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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=1)
EVALUATION_METRIC="accuracy"
SORT_METRICS_BY="max" # your sorting criteria
# get best model based on custom metrics
best_model = mr.get_best_model("titanic_modal",
EVALUATION_METRIC,
SORT_METRICS_BY)
model = best_model
model_dir = model.download()
model = joblib.load(model_dir + "/titanic_model.pkl")
def passenger(Pclass, Age, SibSp, Parch, Fare, Sex, Embarked):
input_list = []
if Pclass == "First Class":
input_list.append(1)
elif Pclass == "Second Class":
input_list.append(2)
else:
input_list.append(3)
input_list.append(Age)
input_list.append(SibSp)
input_list.append(Parch)
input_list.append(Fare)
if Sex == "Male":
input_list.append(0)
input_list.append(1)
else:
input_list.append(1)
input_list.append(0)
if Embarked == "Cherbourg":
input_list.append(1)
input_list.append(0)
input_list.append(0)
elif Embarked == "Queenstown":
input_list.append(0)
input_list.append(1)
input_list.append(0)
else:
input_list.append(0)
input_list.append(0)
input_list.append(1)
# 'res' is a list of predictions returned as the label.
res = model.predict(np.asarray(input_list).reshape(1, -1))
res = str(res[0])
# We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want
# the first element.
passenger_url = "https://raw.githubusercontent.com/GianlucaRub/Scalable-Machine-Learning-and-Deep-Learning/main/Lab1/assets/" + res + ".png"
img = Image.open(requests.get(passenger_url, stream=True).raw)
return img
demo = gr.Interface(
fn=passenger,
title="Titanic Predictive Analytics",
description="Insert passenger class, age, number of sibilings/spouse on board of the Titanic, number of parents/children on board of the Titanic, fare, sex, port of embarkation and see if he/she survived ",
allow_flagging="never",
inputs=[
gr.inputs.Radio(choices=["First Class", "Second Class", "Third Class"], label="Passenger Class"),
gr.inputs.Number(default=20, label="Age (years)"),
gr.inputs.Number(default=1.0, label="Number of sibilings/spouse on board of the Titanic"),
gr.inputs.Number(default=1.0, label="Number of parents/children on board of the Titanic"),
gr.inputs.Number(default=10.0, label="Fare (USD)"),
gr.inputs.Radio(choices=["Male","Female"], label = "Sex"),
gr.inputs.Radio(choices=["Cherbourg","Queenstown","Southampton"], label = "Port of embarkation")
],
outputs=gr.Image(type="pil"))
demo.launch()
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