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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") | |
# "Pclass", "Sex", "Age", "Parch" | |
def titanic(pclass, sex, age, parch): | |
input_list = [] | |
input_list.append(pclass) | |
input_list.append(sex) | |
input_list.append(age) | |
input_list.append(parch) | |
# '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. | |
# TODO(Change URL) | |
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 | |
demo = gr.Interface( | |
fn=titanic, | |
title="Titanic Passenger Survival Predictive Analytics", | |
description="Experiment to predict passenger survival on the Titanic.", | |
allow_flagging="never", | |
inputs=[ | |
gr.inputs.Number(default=1.0, label="Pclass (1 -> 3)"), | |
gr.inputs.Number(default=1.0, label="Sex (0 -> 1)"), | |
gr.inputs.Number(default=1.0, label="Age (0 -> 100)"), | |
gr.inputs.Number(default=1.0, label="Parch (0 -> 2)"), | |
], | |
outputs=gr.Image(type="pil")) | |
demo.launch() | |