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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=3)
model_dir = model.download()
model = joblib.load(model_dir + "/titanic_model.pkl")
def iris(Pclass, Sex, SibSp, Parch, Embarked, Age, Fare_type):
input_list = []
input_list.append(Pclass)
##############################
if Sex=='male':
input_list.append(1)
else:
input_list.append(0)
##############################
input_list.append(SibSp)
input_list.append(Parch)
##############################
if Embarked=='S':
input_list.append(0)
elif Embarked=='C':
input_list.append(1)
else:
input_list.append(2)
##############################
##############################
if Age <= 12:
one_hot_age = [1, 0, 0, 0]
for i in one_hot_age:
input_list.append(i)
elif Age <= 19:
one_hot_age = [0, 1, 0, 0]
for i in one_hot_age:
input_list.append(i)
elif Age <= 39:
one_hot_age = [0, 0, 1, 0]
for i in one_hot_age:
input_list.append(i)
else:
one_hot_age = [0, 0, 0, 1]
for i in one_hot_age:
input_list.append(i)
##############################
##############################
if Fare_type == "low":
one_hot_fare = [1, 0, 0, 0]
for i in one_hot_fare:
input_list.append(i)
elif Fare_type == "medium-low":
one_hot_fare = [0, 1, 0, 0]
for i in one_hot_fare:
input_list.append(i)
elif Fare_type == "medium":
one_hot_fare = [0, 0, 1, 0]
for i in one_hot_fare:
input_list.append(i)
else:
one_hot_fare = [0, 0, 0, 1]
for i in one_hot_fare:
input_list.append(i)
##############################
# '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.
if res[0] == 0:
img_string = "dead"
else:
img_string = "survived"
passenger_url = "https://raw.githubusercontent.com/santroma1/id2223_lab1_titanic/main/assets/" + img_string + ".jpg"
img = Image.open(requests.get(passenger_url, stream=True).raw)
return img
demo = gr.Interface(
fn=iris,
title="Titanic Predictive Analytics",
description="Experiment to predict whether a passanger survived or not.",
allow_flagging="never",
inputs=[
gr.inputs.Number(default=1.0, label="Cabin class (1, 2, 3)"),
gr.Textbox(default='male', label="Sex (male, female)"),
gr.inputs.Number(default=1.0, label="SibSp (number of siblings/spouses aboard)"),
gr.inputs.Number(default=1.0, label="Parch (number of parents/children aboard)"),
gr.Textbox(default="S", label="Port of Embarkation (C = Cherbourg, Q = Queenstown, S = Southampton)"),
gr.inputs.Number(default=1.0, label="Age"),
gr.Textbox(default="low", label="Fare_type (low, medium-low, medium, high)"),
],
outputs=gr.Image(type="pil"))
demo.launch() |