root/alpine313/: jsf-0.11.2 metadata and description

Creates fake JSON files from a JSON schema

author	ghandic
classifiers	Programming Language :: Python :: 3 License :: OSI Approved :: MIT License Operating System :: OS Independent
description_content_type	text/markdown
keywords	JSON Schema, Fake data, Test data, Schema, JSON, Faker, Hypothesis, Rapid Prototype, Data contract
metadata_version	2.1
requires_dist	faker (>=15.3.4) jsonschema (>=4.17.3) pydantic (>=2.0.0) rstr (>=3.2.0) smart-open[http] (>=6.3.0) typing-extensions (>=4.9.0) typer (>=0.7.0) ; extra == 'cli'
requires_python	>=3.8

File	Tox results	History
jsf-0.11.2-py3-none-any.whl Size 48 KB Type Python Wheel Python 3		Uploaded to root/alpine313 by root 2025-09-30 09:03:01

jsf

Use jsf along with fake data generators to provide consistent and meaningful fake data for your system.

Main Features

Provides out of the box data generation from any JSON schema 📦
Extendable custom data providers using any lambda functions 🔗
Multi level state for dependant data (eg multiple objects sharing value, such as children with same surname) 🤓
Inbuilt validation of fake JSON produced ✅
In memory conversion from JSON Schema to Pydantic Models with generated examples 🤯
Seamless integration with FastAPI 🚀

Installation

$ pip install jsf

---> 100%

Usage

Basic 😊

from jsf import JSF

faker = JSF(
    {
        "type": "object",
        "properties": {
            "name": {"type": "string", "$provider": "faker.name"},
            "email": {"type": "string", "$provider": "faker.email"},
        },
        "required": ["name", "email"],
    }
)

fake_json = faker.generate()

Results in ...

{
    'name': 'Jesse Phillips', 
    'email': 'xroberson@hotmail.com'
}

From JSON file 📁

from jsf import JSF

faker = JSF.from_json("demo-schema.json")
fake_json = faker.generate()

Or run straight from the commandline...

Native install

pip install jsf[cli]
jsf --schema jsf/tests/data/custom.json --instance wow.json

Docker

docker run -v $PWD:/data challisa/jsf jsf --schema /data/custom.json --instance /data/example.json

FastAPI Integration 🚀

Create a file main.py with:

from jsf import JSF
from fastapi import FastAPI

app = FastAPI(docs_url="/")
generator = JSF.from_json("custom.json")


@app.get("/generate", response_model=generator.pydantic())
def read_root():
    return generator.generate()

Run the server with:

$ uvicorn main:app --reload

INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO:     Started reloader process [28720]
INFO:     Started server process [28722]
INFO:     Waiting for application startup.
INFO:     Application startup complete.

Navigate to http://127.0.0.1:8000 and check out your endpoint. Notice the following are all automatically created: