๐ŸŒŽ The Modern Declarative Data Flow Framework for the AI Empowered Generation.

Overview

๐ŸŒŽ JSONClasses Pypi Python Version License PR Welcome

JSONClasses is a declarative data flow pipeline and data graph framework.

Official Website: https://www.jsonclasses.com

Official Documentation: https://docs.jsonclasses.com

๐Ÿš— Features

Features
๐Ÿ›  Data Modeling Declarative data model with Python type hints
๐Ÿธ Data Sanitization Two strictness modes
๐Ÿฉบ Data Validation Descriptive data validation rules without even a line of code
๐Ÿงฌ Data Transformation Intuitive with modifier pipelines
๐Ÿฆ– Data Presentation Custom key encoding & decoding strategies
๐ŸŒ Data Graphing Models are linked with each other on the same graph
๐Ÿ„โ€โ™‚๏ธ Data Querying Well-designed protocols and implementations for databases
๐Ÿš€ Synthesized CRUD Only with a line of code
๐Ÿ‘ฎโ€โ™€๏ธ Session & Authorization Builtin support for session and authorization
๐Ÿ” Permission System Supports both object level and field level
๐Ÿ“ File Uploading A configuration is enough for file uploading
๐Ÿ“ฆ Data Seeder Declarative named graph relationship

๐ŸŽ Getting Started

Prerequisites

Python >= 3.10 is required. You can download it here.

Install JSONClasses

Install JSONClasses is simple with pip.

pip install jsonclasses

Install Components

Depends on your need, you can install ORM integration and HTTP library integration with the following commands.

pip install jsonclasses-pymongo jsonclasses-server

๐ŸŽน Examples

Business Logic Examples

Example 1: Dating App Users

Let's say, you are building the base user functionality for a cross-platform dating app.

The product requirements are:

  1. Unique phone number is required
  2. Password should be secure, encrypted, hidden from response
  3. Gender cannot be changed after set
  4. This product is adult only
  5. User intro should be brief

Let's transform the requirements into code.

from jsonclasses import jsonclass, types
from jsonclasses_pymongo import pymongo
from jsonclasses_server import api


@api
@pymongo
@jsonclass
class User:
    id: str = types.readonly.str.primary.mongoid.required
    phone_no: str = types.str.unique.index.match(local_phone_no_regex).required #1
    email: str = types.str.match(email_regex)
    password: str = types.str.writeonly.length(8, 16).match(secure_password_regex).transform(salt).required #2
    nickname: str = types.str.required
    gender: str = types.str.writeonce.oneof(['male', 'female']) #3
    age: int = types.int.min(18).max(100) #4
    intro: str = types.str.truncate(500) #5
    created_at: datetime = types.readonly.datetime.tscreated.required
    updated_at: datetime = types.readonly.datetime.tsupdated.required

โšฝ๏ธ Database & HTTP Library Integrations

๐Ÿฆธ Contributing

  • File a bug report. Be sure to include information like what version of YoMo you are using, what your operating system is, and steps to recreate the bug.
  • Suggest a new feature.

๐Ÿคน๐Ÿปโ€โ™€๏ธ Feedback

Any questions or good ideas, please feel free to come to our Discussion. Any feedback would be greatly appreciated!

License

MIT License

Owner
Fillmula Inc.
Fillmula Inc.
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

Autoformer (NeurIPS 2021) Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting Time series forecasting is a c

THUML @ Tsinghua University 847 Jan 08, 2023
A TensorFlow implementation of the Mnemonic Descent Method.

MDM A Tensorflow implementation of the Mnemonic Descent Method. Mnemonic Descent Method: A recurrent process applied for end-to-end face alignment G.

123 Oct 07, 2022
A PyTorch implementation of "CoAtNet: Marrying Convolution and Attention for All Data Sizes".

CoAtNet Overview This is a PyTorch implementation of CoAtNet specified in "CoAtNet: Marrying Convolution and Attention for All Data Sizes", arXiv 2021

Justin Wu 268 Jan 07, 2023
Attack on Confidence Estimation algorithm from the paper "Disrupting Deep Uncertainty Estimation Without Harming Accuracy"

Attack on Confidence Estimation (ACE) This repository is the official implementation of "Disrupting Deep Uncertainty Estimation Without Harming Accura

3 Mar 30, 2022
Process JSON files for neural recording sessions using Medtronic's BrainSense Percept PC neurostimulator

percept_processing This code processes JSON files for streamed neural data using Medtronic's Percept PC neurostimulator with BrainSense Technology for

Maria Olaru 3 Jun 06, 2022
Implements MLP-Mixer: An all-MLP Architecture for Vision.

MLP-Mixer-CIFAR10 This repository implements MLP-Mixer as proposed in MLP-Mixer: An all-MLP Architecture for Vision. The paper introduces an all MLP (

Sayak Paul 51 Jan 04, 2023
PyTorch implementation of the paper:A Convolutional Approach to Melody Line Identification in Symbolic Scores.

Symbolic Melody Identification This repository is an unofficial PyTorch implementation of the paper:A Convolutional Approach to Melody Line Identifica

Sophia Y. Chou 3 Feb 21, 2022
Code release for NeurIPS 2020 paper "Co-Tuning for Transfer Learning"

CoTuning Official implementation for NeurIPS 2020 paper Co-Tuning for Transfer Learning. [News] 2021/01/13 The COCO 70 dataset used in the paper is av

THUML @ Tsinghua University 35 Sep 23, 2022
An official source code for "Augmentation-Free Self-Supervised Learning on Graphs"

Augmentation-Free Self-Supervised Learning on Graphs An official source code for Augmentation-Free Self-Supervised Learning on Graphs paper, accepted

Namkyeong Lee 59 Dec 01, 2022
CAPITAL: Optimal Subgroup Identification via Constrained Policy Tree Search

CAPITAL: Optimal Subgroup Identification via Constrained Policy Tree Search This repository is the official implementation of CAPITAL: Optimal Subgrou

Hengrui Cai 0 Oct 19, 2021
Underwater image enhancement

LANet Our work proposes an adaptive learning attention network (LANet) to solve the problem of color casts and low illumination in underwater images.

LiuShiBen 7 Sep 14, 2022
Gans-in-action - Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks

GANs in Action by Jakub Langr and Vladimir Bok List of available code: Chapter 2: Colab, Notebook Chapter 3: Notebook Chapter 4: Notebook Chapter 6: C

GANs in Action 914 Dec 21, 2022
meProp: Sparsified Back Propagation for Accelerated Deep Learning

meProp The codes were used for the paper meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting (ICML 2017) [pdf]

LancoPKU 107 Nov 18, 2022
Realtime Face Anti Spoofing with Face Detector based on Deep Learning using Tensorflow/Keras and OpenCV

Realtime Face Anti-Spoofing Detection ๐Ÿค– Realtime Face Anti Spoofing Detection with Face Detector to detect real and fake faces Please star this repo

Prem Kumar 86 Aug 03, 2022
GANSketchingJittor - Implementation of Sketch Your Own GAN in Jittor

GANSketching in Jittor Implementation of (Sketch Your Own GAN) in Jittor(่ฎกๅ›พ). Or

Bernard Tan 10 Jul 02, 2022
Localized representation learning from Vision and Text (LoVT)

Localized Vision-Text Pre-Training Contrastive learning has proven effective for pre- training image models on unlabeled data and achieved great resul

Philip Mรผller 10 Dec 07, 2022
This repository compare a selfie with images from identity documents and response if the selfie match.

aws-rekognition-facecompare This repository compare a selfie with images from identity documents and response if the selfie match. This code was made

1 Jan 27, 2022
PyTorch implementation of "Representing Shape Collections with Alignment-Aware Linear Models" paper.

deep-linear-shapes PyTorch implementation of "Representing Shape Collections with Alignment-Aware Linear Models" paper. If you find this code useful i

Romain Loiseau 27 Sep 24, 2022
Implementation of "A Deep Learning Loss Function based on Auditory Power Compression for Speech Enhancement" by pytorch

This repository is used to suspend the results of our paper "A Deep Learning Loss Function based on Auditory Power Compression for Speech Enhancement"

ScorpioMiku 19 Sep 30, 2022
Supplementary code for SIGGRAPH 2021 paper: Discovering Diverse Athletic Jumping Strategies

SIGGRAPH 2021: Discovering Diverse Athletic Jumping Strategies project page paper demo video Prerequisites Important Notes We suspect there are bugs i

54 Dec 06, 2022