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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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import hopsworks |
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import joblib |
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project = hopsworks.login() |
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fs = project.get_feature_store() |
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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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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 = model.predict(np.asarray(input_list).reshape(1, -1)) |
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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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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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demo.launch() |
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