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ds_flask_app.py
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ds_flask_app.py
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from sklearn.externals import joblib
import numpy as np
from flask import Flask, request
from flasgger import Swagger
clf = joblib.load('models/svm.pkl')
app = Flask(__name__)
template = {
"info": {
"title": "Iris prediction API",
"version": "1.0"
}
}
swagger = Swagger(app, template=template)
@app.route('/predict')
def predict_iris():
"""Endpoint for iris prediction
---
parameters:
- name: sepal_length
in: query
type: number
required: true
- name: sepal_width
in: query
type: number
required: true
- name: petal_width
in: query
type: number
required: true
- name: petal_length
in: query
type: number
required: true
responses:
200:
description: Value of prediction
type: number
"""
s_len = request.args.get('sepal_length')
s_wid = request.args.get('sepal_width')
p_len = request.args.get('petal_length')
p_wid = request.args.get('petal_width')
pred = clf.predict(np.array([[s_len, s_wid, p_len, p_wid]]))
return str(pred[0])
if __name__ == '__main__':
app.run(host='127.0.0.1', port=8080)