@app.route('/predict-iris')
def predict_iris():

    # Load data
    iris = load_iris()

    # print("Loaded iris", iris)

    # Fit our model
    logreg = LogisticRegression()
    model  = logreg.fit(iris['data'], iris['target'])
    model.predict_proba(iris['data'])

    # Parameters from GET request
    sepal_length  = request.args.get("sepal_length")
    sepal_width   = request.args.get("sepal_width")
    petal_length  = request.args.get("petal_length")
    petal_width   = request.args.get("petal_width")

    # To predict
    to_predict = np.array([
        float(sepal_length),
        float(sepal_width),
        float(petal_length),
        float(petal_width)
    ])

    print(
        "My input parameters are:",
        sepal_length, sepal_width,
        petal_length, petal_width
    )

    if all([sepal_length, sepal_width, petal_length, petal_width]):
        result = {
            "message": "OK",
            "predict": model.predict(to_predict).tolist(),
            "probas": model.predict_proba(to_predict).tolist()
        }
    else:
        result = {
            "message": "Please set input!"
        }

    return jsonify(result)