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import gradio as gr
import numpy as np
from PIL import Image
import requests
import hopsworks
import joblib
project = hopsworks.login()
fs = project.get_feature_store()
mr = project.get_model_registry()
model = mr.get_model("titanic_modal", version=2)
model_dir = model.download()
model = joblib.load(model_dir + "/titanic_model.pkl")
def titanic(Pclass,Sex,Age,SibSp,Parch,Fare,Embarked):
input_list = []
input_list.append(Pclass)
input_list.append(Sex)
input_list.append(Age)
input_list.append(SibSp)
input_list.append(Parch)
input_list.append(Fare)
input_list.append(Embarked)
# 'res' is a list of predictions returned as the label.
res = model.predict(np.asarray(input_list).reshape(1, -1))
alive_url = "https://illustoon.com/photo/dl/1045.png"
died_url = "https://illustoon.com/photo/dl/1052.png"
img_path = alive_url if int(res[0]) == 1 else died_url
img = Image.open(requests.get(img_path, stream=True).raw)
return img
demo = gr.Interface(
fn=titanic,
title="Titanic Survival Predictive Analytics",
description="Experiment with passengers information to predict whether they can survive in titanic.",
allow_flagging="never",
inputs=[
gr.inputs.Number(default=1.0, label="Class [0, 1, 2]"),
gr.inputs.Number(default=1.0, label="Sex [0(male), 1(female)]"),
gr.inputs.Number(default=1.0, label="Age [y/o]"),
gr.inputs.Number(default=1.0, label="sibsp [0-5]]"),
gr.inputs.Number(default=1.0, label="Parch [0-6]]"),
gr.inputs.Number(default=1.0, label="Fare [USD]"),
gr.inputs.Number(default=1.0, label="Embarked [0 (S), 1 (C), 2 (Q)]"),
],
outputs=gr.Image(type="pil"))
demo.launch()
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