File size: 2,617 Bytes
0f278ae 7b136b2 0f278ae 7b136b2 f987c86 0f278ae 57f1607 0f278ae 16950fb 0f278ae | 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 56 57 58 59 60 | import gradio as gr
from PIL import Image
import requests
import hopsworks
import joblib
import pandas as pd
import numpy as np
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_, fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides, free_sulfur_dioxide, total_sulfur_dioxide, density, pH, sulphates, alcohol):
print("Calling function")
# df = pd.DataFrame([[sepal_length],[sepal_width],[petal_length],[petal_width]],
df = pd.DataFrame([[type_, fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides, free_sulfur_dioxide, total_sulfur_dioxide, density, pH, sulphates, alcohol]],
columns=['type', 'fixed_acidity', 'volatile_acidity', 'citric_acid', 'residual_sugar', 'chlorides', 'free_sulfur_dioxide', 'total_sulfur_dioxide', 'density', 'ph', 'sulphates', 'alcohol'])
print("Predicting")
print(df)
# 'res' is a list of predictions returned as the label.
res = np.round(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("Res: {0}").format(res)
print(res)
wine_url = "https://raw.githubusercontent.com/aym1king/serverless-intro/master/wine/wine_imgs/" + str(int(res[0])) + ".png"
img = Image.open(requests.get(wine_url, stream=True).raw)
return img
demo = gr.Interface(
fn=wine,
title="Wine Quality Predictive Analytics",
description="Experiment with wine features to predict which quality it has.",
allow_flagging="never",
inputs=[
gr.inputs.Number(default=0, label="type (0 = white, 1 = red)"),
gr.inputs.Number(default=7.3, label="fixed acidity"),
gr.inputs.Number(default=0.4, label="volatile acidity"),
gr.inputs.Number(default=0.3, label="citric acid"),
gr.inputs.Number(default=5.8, label="residual sugar"),
gr.inputs.Number(default=0.1, label="chlorides"),
gr.inputs.Number(default=30, label="free sulfur dioxide"),
gr.inputs.Number(default=120, label="total sulfur dioxide"),
gr.inputs.Number(default=1.0, label="density"),
gr.inputs.Number(default=3.2, label="pH"),
gr.inputs.Number(default=0.5, label="sulphates"),
gr.inputs.Number(default=9.8, label="alcohol"),
],
outputs=gr.Image(type="pil"))
demo.launch(debug=True)
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