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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 xgboost |
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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(sex, age, pclass, parch, embarked): |
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input_list = [] |
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if pclass=='1 First Class': |
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input_list.append(1) |
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elif pclass=='2 Second Class': |
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input_list.append(2) |
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else: |
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input_list.append(3) |
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if sex=='Female': |
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input_list.append(1) |
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else: |
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input_list.append(0) |
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input_list.append(age) |
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input_list.append(parch) |
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if embarked=='C (Cherbourg)': |
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input_list.append(2) |
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elif embarked=='S (Southampton)': |
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input_list.append(1) |
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else: |
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input_list.append(0) |
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res = model.predict(np.asarray(input_list,dtype=object).reshape(1,-1)) |
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flower_url = "https://raw.githubusercontent.com/avatar46/ID2223_lab1/main/images/" + str(res[0]) + ".png" |
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img = Image.open(requests.get(flower_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 different entries to predict if the person will survive.", |
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allow_flagging="never", |
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inputs=[ |
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gr.inputs.Radio(default='Female', label="Gender", choices=['Female','Male']), |
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gr.inputs.Slider(0,150,label='Age'), |
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gr.inputs.Radio(default='1 First Class', label="Passenger Class ", choices=['1 First Class', '2 Second Class', '3 Third Class']), |
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gr.inputs.Number(default=1.0, label="Parch: # of parents / children aboard the Titanic "), |
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gr.inputs.Radio(default='C (Cherbourg)', label="Embarkation Port", choices=['C (Cherbourg)', 'Q (Queenstown)', 'S (Southampton)']), |
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], |
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outputs=gr.Image(type="pil")) |
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demo.launch() |
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