File size: 1,972 Bytes
8935477 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
import gradio as gr
import numpy as np
from PIL import Image
import requests
import hopsworks
import joblib
project = hopsworks.login(api_key_value="CDqcnm3gyfxjyCO8.TZwOClLOwCqDp33vX0P5Q2nsvNNyEhfBMArwNoPjnb9tUSSKq6I8X35HQ5D2tlJ7")
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):
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(np.random.choice([0,1], 9))
# '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.
man_url = "https://raw.githubusercontent.com/Tilosmsh/IL2223_lab1/main/images/" + ("survived.jpg" if res[0] else "dead.jpg")
img = Image.open(requests.get(man_url, stream=True).raw)
return img
demo = gr.Interface(
fn=titanic,
title="Titanic Predictive Analytics",
description="Experiment with passenger class, sex, age, number of siblings, number of parents & children and fare, to predict whether the passenger survived.",
allow_flagging="never",
inputs=[
gr.inputs.Number(default=1, label="Passenger Class (0, 1 or 2)"),
gr.inputs.Number(default=1, label="Sex (0 or 1)"),
gr.inputs.Number(default=30.0, label="Age (0 to 80)"),
gr.inputs.Number(default=1, label="Number of Siblings (0 to 8)"),
gr.inputs.Number(default=1, label="Number of Parents and children (0 to 6)"),
gr.inputs.Number(default=35.0, label="Fare (0 to 513)"),
],
outputs=gr.Image(type="pil"))
demo.launch()
|