semvec/pypi-packages huggingface dataset:
quickcrud 0.1.0
CLI to automatically generate FastAPI APIs from a SQL schema.
License: MIT
Requires Python: <4.0,>=3.12
Keywords: fastapi, crud, sql, docker

# QuickCRUD

QuickCRUD is a command-line interface (CLI) tool designed to automatically generate FastAPI APIs from a provided SQL schema. By using this tool, developers can quickly generate CRUD (Create, Read, Update, Delete) operations for each table in the database schema, saving valuable time when setting up backend APIs.

## Features

- Automatically generates FastAPI CRUD operations for SQL schema tables.
- Supports PostgreSQL, MySQL, and SQLite databases.
- Generates API routes for basic operations: GET, POST, PUT, DELETE.
- Creates a ready-to-use Docker Compose setup with two containers: one for the FastAPI application and another for the database.

## Current Status

As of now, **QuickCRUD** is in its **early stages**. 

## Installation

To install **QuickCRUD**:


```
pip install quickcrud
```

## Usage

To generate FastAPI APIs from your SQL schema, simply run the following command:

```
quickcrud --schema path/to/your/schema.sql --db-type postgres
```

Replace `postgres` with `mysql` or `sqlite` depending on your database type.

## Development

To contribute to QuickCRUD, clone the repository and install the dependencies:

```
git clone https://github.com/wendersoon/QuickCRUD
cd QuickCRUD
poetry install
```

You can run tests, format the code with Black, and check for linting issues using the pre-configured tools.
mlend 1.0.0.4
MLEnd Datasets
License: MIT
Requires Python: >=3.5
Keywords: MLEndDatasets Datasets MachineLearning PrinciplesOfMachineLearning Data AI

# MLEnd Datasets

### Links: **[Homepage](https://MLEndDatasets.github.io)** | **[Documentation](https://mlend.readthedocs.io/)** | **[Github](https://github.com/MLEndDatasets)**  |  **[PyPi - project](https://pypi.org/project/mlend/)** |     _ **Installation:** [pip install mlend](https://pypi.org/project/mlend/)
-----

-----

## Installation

**Requirement**:  numpy, matplotlib, scipy.stats, spkit

### with pip

```
pip install mlend
```

### update with pip

```
pip install mlend --upgrade
```


## Download data :  Spoken Numerals

```
import mlend
from mlend import download_spoken_numerals, spoken_numerals_load


datadir = download_spoken_numerals(save_to = '../Data/MLEnd', subset = {},verbose=1,overwrite=False)

```

## Create Training and Testing Sets

```
TrainSet, TestSet, MAPs = spoken_numerals_load(datadir_main = datadir, train_test_split = 'Benchmark_B', verbose=1,encode_labels=True)

```

## Download data :  London Sounds


```
import mlend
from mlend import download_london_sounds, london_sounds_load


datadir = download_london_sounds(save_to = '../Data/MLEnd', subset = {},verbose=1,overwrite=False)

```


## Download data :  Hums and Whistles


```
import mlend
from mlend import download_hums_whistles, hums_whistles_load


datadir = download_hums_whistles(save_to = '../Data/MLEnd', subset = {},verbose=1,overwrite=False)

```


## Download data :  Yummy


```
import mlend
from mlend import download_yummy, yummy_load

subset = {}

datadir = download_yummy(save_to = '../MLEnd', subset = subset,verbose=1,overwrite=False)

```




# Contacts:
* **Jesús Requena Carrión**
* Queen Mary University of London

* **Nikesh Bajaj**
* Queen Mary University of London
* n.bajaj[AT]qmul.ac.uk, n.bajaj[AT]imperial[dot]ac[dot]uk

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