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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="otd1BvtKwvlF8OC1.Y8Kyt1QpZqDPMRNPIF3KvVGuFJpRdxIy39879ueQwymTgSDUU9vWKFMOnBqsyxfk")
fs = project.get_feature_store()
#q
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, parch, fare, embarked):
input_list = []
input_list.append(pclass)
input_list.append(sex)
input_list.append(age)
input_list.append(sibsp)
input_list.append(parch)
input_list.append(fare)
input_list.append(embarked)
# '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.
ret_str = "Survived" if res[0] == 1 else "Not survived"
return ret_str
demo = gr.Interface(
fn=titanic,
title="Titanic Predictive Analytics",
description="Experiment to predict if a passenger survived the Titanic disaster",
allow_flagging="never",
inputs=[
gr.inputs.Number(default=1.0, label="PClass"),
gr.inputs.Number(default=1.0, label="Sex: Female = 0, Male = 1"),
gr.inputs.Number(default=1.0, label="Age"),
gr.inputs.Number(default=1.0, label="SibSp"),
gr.inputs.Number(default=1.0, label="Parch"),
gr.inputs.Number(default=1.0, label="Fare"),
gr.inputs.Number(default=1.0, label="Embarked: S = 0, C = 1, Q = 2"),
],
outputs=gr.Textbox())
demo.launch()
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