/Mac
```
./main.py <path to processed data folder> 
```

####Windows
```
python main.py <path to processed data folder> 
```


# Output 
Outputs the year of data visualized as a .jpeg
cleaninsights 0.1
CleanInsights gives developers a safe, sustainable, and secure way to gather insights about their users using cutting edge techniques like differential privacy, onion routing, certificate pinning and more.
Keywords: privacy metrics analytics

Clean Insights Python SDK 📏🐍💡
===============================

Privacy-preserving measurement and analytics for Python.

[![Gitlab pipeline status](https://img.shields.io/gitlab/pipeline/cleaninsights/clean-insights-python-sdk/develop)](https://gitlab.com/cleaninsights/clean-insights-python-sdk/-/pipelines?page=1&scope=all&ref=develop)
[![Docs](https://img.shields.io/badge/docs-latest-blueviolet)](https://cleaninsights.gitlab.io/clean-insights-python-sdk/)
[![PyPI](https://img.shields.io/pypi/v/cleaninsights)](https://pypi.org/project/cleaninsights/)
[![License](https://img.shields.io/badge/License-BSD%202--Clause-orange.svg)](https://gitlab.com/cleaninsights/clean-insights-python-sdk/-/blob/7e298542366af909e439746abba03c285b3d4cdc/COPYING)

----

[Clean Insights](https://cleaninsights.org/) gives developers a way to plug
into a secure, private measurement platform. It is focused on assisting in
answering key questions about app usage patterns, and not on enabling invasive
surveillance of all user habits. Our approach provides programmatic levers to
pull to cater to specific use cases and privacy needs. It also provides methods
for user interactions that are ultimately empowering instead of alienating.

This Python SDK is part of the wider Clean Insights ecosystem that includes
SDKs in other languages, a consent user interface toolkit and server plugins.
All these tools can be found on the [Clean Insights Developers
Page](https://cleaninsights.org/dev).

# Online Documentation

Developer documentation is available at
https://cleaninsights.gitlab.io/clean-insights-python-sdk/.

# Copyright

(C) Iain R. Learmonth 2021. See `COPYING` for permissions.