app.py

import gradio as gr
import hopsworks
import joblib
import pandas as pd

project = hopsworks.login()
fs = project.get_feature_store()

mr = project.get_model_registry()
model = mr.get_model("wine_model", version=1)
model_dir = model.download()
model = joblib.load(model_dir + "/wine_model.pkl")
print("Model downloaded")

def wine(alcohol, chlorides, citric_acid, fixed_acidity, ph, residual_sugar, sulphates, total_sulfur_dioxide, type, volatile_acidity):
    print("Calling function")
    df = pd.DataFrame([[alcohol, chlorides, citric_acid, fixed_acidity, ph, residual_sugar, sulphates, total_sulfur_dioxide, type, volatile_acidity]], 
                      columns=['alcohol', 'chlorides', 'citric_acid', 'fixed_acidity', 'ph', 'residual_sugar', 'sulphates', 'total_sulfur_dioxide', 'type', 'volatile_acidity'])
    print("Predicting")
    print(df)
    # 'res' is a list of predictions returned as the label.
    res = model.predict(df) 
    # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want 
    # the first element.

    print(res)
    return res[0]

iface = gr.Interface(
    fn=wine, 
    title="Wine Quality Prediction", 
    description="Predict the quality of a wine based on its features.",
    allow_flagging="never",
    inputs=[
        gr.inputs.Number(label="alcohol"),
        gr.inputs.Number(label="chlorides"),
        gr.inputs.Number(label="citric acid"),
        gr.inputs.Number(label="fixed acidity"),
        gr.inputs.Number(label="ph"),
        gr.inputs.Number(label="residual sugar"),
        gr.inputs.Number(label="sulphates"),
        gr.inputs.Number(label="total sulfur dioxide"),
        gr.inputs.Number(label="type"),
        gr.inputs.Number(label="volatile acidity"),
        ],
    outputs=gr.Number(type="number"))

iface.launch()