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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_modal", version=1)
model_dir = model.download()
model = joblib.load(model_dir + "/titanic_model.pkl")
def titanic(Sex_Code,Pclass,Embarked_Code,Title_Code,FamilySize,AgeBin_Code,FareBin_Code):
input_list = []
input_list.append(Sex_Code)
input_list.append(Pclass)
input_list.append(Embarked_Code)
input_list.append(Title_Code)
input_list.append(FamilySize)
input_list.append(AgeBin_Code)
input_list.append(FareBin_Code)
# '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):
flower_url = "https://images.pexels.com/photos/8384595/pexels-photo-8384595.jpeg?auto=compress&cs=tinysrgb&w=600"
elif(res[0]==0):
flower_url = "https://images.pexels.com/photos/1121906/pexels-photo-1121906.jpeg?auto=compress&cs=tinysrgb&w=600"
img = Image.open(requests.get(flower_url, stream=True).raw)
return img
demo = gr.Interface(
fn=titanic,
title="Titanic Survivor Predictive Analytics",
description="Experiment with input parameters to predict survival",
allow_flagging="never",
inputs=[
#0= female 1=male
gr.Radio(["Female", "Male"], label="Gender", type="index"),
gr.Radio([1, 2, 3], label="Ticket class", type = "value"),
gr.Radio([1, 2, 3], label="Embarked from", type="index"),
gr.Radio(["Master", "miscellaneous", "Miss", "Mr", "Mrs" ], label="Title", type="index"),
gr.Radio([1,2,3,4,5,6,7,8,11], label="Family size", type="value"),
gr.Radio(["age<=16", "16<age<=32", "32<age<=48", "48<age<=64", "64<age<=80"], label="Age", type="index"),
gr.Radio(["Low", "Medium", "High", "Very high"], label="Fare", type="index"),
],
outputs=gr.Image(type="pil")
)
demo.launch()
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