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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(api_key_value="U1TTeOPaUDFWhd6N.H604QVLj5yOFVPGeSrXsoFY2IKrGkdqR0iTzWMr22rZxXQrn5VoYKdb4fghqxTna") |
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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, SibSp): |
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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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res = model.predict(np.asarray(input_list).reshape(1, -1)) |
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if (res[0] == 0): |
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result = "I'm sorry, the person is dead" |
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else: |
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result = "Awesome, the person is survived!!!!!!" |
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return result |
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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 Passenger class/Sex/Age/SibSp to predict if the person 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="Pclass (Flight class 1/2/3)"), |
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gr.inputs.Number(default=1.0, label="Sex (male=1/female=2)"), |
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gr.inputs.Number(default=1.0, label="Age (in years)"), |
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gr.inputs.Number(default=1.0, label="SibSp (number of siblings)"), |
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], |
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outputs=gr.Textbox(label="Result: ")) |
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demo.launch() |
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