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import gradio as gr
import numpy as np
from PIL import Image
import requests
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", version=1)
model_dir = model.download()
model = joblib.load(model_dir + "/titanic_model.pkl")
CLASS_TO_VALUE = {
"1st class": "1",
"2nd class": "2",
"3rd class": "3",
}
PORT_TO_VALUE = {
"Cherbourg": "C",
"Queenstown": "Q",
"Southampton": "S",
}
def titanic(ticket_class, sex, port, fare, age, sibsp, parch):
data = {
"pclass": [CLASS_TO_VALUE[ticket_class]],
"sex": [sex],
"embarked": [PORT_TO_VALUE[port]],
"fare": [fare],
"age": [age],
"sibsp": [int(sibsp)],
"parch": [int(parch)],
}
df = pd.DataFrame(data)
# 'res' is a list of predictions returned as the label.
res = model.predict(df)
# We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want
# the first element.
if res:
url = "https://m.media-amazon.com/images/I/71M6k7ZQNcL._RI_.jpg"
else:
url = "https://thumbs.dreamstime.com/b/allvarlig-sten-med-skallen-34707626.jpg"
img = Image.open(requests.get(url, stream=True).raw)
return img
demo = gr.Interface(
fn=titanic,
title="Titanic survival prediction",
description="Experiment with parameters to predict if the fictional passenger survived",
allow_flagging="never",
inputs=[
gr.inputs.Dropdown(["1st class", "2nd class", "3rd class"], label="Ticket class"),
gr.inputs.Dropdown(["female", "male"], label="Sex"),
gr.inputs.Dropdown(["Cherbourg", "Queenstown", "Southampton"], label="Port of Embarkation"),
gr.inputs.Number(default=50.0, label="Fare"),
gr.inputs.Number(default=20.0, label="Age"),
gr.inputs.Number(default=0, label="Number of siblings/spouses aboard the Titanic"),
gr.inputs.Number(default=0, label="Number of parents/children aboard the Titanic"),
],
outputs=gr.Image(type="pil"))
demo.launch()
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