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import gradio as gr
from PIL import Image
import requests
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(type, volatile_acidity, citric_acid, chlorides, density, sulphates, alcohol):
print("Calling function")
df = pd.DataFrame([[type, volatile_acidity, citric_acid, chlorides, density, sulphates, alcohol]],
columns=['type', 'volatile_acidity', 'citric_acid', 'chlorides', 'density', 'sulphates', 'alcohol'])
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(f"Res: {res[0]}")
return res[0]
demo = gr.Interface(
fn=wine,
title="Wine Quality Predictive Analytics",
description="Experiment with type (red/white), volatile acidity, citric acid, chlorides, density, sulphates, alcohol, quality to predict the wine's quality.",
allow_flagging="never",
inputs=[
gr.Number(value=1.0, label="wine type (red = 1, white = 0)"),
gr.Number(value=1.0, label="Volatile acidity"),
gr.Number(value=1.0, label="citric_acid"),
gr.Number(value=1.0, label="chlorides"),
gr.Number(value=1.0, label="density"),
gr.Number(value=1.0, label='sulphates'),
gr.Number(value=1.0, label='alcohol'),
],
outputs=gr.Number(label="quality"))
demo.launch(debug=True)
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