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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()
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