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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="U1TTeOPaUDFWhd6N.H604QVLj5yOFVPGeSrXsoFY2IKrGkdqR0iTzWMr22rZxXQrn5VoYKdb4fghqxTna")
fs = project.get_feature_store()
#HwJaWmtvaCzFra3g.89QYueFGuScRnJkiepzG2tiWtKSrqNHCCJrnVie9fwhIMeJxRUpAGAT7mF36MDMv
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, SibSp):
input_list = []
input_list.append(Pclass)
input_list.append(Sex)
input_list.append(Age)
input_list.append(SibSp)
# '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.
# flower_url = "https://raw.githubusercontent.com/featurestoreorg/serverless-ml-course/main/src/01-module/assets/" + res[0] + ".png"
# img = Image.open(requests.get(flower_url, stream=True).raw)
# return img
if (res[0] == 0):
result = "I'm sorry, the person is dead"
else:
result = "Awesome, the person is survived!!!!!!"
return result
demo = gr.Interface(
fn=titanic,
title="Titanic Predictive Analytics",
description="Experiment with Passenger class/Sex/Age/SibSp to predict if the person is survived or not.",
allow_flagging="never",
inputs=[
gr.inputs.Number(default=1.0, label="Pclass (Flight class 1/2/3)"),
gr.inputs.Number(default=1.0, label="Sex (male=1/female=2)"),
gr.inputs.Number(default=1.0, label="Age (in years)"),
gr.inputs.Number(default=1.0, label="SibSp (number of siblings)"),
],
outputs=gr.Textbox(label="Result: "))
demo.launch()
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