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import gradio as gr
import numpy as np
from PIL import Image
import requests
from feature_engineering import feat_eng
import hopsworks
import joblib
import pandas as pd
project = hopsworks.login()
fs = project.get_feature_store()
mr = project.get_model_registry()
model = mr.get_model("titanic_modal_simple_classifier", version=1)
model_dir = model.download()
model = joblib.load(model_dir + "/titanic_model.pkl")
leo_url = "https://media.tenor.com/FghTtX3ZgbAAAAAC/drowning-leo.gif"
rose_url = "https://media4.giphy.com/media/6A5zBPtbknIGY/giphy.gif?cid=ecf05e477syp5zeoheii45de76uicvgu0nuegojslz3zgodt&rid=giphy.gif&ct=g"
def titanic(pclass, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked):
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]})
df_post = feat_eng(df_pre)
# 'res' is a list of predictions returned as the label.
res = model.predict(df_post)[0]
# We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want
# the first element.
img = Image.open(leo_url) if res == 0 else Image.open(rose_url)
return img
demo = gr.Interface(
fn=titanic,
title="Titanic Survival Predictive Analytics",
description="Experiment with Titanic Passenger data to predict survival",
allow_flagging="never",
inputs=[
gr.inputs.Number(default=1.0, label="pclass, [1,2,3]"),
gr.inputs.Textbox(default="Anton", label="name"),
gr.inputs.Textbox(default="male", label="sex, male or female"),
gr.inputs.Number(default=25, label="age"),
gr.inputs.Number(default=2, label="sibsb"),
gr.inputs.Number(default=2, label="parch"),
gr.inputs.Textbox(default="blabla", label="Ticket"),
gr.inputs.Number(default=200, label="Fare"),
gr.inputs.Textbox(default="blabla", label="Cabin"),
gr.inputs.Textbox(default="blabla", label="Embarked: [S, C, Q]")
],
outputs=gr.Image(type="pil"))
demo.launch() |