Provide OpenAPI document and validation for flask service.
Mainly built for Machine Learning Model services.
If you're using Falcon, check my another library Falibrary.
- JSON data(request&response) validation with pydantic
- support HTTP exceptions (default&customized)
- OpenAPI spec
- Redoc UI
- Swagger UI
- support flask url path validation
- support header validation
- support cookie validation
install with pip install flaskerk (Python 3.6+)
from flask import Flask, request
from pydantic import BaseModel, Schema
from random import random
from flaskerk import Flaskerk, HTTPException
app = Flask(__name__)
api = Flaskerk()
class Query(BaseModel):
text: str
class Response(BaseModel):
label: int
score: float = Schema(
...,
gt=0,
lt=1,
)
class Data(BaseModel):
uid: str
limit: int
vip: bool
e403 = HTTPException(code=403, msg='lucky for you')
@app.route('/api/predict/<string(length=2):source>/<string(length=2):target>', methods=['POST'])
@api.validate(query=Query, data=Data, resp=Response, x=[e403])
def predict(source, target):
print(f'=> from {source} to {target}') # path
print(f'Data: {request.json_data}') # Data
print(f'Query: {request.query}') # Query
if random() < 0.5:
e403.abort()
return Response(label=int(10 * random()), score=random())
if __name__ == '__main__':
api.register(app)
app.run()try it with http POST ':5000/api/predict/zh/en?text=hello' uid=0b01001001 limit=5 vip=true
Open the docs in http://127.0.0.1:5000/docs .
For more examples, check examples.