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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(api_key_value="4CY1rwa8iz8Yu6gG.TwayrYmsX4GQfhSp3LNKYTLvyFMfqAvnzNUQp5ae9K5HhfYxb5mcnLAutm1K18zV")
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 passenger(pclass, sex, family, groupedage):
input_list = []
input_list.append(pclass)
input_list.append(sex)
input_list.append(family)
input_list.append(groupedage)
# '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] == 1:
passenger_url = "https://cdn.pixabay.com/photo/2018/08/02/18/58/survival-3580200_960_720.png"
else:
passenger_url = "https://pngimg.com/uploads/death/death_PNG55.png"
img = Image.open(requests.get(passenger_url, stream=True).raw)
return img
#return res[0]
demo_titanic = gr.Interface(
fn=passenger,
title="Titanic Predictive Analytics",
description="Experiment to predict if a passenger survived or died in the titanic",
allow_flagging="never",
inputs=[
gr.inputs.Number(default=1.0, label="Pclass (Min:1, Max=3"),
gr.inputs.Number(default=1.0, label="Sex (Female:0 and Male:1)"),
gr.inputs.Number(default=1.0, label="Family (Number of family members in the boat[0,7])"),
gr.inputs.Number(default=1.0, label="Age (Child:0, Adult:1 and Old:2)")
],
outputs=gr.Image(type="pil"))
#outputs=gr.Label(num_top_classes=2)
demo_titanic.launch()
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