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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, residual_sugar, chlorides, free_sulfur_dioxide,
total_sulfur_dioxide, density, ph, sulphates, alcohol):
#Maps type to int with default white
if Type=="red":
Type=1
else:
Type=0
print("Calling function")
df = pd.DataFrame([[Type, volatile_acidity, citric_acid, residual_sugar, chlorides, free_sulfur_dioxide,
total_sulfur_dioxide, density, ph, sulphates, alcohol]],
columns=["type", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides",
"free_sulfur_dioxide", "total_sulfur_dioxide", "density", "ph", "sulphates", "alcohol"])
print("Predicting")
print(df)
res = model.predict(df)
print(res)
return "Predicted quality: "+str(res[0])
demo = gr.Interface(
fn=wine,
title="Wine Quality Predictive Analytics",
description="Experiment with features to predict wine quality.",
allow_flagging="never",
inputs=[
gr.inputs.Textbox(default="white", label="Type"),
gr.inputs.Number(default=0.34, label="Volatile acidity"),
gr.inputs.Number(default=0.32, label="Citric acid"),
gr.inputs.Number(default=5.4, label="Residual sugar"),
gr.inputs.Number(default=0.056, label="Chlorides"),
gr.inputs.Number(default=31.0, label="Free sulfur dioxide"),
gr.inputs.Number(default=116.0, label="Total sulfur dioxide"),
gr.inputs.Number(default=0.995, label="Density"),
gr.inputs.Number(default=3.2, label="pH"),
gr.inputs.Number(default=0.53, label="Sulphates"),
gr.inputs.Number(default=10.5, label="Alcohol precentage"),
],
outputs=gr.Label())
demo.launch(debug=True)
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