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
```console
$ 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
```bash
pip install jsf[cli]
jsf --schema jsf/tests/data/custom.json --instance wow.json
```
#### Docker
```bash
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:
```console
$ 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](http://127.0.0.1:8000) and check out your endpoint. Notice the following are all automatically created:
- Schema with descriptions and examples
- Example response
- Data generation by clicking "try it out"
Partially supported features
- string
contentMediaType- only a subset of these are supported, however they can be expanded within this file
Credits
- This repository is a Python port of json-schema-faker with some minor differences in implementation.