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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("iris_modal", version=1)
# model_dir = model.download()
# model = joblib.load(model_dir + "/iris_model.pkl")
# 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=iris,
# title="Iris Flower Predictive Analytics",
# description="Experiment with sepal/petal lengths/widths to predict which flower it is.",
# allow_flagging="never",
# inputs=[
# gr.inputs.Number(default=1.0, label="sepal length (cm)"),
# gr.inputs.Number(default=1.0, label="sepal width (cm)"),
# gr.inputs.Number(default=1.0, label="petal length (cm)"),
# gr.inputs.Number(default=1.0, label="petal width (cm)"),
# ],
# outputs=gr.Image(type="pil"))
# demo.launch()
import gradio as gr
from PIL import Image
import hopsworks
project = hopsworks.login(api_key_value="rA4UUi0EGe9o2Lpo.xoqva15Ia7l8Fz7PBFrFTV4WjSG8B1aQofJlVp3oV3Xp0iHyFTzw5ybC4OapypyU")
fs = project.get_feature_store()
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() |