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import gradio as gr
import hopsworks
import joblib
import pandas as pd
import requests
from PIL import Image
project = hopsworks.login()
fs = project.get_feature_store()
print("trying to dl model")
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(volatile_acidity, chlorides, density, alcohol):
print("Calling wine function")
df = pd.DataFrame(
[[alcohol, chlorides, volatile_acidity, density]],
columns=["alcohol", "chlorides", "volatile_acidity", "density"],
)
print("Predicting")
print(df)
res = model.predict(df)
print(res)
return res
demo = gr.Interface(
fn=wine,
title="Wine Quality Predictive Analytics",
description="Experiment with different values for these properties",
allow_flagging="never",
inputs=[
gr.Number(label="Alcohol"),
gr.Number(label="Chlorides"),
gr.Number(label="Volatile Acidity"),
gr.Number(label="Density"),
],
outputs=gr.Number(label="Quality"),
)
demo.launch(debug=True)
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