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import gradio as gr |
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import numpy as np |
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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_v2", version=1) |
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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, pricerange): |
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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(pricerange) |
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res = model.predict(np.asarray(input_list).reshape(1, -1)) |
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if res[0]==0: |
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output = "Did not survive" |
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else: |
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output = "Survived" |
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return output |
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demo = gr.Interface( |
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fn=titanic, |
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title="Titanic Predictive Analytics", |
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description="Experiment with passenger information to predict if the passenger survived or not", |
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allow_flagging="never", |
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inputs=[ |
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gr.inputs.Number(default=1, label="ticket class (1 = 1st, 2 = 2nd, 3 = 3rd)"), |
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gr.inputs.Number(default=0, label="sex (0=male, 1=female)"), |
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gr.inputs.Number(default=24, label="age (years)"), |
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gr.inputs.Number(default=1.0, label="# of siblings/spouses aboard"), |
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gr.inputs.Number(default=1.0, label="# of children/parents aboard"), |
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gr.inputs.Number(default=1.0, label="pricerange (1=cheapest, 5=most expensive)"), |
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], |
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outputs="text") |
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demo.launch() |
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