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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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from feature_engineering import feat_eng |
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import hopsworks |
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import joblib |
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import pandas as pd |
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project = hopsworks.login() |
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fs = project.get_feature_store() |
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sharebool = True |
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mr = project.get_model_registry() |
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model = mr.get_model("titanic_modal_simple_classifier", 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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print(model_dir) |
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leo_url = "./imgs/leo.gif" |
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rose_url = "./imgs/rose.webp" |
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def titanic(pclass, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked): |
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df_pre = pd.DataFrame({"PassengerId":[-1], "Pclass": [pclass], "Name": [name], "Sex": [sex], "Age": [age], "SibSp": [sibsp], "Parch": [parch], "Ticket": [ticket], "Fare": [fare], "Cabin": [cabin], "Embarked": [embarked]}) |
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df_post = feat_eng(df_pre) |
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print(df_post) |
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res = model.predict(df_post)[0] |
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img = Image.open(leo_url) if res == 0 else Image.open(rose_url) |
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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 Passenger data to predict survival", |
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allow_flagging="never", |
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inputs=[ |
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gr.inputs.Number(default=1.0, label="pclass, [1,2,3]"), |
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gr.inputs.Textbox(default="Mr. Anton", label="name"), |
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gr.inputs.Textbox(default="male", label="sex, male or female"), |
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gr.inputs.Number(default=25, label="age"), |
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gr.inputs.Number(default=2, label="sibsb"), |
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gr.inputs.Number(default=2, label="parch"), |
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gr.inputs.Textbox(default="blabla", label="Ticket"), |
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gr.inputs.Number(default=200, label="Fare"), |
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gr.inputs.Textbox(default="A123", label="Cabin"), |
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gr.inputs.Textbox(default="S", label="Embarked: [S, C, Q]") |
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], |
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outputs=gr.Image(type="pil")) |
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demo.launch() |
