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.
[MICCAI'20] AlignShift: Bridging the Gap of Imaging Thickness in 3D Anisotropic Volumes

AlignShift NEW: Code for our new MICCAI'21 paper "Asymmetric 3D Context Fusion for Universal Lesion Detection" will also be pushed to this repository

Medical 3D Vision 42 Jan 06, 2023
ObjectDrawer-ToolBox: a graphical image annotation tool to generate ground plane masks for a 3D object reconstruction system

ObjectDrawer-ToolBox is a graphical image annotation tool to generate ground plane masks for a 3D object reconstruction system, Object Drawer.

77 Jan 05, 2023
Transformer model implemented with Pytorch

transformer-pytorch Transformer model implemented with Pytorch Attention is all you need-[Paper] Architecture Self-Attention self_attention.py class

Mingu Kang 12 Sep 03, 2022
Using image super resolution models with vapoursynth and speeding them up with TensorRT

vs-RealEsrganAnime-tensorrt-docker Using image super resolution models with vapoursynth and speeding them up with TensorRT. Also a docker image since

4 Aug 23, 2022
This repository contains all the code and materials distributed in the 2021 Q-Programming Summer of Qode.

Q-Programming Summer of Qode This repository contains all the code and materials distributed in the Q-Programming Summer of Qode. If you want to creat

Sammarth Kumar 11 Jun 11, 2021
Get the partition that a file belongs and the percentage of space that consumes

tinos_eisai_sy Get the partition that a file belongs and the percentage of space that consumes (works only with OSes that use the df command) tinos_ei

Konstantinos Patronas 6 Jan 24, 2022
Adversarial examples to the new ConvNeXt architecture

Adversarial examples to the new ConvNeXt architecture To get adversarial examples to the ConvNeXt architecture, run the Colab: https://github.com/stan

Stanislav Fort 19 Sep 18, 2022
CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images

CurriculumNet Introduction This repo contains related code and models from the ECCV 2018 CurriculumNet paper. CurriculumNet is a new training strategy

156 Jul 04, 2022
Official Pytorch implementation of "CLIPstyler:Image Style Transfer with a Single Text Condition"

CLIPstyler Official Pytorch implementation of "CLIPstyler:Image Style Transfer with a Single Text Condition" Environment Pytorch 1.7.1, Python 3.6 $ c

203 Dec 30, 2022
Cross Quality LFW: A database for Analyzing Cross-Resolution Image Face Recognition in Unconstrained Environments

Cross-Quality Labeled Faces in the Wild (XQLFW) Here, we release the database, evaluation protocol and code for the following paper: Cross Quality LFW

Martin Knoche 10 Dec 12, 2022
Pytorch Implementation of Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations

NANSY: Unofficial Pytorch Implementation of Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations Notice Papers' D

Dongho Choi ์ตœ๋™ํ˜ธ 104 Dec 23, 2022
Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network

Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network This is the official implementation of

azad 2 Jul 09, 2022
Implementation of accepted AAAI 2021 paper: Deep Unsupervised Image Hashing by Maximizing Bit Entropy

Deep Unsupervised Image Hashing by Maximizing Bit Entropy This is the PyTorch implementation of accepted AAAI 2021 paper: Deep Unsupervised Image Hash

62 Dec 30, 2022
Source code for EquiDock: Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (ICLR 2022)

Source code for EquiDock: Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (ICLR 2022) Please cite "Independent SE(3)-Equivar

Octavian Ganea 154 Jan 02, 2023
Code for Towards Streaming Perception (ECCV 2020) :car:

sAP โ€” Code for Towards Streaming Perception ECCV Best Paper Honorable Mention Award Feb 2021: Announcing the Streaming Perception Challenge (CVPR 2021

Martin Li 85 Dec 22, 2022
An implementation of the BADGE batch active learning algorithm.

Batch Active learning by Diverse Gradient Embeddings (BADGE) An implementation of the BADGE batch active learning algorithm. Details are provided in o

125 Dec 24, 2022
Visualization toolkit for neural networks in PyTorch! Demo -->

FlashTorch A Python visualization toolkit, built with PyTorch, for neural networks in PyTorch. Neural networks are often described as "black box". The

Misa Ogura 692 Dec 29, 2022
Benchmark for the generalization of 3D machine learning models across different remeshing/samplings of a surface.

Discretization Robust Correspondence Benchmark One challenge of machine learning on 3D surfaces is that there are many different representations/sampl

Nicholas Sharp 10 Sep 30, 2022
PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning.

neural-combinatorial-rl-pytorch PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning. I have implemented the basic

Patrick E. 454 Jan 06, 2023
Official implementation of the network presented in the paper "M4Depth: A motion-based approach for monocular depth estimation on video sequences"

M4Depth This is the reference TensorFlow implementation for training and testing depth estimation models using the method described in M4Depth: A moti

Michaรซl Fonder 76 Jan 03, 2023