demo_bottle.py

from sklearn import datasets
from sklearn.linear_model.logistic import LogisticRegression
import bottle
from bottle import request

iris_ds = datasets.load_iris()

# deux premières features pour simplifier
X = iris_ds.data[:, :2]
y = iris_ds.target

lr = LogisticRegression()
lr.fit(X, y)

app = bottle.Bottle()


@app.post('/iris')
def classify_iris():
    iris = request.json
    print(iris)
    predict = int(lr.predict([iris])[0])
    print(predict)
    return {"iris_class": predict}


if __name__ == '__main__':
    app.run(host='0.0.0.0',
            port=8980)

test_request.sh

curl -X POST localhost:8980/iris -H "Content-Type: application/json" -d "[5.9, 3.0]"