This particular solution fits a model inline upon each request to the endpoint. This is not ideal so one should load outside the Flask route method before prediction. More ideally, the model should be loaded through a serialization library like joblib.

inline_model_flask_route_solution.py

@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)