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import gradio as gr
import numpy as np
from PIL import Image
import requests
import hopsworks
import joblib
project = hopsworks.login()
fs = project.get_feature_store()
mr = project.get_model_registry()
model = mr.get_model("iris_modal", version=1)
model_dir = model.download()
model = joblib.load(model_dir + "/iris_model.pkl")
def iris(sepal_length, sepal_width, petal_length, petal_width):
input_list = []
input_list.append(sepal_length)
input_list.append(sepal_width)
input_list.append(petal_length)
input_list.append(petal_width)
# 'res' is a list of predictions returned as the label.
res = model.predict(np.asarray(input_list).reshape(1, -1))
# We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want
# the first element.
flower_url = "https://raw.githubusercontent.com/featurestoreorg/serverless-ml-course/main/src/01-module/assets/" + res[0] + ".png"
img = Image.open(requests.get(flower_url, stream=True).raw)
return img
demo = gr.Interface(
fn=iris,
title="Iris Flower Predictive Analytics",
description="Experiment with sepal/petal lengths/widths to predict which flower it is.",
allow_flagging="never",
inputs=[
gr.inputs.Number(default=1.0, label="sepal length (cm)"),
gr.inputs.Number(default=1.0, label="sepal width (cm)"),
gr.inputs.Number(default=1.0, label="petal length (cm)"),
gr.inputs.Number(default=1.0, label="petal width (cm)"),
],
outputs=gr.Image(type="pil"))
demo.launch()
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