Flask route solution from a past student "Ian" that works with a loaded model based on a DecisionTreeClassifier instance.

ian_flask_setosa.py

@app.route('/ian')
def ian():

    from sklearn.datasets import load_iris
    import pandas as pd

    data = load_iris()
    df = pd.DataFrame(data['data'], columns=['sepal_len', 'sepal_width', 'petal_lengh', 'petal_width'])
    y = data['target']

    from sklearn.tree import DecisionTreeClassifier
    model = DecisionTreeClassifier(criterion='gini', random_state=42)

    input_sepal_len = request.args.get("sepal_len")
    input_sepal_width = request.args.get("sepal_width")
    input_petal_lengh = request.args.get("petal_lengh")
    input_petal_width = request.args.get("petal_width")

    if input_sepal_len:
        model.fit(df, y)
        list_of_data_to_fit =  [
            float(input_sepal_len),
            float(input_sepal_width),
            float(input_petal_lengh),
            float(input_petal_width)
        ]

        predicted           =  model.predict(list_of_data_to_fit).tolist()
        probabilities       =  model.predict_proba(list_of_data_to_fit).tolist()
        
        result = {
            "response": "ok",
            "predictions": predicted,
            "score": model.score(df, y),
            "probabilities": {flower: probabilities[0][index] for index, flower in enumerate(model.classes_.tolist())}
        }

    else:
        return "Please pass an input"

    return jsonify(result)