flaskerk 0.3.7

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Flaskerk

Provide OpenAPI document and validation for flask service.

Mainly built for Machine Learning Model services.

Features

• 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

Quick Start

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(app)

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__':
    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.