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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("iris_model", version=1)
model_dir = model.download()
model = joblib.load(model_dir + "/iris_model.pkl")
print("Model downloaded")
def iris(sepal_length, sepal_width, petal_length, petal_width):
print("Calling function")
# df = pd.DataFrame([[sepal_length],[sepal_width],[petal_length],[petal_width]],
df = pd.DataFrame([[sepal_length,sepal_width,petal_length,petal_width]],
columns=['sepal_length','sepal_width','petal_length','petal_width'])
print("Predicting")
print(df)
# 'res' is a list of predictions returned as the label.
res = 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)
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.Number(value=2.0, label="sepal length (cm)"),
gr.Number(value=1.0, label="sepal width (cm)"),
gr.Number(value=2.0, label="petal length (cm)"),
gr.Number(value=1.0, label="petal width (cm)"),
],
outputs=gr.Image(type="pil"))
demo.launch(debug=True)
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