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import gradio as gr
import numpy as np
from PIL import Image
import requests
import hopsworks
import joblib
import pandas as pd
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(pclass,age,sibsp,parch,fare,sex,embarked):
input_list = []
input_list.append(pclass)
input_list.append(age)
input_list.append(sibsp)
input_list.append(parch)
input_list.append(fare)
if sex == "male":
input_list.append(0)
input_list.append(1)
elif sex == "female":
input_list.append(1)
input_list.append(0)
if embarked == "C":
input_list.append(1)
input_list.append(0)
input_list.append(0)
input_list.append(0)
elif embarked == "Q":
input_list.append(0)
input_list.append(1)
input_list.append(0)
input_list.append(0)
elif embarked == "S":
input_list.append(0)
input_list.append(0)
input_list.append(1)
input_list.append(0)
elif embarked == "Unknown":
input_list.append(0)
input_list.append(0)
input_list.append(0)
input_list.append(1)
# input_df = pd.DataFrame(data=input_list, columns = ['Pclass', 'Age', 'SibSp', 'Parch',
# 'Fare', 'Sex_female','Sex_male',
# 'Embarked_C', 'Embarked_Q', 'Embarked_S',
# 'Embarked_Unknown'])
# '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:
res_str = "survivor"
else:
res_str = "victim"
passenger_url = "https://raw.githubusercontent.com/tomaskubaitis/serverless_ml_titanic/assets/" + res_str + ".png"
img = Image.open(requests.get(passenger_url, stream=True).raw)
return img
# if res[0] == 1:
# return "The passenger is predicted to be a survivor."
# else:
# return "The passenger is predicted to be a victim."
demo = gr.Interface(
fn=titanic,
title="Titanic Passenger Predictive Analytics",
description="Experiment with passenger data to predict whether the passenger is a survivor or not.",
allow_flagging="never",
inputs=[
gr.inputs.Number(default=2, label="Passenger class (choose from either 1, 2 or 3)"),
gr.inputs.Number(default=30, label="Age in full years (if child younger than 1 round up to 1)"),
gr.inputs.Number(default=1, label="Number of siblings or spouses"),
gr.inputs.Number(default=0, label="Number of parents or children"),
gr.inputs.Number(default=100, label="Fare (cost between 0 and 513)"),
gr.inputs.Textbox(default="male", label="Sex (choose from either male or female)"),
gr.inputs.Textbox(default="Unknown", label="Embarked (choose from either C, Q, S or Unknown)"),
],
# outputs=gr.outputs.Textbox())
outputs=gr.Image(type="pil"))
demo.launch() |