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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=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 iris(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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survival_url = "https://raw.githubusercontent.com/freeja/id2223/main/serverless-titanic/" + str(res[0]) + ".png" |
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img = Image.open(requests.get(survival_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="Titanic Survival Analytics", |
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description="Experiment with passenger stats to predict the outcome of their journey.", |
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allow_flagging="never", |
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inputs=[ |
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gr.inputs.Number(default=1.0, label="pclass (1-3)"), |
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gr.inputs.Number(default=1.0, label="sex (male:0, female:1)"), |
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gr.inputs.Number(default=1.0, label="age (1-80)"), |
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gr.inputs.Number(default=1.0, label="sibsp (cm)"), |
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gr.inputs.Number(default=1.0, label="parch (0-4)"), |
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gr.inputs.Number(default=1.0, label="fare (0-512)"), |
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gr.inputs.Number(default=1.0, label="embarked (1-3)"), |
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], |
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outputs=gr.Image(type="pil")) |
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demo.launch() |
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