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import gradio as gr
from PIL import Image
import requests
import hopsworks
import joblib
import pandas as pd
import random
project = hopsworks.login(project="zeihers_mart")
fs = project.get_feature_store()
mr = project.get_model_registry()
model = mr.get_model("wine_model", version=3)
model_dir = model.download()
model = joblib.load(model_dir + "/wine_model.pkl")
print("Model downloaded")
def wine(alcohol, volatile_acidity, total_sulfur_dioxide, chlorides, density):
print("Calling function")
df = pd.DataFrame([[alcohol, volatile_acidity, total_sulfur_dioxide, chlorides, density]],
columns=["alcohol", "volatile_acidity", "total_sulfur_dioxide", "chlorides", "density"])
print("Predicting")
print(df)
res = model.predict(df)
print(res)
return res[0]
demo = gr.Interface(
fn=wine,
title="Wine Predictive Analytics",
description="Experiment with inputs to predict wine quality.",
allow_flagging="never",
inputs=[
gr.Number(precision=3, value=random.uniform(8.0, 14.9), label="alcohol"),
gr.Number(precision=3, value=random.uniform(0.08, 1.58), step=0.1, label="volatile acidity"),
gr.Number(precision=3, value=random.uniform(6.0, 440.0), label="total sulfur dioxide"),
gr.Number(precision=3, value=random.uniform(0.009, 0.611), step=0.01, label="chlorides"),
gr.Number(precision=3, value=random.uniform(0.987, 1.039), step=0.01, label="density"),
],
outputs=gr.Number(label="Prediction"))
demo.launch(debug=True)
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