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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("titanic_survival_modal", version=1)
model_dir = model.download()
model = joblib.load(model_dir + "/titanic_model.pkl")
def tb_titanic(pclass,sex,age,sibsp,parch,embarked,fare_per_customer,cabin):
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(embarked)
input_list.append(fare_per_customer)
input_list.append(cabin)
# 'res' is a list of predictions returned as the label.
#global res
res = model.predict(np.asarray(input_list).reshape(1, 8))
return ("This guy will"+(" survive. " if res[0]=="S" else " die. "))
demo = gr.Interface(
fn=tb_titanic,
title="Titanic Predictive Analytics",
description="Predict survivals. 0 for dead and 1 for survived. ",
inputs=[
gr.inputs.Number(default=1.0, label="pclass, "),
gr.inputs.Number(default=1.0, label="gender, 0 for male and 1 for female"),
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="embarked, 1 for C, 2 for S, 3 for Q, and 0 for unknown"),
gr.inputs.Number(default=1.0, label="fare_per_customer"),
gr.inputs.Number(default=1.0, label="cabin, 1 for the known and 0 for the unknown"),
],
outputs=gr.Textbox()
)
# outputs=gr.outputs.Textbox(self,type="auto",label="Hi"))
#("This guy will"+("survive. " if res[0]==1 else "die. ")
demo.launch()
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