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        "description": "# Flaskerk\n\n[![Build Status](https://travis-ci.com/kemingy/flaskerk.svg?branch=master)](https://travis-ci.com/kemingy/flaskerk)\n![GitHub](https://img.shields.io/github/license/kemingy/flaskerk)\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/flaskerk)\n\nProvide OpenAPI document and validation for flask service.\n\nMainly built for Machine Learning Model services.\n\nIf you're using Falcon, check my another library [Falibrary](https://github.com/kemingy/falibrary).\n\n## Features\n\n- [x] JSON data(request&response) validation with [pydantic](https://github.com/samuelcolvin/pydantic/)\n- [x] support HTTP exceptions (default&customized)\n- [x] [OpenAPI spec](https://github.com/OAI/OpenAPI-Specification)\n- [x] [Redoc UI](https://github.com/Redocly/redoc)\n- [x] [Swagger UI](https://github.com/swagger-api/swagger-ui)\n- [x] support flask url path validation\n- [ ] support header validation\n- [ ] support cookie validation\n\n## Quick Start\n\ninstall with `pip install flaskerk` (Python 3.6+)\n\n### Simple demo\n\n```py\nfrom flask import Flask, request, jsonify\nfrom flaskerk import Flaskerk\nfrom pydantic import BaseModel\n\nclass Query(BaseModel):\n    text: str\n\napp = Flask(__name__)\napi = Flaskerk()\n\n@app.route('/api/classify')\n@api.validate(query=Query)\ndef classify():\n    print(request.query)\n    return jsonify(label=0)\n\nif __name__ == \"__main__\":\n    api.register(app)\n    app.run()\n```\n\nChanges you need to make:\n\n* create model with [`pydantic`](https://github.com/samuelcolvin/pydantic/)\n* decorate the route function with `Flaskerk.validate()`\n* specify which part you need in `validate`\n  * `query` (args in url)\n  * `data` (JSON data)\n  * `resp` (response)\n  * `x` (HTTP Exceptions)\n* register to Flask application\n\nAfter that, this library will help you validate the incoming request and provide API document in `/docs`.\n\nFor more details, check the [document](https://kemingy.github.io/flaskerk).\n\n\n### More feature\n\n```py\nfrom flask import Flask, request\nfrom pydantic import BaseModel, Schema\nfrom random import random\nfrom flaskerk import Flaskerk, HTTPException\n\napp = Flask(__name__)\napi = Flaskerk()\n\nclass Query(BaseModel):\n    text: str\n\nclass Response(BaseModel):\n    label: int\n    score: float = Schema(\n        ...,\n        gt=0,\n        lt=1,\n    )\n\nclass Data(BaseModel):\n    uid: str\n    limit: int\n    vip: bool\n\ne403 = HTTPException(code=403, msg='lucky for you')\n\n@app.route('/api/predict/<string(length=2):source>/<string(length=2):target>', methods=['POST'])\n@api.validate(query=Query, data=Data, resp=Response, x=[e403])\ndef predict(source, target):\n    print(f'=> from {source} to {target}')  # path\n    print(f'Data: {request.json_data}')  # Data\n    print(f'Query: {request.query}')  # Query\n    if random() < 0.5:\n        e403.abort()\n    return Response(label=int(10 * random()), score=random())\n\nif __name__ == '__main__':\n    api.register(app)\n    app.run()\n```\n\ntry it with `http POST ':5000/api/predict/zh/en?text=hello' uid=0b01001001 limit=5 vip=true`\n\nOpen the docs in http://127.0.0.1:5000/docs .\n\nFor more examples, check [examples](/examples).\n\n## FAQ\n\n> Can I just do the validation without generating API document?\n\nSure. If you don't register it to Flask application, there won't be document routes.\n\n> \n\n",
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