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e7a3d57 6cb1810 e7a3d57 7f4c3e2 e7a3d57 a715e4b e7a3d57 509a137 e7a3d57 509a137 e7a3d57 509a137 e7a3d57 509a137 e7a3d57 a715e4b e7a3d57 869b3d0 a715e4b e7a3d57 9db15a2 e7a3d57 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | import gradio as gr
from PIL import Image
import requests
import hopsworks as hw
import joblib
import pandas as pd
import xgboost as xgb
project = hw.login(project="jayeshv")
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 Loaded...")
def wine(type,
fixed_acidity,
volatile_acidity,
sulphates,
alcohol,
density):
print("Lets taste wine?")
df = pd.DataFrame([[type, fixed_acidity, volatile_acidity, sulphates, alcohol, density]],
columns = ['type', 'fixed_acidity', 'volatile_acidity',
'sulphates', 'alcohol', 'density'])
print("Predicting...")
print(df.head())
res = model.predict(df)
print(res)
return res
demo = gr.Interface(
fn = wine,
title = 'Wine Quality prediction',
description = '',
allow_flagging = 'never',
inputs = [
gr.Number(value=0, label="type"),
gr.Number(value=6.3, label="fixed_acidity"),
gr.Number(value=0.30, label="volatile_acidity"),
gr.Number(value=0.49, label="sulphates"),
gr.Number(value=9.5, label="alcohol"),
gr.Number(value=0.994, label="density")
],
outputs="number" # output's an integer from 3-9
)
demo.launch(debug=True)
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