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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 titanic(pclass, sex, age, fare): |
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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(fare) |
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print(np.asarray(input_list).reshape(1, -1)) |
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res = model.predict(np.asarray(input_list).reshape(1, -1)) |
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image_url = "https://raw.githubusercontent.com/xrisaD/ScalableML-Titanic/main/images/" + str(res[0]) + ".jpg" |
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img = Image.open(requests.get(image_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 titanic features to predict if passanger survived.", |
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allow_flagging="never", |
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inputs=[ |
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gr.inputs.Dropdown(default=1, label="Passanger Class", choices = [1, 2, 3]), |
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gr.inputs.Dropdown(default="Female", label="Sex", choices=["Female", "Male"], type="index"), |
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gr.inputs.Dropdown(default="0-21", label="Age", choices=['0-21', '22-25', '26-40', '41-80', 'unknown'], type="index"), |
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gr.inputs.Slider(minimum=0, maximum=600, default=50, label="Fare"), |
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], |
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outputs=gr.Image(type="pil")) |
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demo.launch() |
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