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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(sex, age, sibsp, parch, fare, embarked, pclass): |
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input_list = [] |
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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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if embarked == 1: |
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input_list.append(1) |
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input_list.append(0) |
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input_list.append(0) |
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elif embarked == 2: |
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input_list.append(0) |
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input_list.append(1) |
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input_list.append(0) |
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else: |
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input_list.append(0) |
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input_list.append(0) |
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input_list.append(1) |
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if pclass == 1: |
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input_list.append(1) |
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input_list.append(0) |
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input_list.append(0) |
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elif pclass == 2: |
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input_list.append(0) |
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input_list.append(1) |
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input_list.append(0) |
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else: |
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input_list.append(0) |
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input_list.append(0) |
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input_list.append(1) |
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res = model.predict(np.asarray(input_list).reshape(1, -1)) |
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if res[0] == 1: |
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image_url = "https://i.ibb.co/0X0JTcx/survive.jpg" |
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else: |
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image_url = "https://i.ibb.co/C8SdRn2/drowning.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 Predictive Analytics", |
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description="Experiment with titanic dataset to predicte if a passenger is survived or not", |
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allow_flagging="never", |
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inputs=[ |
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gr.inputs.Number(default=1.0, label="sex"), |
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gr.inputs.Number(default=1.0, label="age"), |
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gr.inputs.Number(default=1.0, label="sibsp"), |
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gr.inputs.Number(default=1.0, label="parch"), |
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gr.inputs.Number(default=1.0, label="fare"), |
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gr.inputs.Number(default=1.0, label="embarked"), |
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gr.inputs.Number(default=1.0, label="pclass"), |
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], |
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outputs=gr.Image(type="pil")) |
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demo.launch(debug=True) |
