app.py

import gradio as gr
from sklearn.hub import HubLoader 

hub = HubLoader("risingodegua/wine-quality-model", "sklearn_model.joblib")
model = hub.load()

def wine_quality_predictor(X):
    '''Predicts the quality of wine

    Parameters
    ----------

    X : numpy, list
        A list containing values used for prediction. 

    Returns
    -------
    List
        The list of predicted values.
       
    '''
    return model.predict(X.to_numpy())


headers = [
    "fixed acidity",
    "volatile acidity",
    "citric acid",
    "residual sugar",
    "chlorides",
    "free sulfur dioxide",
    "total sulfur dioxide",
    "density",
    "pH",
    "sulphates",
    "alcohol",
]
default = [
    [7.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.9978, 3.51, 0.56, 9.4],
    [7.8, 0.88, 0, 2.6, 0.098, 25, 67, 0.9968, 3.2, 0.68, 9.8],
    [7.8, 0.76, 0.04, 2.3, 0.092, 15, 54, 0.997, 3.26, 0.65, 9.8],
]

iface = gr.Interface(
    wine_quality_predictor,
    gr.inputs.Dataframe(
        headers=headers,
        default=default,
    ),
    ["numpy"],
    description="Enter wine properties for prediction"
)
iface.launch()