INNER CODE UNIT · Python
iris
featurestoreorg/serverless-ml-course · src/04-module/app.py:19
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.",