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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="rA4UUi0EGe9o2Lpo.xoqva15Ia7l8Fz7PBFrFTV4WjSG8B1aQofJlVp3oV3Xp0iHyFTzw5ybC4OapypyU") | |
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, 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. | |
return res[0] | |
# def iris(sepal_length, sepal_width, petal_length, petal_width): | |
# input_list = [] | |
# input_list.append(sepal_length) | |
# input_list.append(sepal_width) | |
# input_list.append(petal_length) | |
# input_list.append(petal_width) | |
# # '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. | |
# 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 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"), | |
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") | |
], | |
outputs=gr.Textbox()) | |
demo.launch() | |
# monitoring part of the code | |
# import gradio as gr | |
# from PIL import Image | |
# import hopsworks | |
# project = hopsworks.login(api_key_value="rA4UUi0EGe9o2Lpo.xoqva15Ia7l8Fz7PBFrFTV4WjSG8B1aQofJlVp3oV3Xp0iHyFTzw5ybC4OapypyU") | |
# fs = project.get_feature_store() | |
# #h | |
# dataset_api = project.get_dataset_api() | |
# dataset_api.download("Resources/images/latest_iris.png") | |
# dataset_api.download("Resources/images/actual_iris.png") | |
# dataset_api.download("Resources/images/df_recent.png") | |
# dataset_api.download("Resources/images/confusion_matrix.png") | |
# with gr.Blocks() as demo: | |
# with gr.Row(): | |
# with gr.Column(): | |
# gr.Label("Today's Predicted Image") | |
# input_img = gr.Image("latest_iris.png", elem_id="predicted-img") | |
# with gr.Column(): | |
# gr.Label("Today's Actual Image") | |
# input_img = gr.Image("actual_iris.png", elem_id="actual-img") | |
# with gr.Row(): | |
# with gr.Column(): | |
# gr.Label("Recent Prediction History") | |
# input_img = gr.Image("df_recent.png", elem_id="recent-predictions") | |
# with gr.Column(): | |
# gr.Label("Confusion Maxtrix with Historical Prediction Performance") | |
# input_img = gr.Image("confusion_matrix.png", elem_id="confusion-matrix") | |
# demo.launch() |