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Upload app.py

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