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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("titan_modal", version=50)
model_dir = model.download()
model = joblib.load(model_dir + "/titan_model.pkl")
def titan(pclass, sex, age, fare, famliy):
input_list = []
input_list.append(pclass)
input_list.append(sex)
input_list.append(age)
input_list.append(fare)
input_list.append(famliy)
# '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.
survivor_url = "https://raw.githubusercontent.com/Chaouo/Titanic_serverless_ML/main/image/"+ str(res[0]) + ".png"
img = Image.open(requests.get(survivor_url, stream=True).raw)
return img
demo = gr.Interface(
fn=titan,
title="Titanic Survival Predictive Analytics",
description="Experiment with pclass, sex, age, fare, famliy to predict which flower it is.",
allow_flagging="never",
inputs=[
gr.inputs.Number(default=1.0, label="pclass (1-3)"),
gr.inputs.Number(default=1.0, label="sex (0 indecates male and 1 indecates female)"),
gr.inputs.Number(default=1.0, label="age"),
gr.inputs.Number(default=1.0, label="fare (0-512)"),
gr.inputs.Number(default=1.0, label="famliy (numbers)"),
],
outputs=gr.Image(type="pil"))
demo.launch()
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