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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=2)
model_dir = model.download()
model = joblib.load(model_dir + "/wine_model.pkl")
print("Model downloaded")
def wine(alcohol, chlorides, density, type, volatil_acidity):
print("Calling function")
df = pd.DataFrame([[alcohol, chlorides, density, type, volatil_acidity]],
columns=['alcohol','chlorides','density','type','volatil_acidity'])
print("Predicting")
print(df)
res = model.predict(df)
print(res)
wine_url = "https://raw.githubusercontent.com/Anniyuku/wine_quality/main/" + res[0] + ".png"
img = Image.open(requests.get(wine_url, stream=True).raw)
return img
demo = gr.Interface(
fn=wine,
title="Wine Predictive Analytics",
description="Experiment with alcohol, chlorides, density, type, volatil_acidity to predict which flower it is.",
allow_flagging="never",
inputs=[
gr.inputs.Number(default=10.00, label="alcohol"),
gr.inputs.Number(default=0.60, label="chlorides"),
gr.inputs.Number(default=1.00, label="density"),
gr.inputs.Number(default=1.00, label="type"),
gr.inputs.Number(default=1.00, label="volatil_acidity"),
],
outputs=gr.Image(type="pil"))
demo.launch(debug=True)
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