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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()
# #q
# 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]
# 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: Female = 0, Male = 1"),
# 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: S = 0, C = 1, Q = 2"),
# ],
# 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/df_recent.png")
dataset_api.download("Resources/images/confusion_matrix.png")
with gr.Blocks() as demo:
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() |