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Create app.py
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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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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("titanic_modal", version=10)
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model_dir = model.download()
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model = joblib.load(model_dir + "/titanic_model.pkl")
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def titanic(age, embarked, fare, parch, pclass, sex, sibsp):
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input_list = []
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input_list.append(age)
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input_list.append(embarked)
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input_list.append(fare)
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input_list.append(parch)
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input_list.append(pclass)
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input_list.append(sex)
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input_list.append(sibsp)
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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 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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if res == [1]:
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res = 'survive'
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else:
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res = 'die'
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return res
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demo = gr.Interface(
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fn=titanic,
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title="Titanic Survivor Predictive Analytics",
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description="Experiment with age/embarked/fare/parch/pclass/sex/sibsp to predict if the passenger survived.",
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allow_flagging="never",
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inputs=[
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gr.inputs.Number(default=2.0, label="age"),
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gr.inputs.Number(default=1.0, label="embarked (0 for S, 1 for C, 2 for Q)"),
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gr.inputs.Number(default=35.0, label="fare"),
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gr.inputs.Number(default=1.0, label="parch"),
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gr.inputs.Number(default=1.0, label="pclass"),
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gr.inputs.Number(default=1.0, label="sex (0 for male, 1 for male)"),
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gr.inputs.Number(default=1.0, label="sibsp")
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],
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outputs=gr.Textbox())
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demo.launch()
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