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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() |