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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_bin, fare_bin): |
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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_bin) |
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input_list.append(fare_bin) |
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res = model.predict(np.asarray(input_list).reshape(1, -1)) |
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res_0 = str(res[0]) |
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prediction_url = "https://raw.githubusercontent.com/torileatherman/serverless_ml_titanic/main/src/assets/"+res_0+".png" |
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img = Image.open(requests.get(prediction_url, stream=True).raw) |
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return img |
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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 class, sex, age, and fare type 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=1, label="Class (1 is highest, 3 is lowest"), |
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gr.inputs.Number(default=1, label="Gender (0 is male, 1 is female)"), |
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gr.inputs.Number(default=20, label="Age (years)"), |
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gr.inputs.Number(default=1, label="Fare Type (1 is lowest, 4 is highest)"), |
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], |
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outputs=gr.Image(type="pil")) |
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demo.launch() |