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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=2) |
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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(Pclass,Sex,Age,SibSp,Parch,Fare,Embarked): |
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input_list = [] |
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input_list.append(Pclass) |
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input_list.append(Sex) |
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input_list.append(Age) |
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input_list.append(SibSp) |
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input_list.append(Parch) |
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input_list.append(Fare) |
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input_list.append(Embarked) |
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res = model.predict(np.asarray(input_list).reshape(1, -1)) |
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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 Survival Predictive Analytics", |
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description="Experiment with passengers information to predict whether they can survive in titanic.", |
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allow_flagging="never", |
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inputs=[ |
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gr.inputs.Number(default=1.0, label="Class [0, 1, 2]"), |
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gr.inputs.Number(default=1.0, label="Sex [0(male), 1(female)]"), |
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gr.inputs.Number(default=1.0, label="Age [y/o]"), |
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gr.inputs.Number(default=1.0, label="sibsp [0-5]]"), |
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gr.inputs.Number(default=1.0, label="Parch [0-6]]"), |
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gr.inputs.Number(default=1.0, label="Fare [USD]"), |
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gr.inputs.Number(default=1.0, label="Embarked [0 (S), 1 (C), 2 (Q)]"), |
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], |
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outputs=gr.Text(value="none") |
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) |
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demo.launch() |
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