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import gradio as gr
import numpy as np
from PIL import Image
import requests
import xgboost
import hopsworks
import joblib
project = hopsworks.login()
fs = project.get_feature_store()
mr = project.get_model_registry()
model = mr.get_model("titanic_modal", version=1)
model_dir = model.download()
model = joblib.load(model_dir + "/titanic_model.pkl")
def titanic(pclass, sex, age, sibs, par_ch, fare, deck, embarked):
input_list = []
input_list.append(pclass)
input_list.append(sex)
input_list.append(age)
input_list.append(sibs)
input_list.append(par_ch)
input_list.append(fare)
input_list.append(deck)
input_list.append(embarked)
# 'res' is a list of predictions returned as the label.
res = model.predict(np.asarray(input_list).reshape(1, -1))
# We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want
# the first element.
return res[0]
demo = gr.Interface(
fn=titanic,
title="Titanic Survival Predictive Analytics",
description="Experiment with passenger class, sex, age, siblings, parents/children, fare, deck, and embarked to predict if a hypothetical passenger survived titanic.",
allow_flagging="never",
inputs=[
gr.inputs.Number(default=0.0, label="Passenger class"),
gr.inputs.Number(default=0.0, label="Sex"),
gr.inputs.Number(default=0.0, label="Age"),
gr.inputs.Number(default=0.0, label="Siblings"),
gr.inputs.Number(default=0.0, label="Parents/Children"),
gr.inputs.Number(default=0.0, label="Fare"),
gr.inputs.Number(default=0.0, label="Deck"),
gr.inputs.Number(default=0.0, label="Embarked"),
],
outputs=gr.inputs.Number(label="Survived"))
demo.launch()
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