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import gradio as gr
import numpy as np
import pandas as pd
from pandas.core.frame import DataFrame
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("aurora_model", version=1)
model_dir = model.download()
model = joblib.load(model_dir+"/aurora_model.pkl")
def tb_aurora(Kp_index, visibility, icon):
input_list = []
input_list.append(Kp_index)
input_list.append(visibility)
input_icon = icon
icon_feature_list = ['clear_day', 'clear_night', 'cloudy', 'fog', 'partly_cloudy_day', 'partly_cloudy_night', 'rain',
'snow', 'wind']
icon_feature_list.append(input_icon)
icon_df = DataFrame(icon_feature_list)
icon_df_one = pd.get_dummies(icon_df)
icon = icon_df_one.values.tolist()[9]
input_list.extend(icon)
print(input_list)
# 'res' is a list of predictions returned as the label.
# global res
res = model.predict(np.asarray(input_list).reshape(1, 11))
aurora_url = "https://raw.githubusercontent.com/NeoForNew/ID2223_scalable_machine_learning_and_deep_learning/main/Project/pic/" + str(res[0]) + ".png"
img = Image.open(requests.get(aurora_url, stream=True).raw)
return img
demo = gr.Interface(
fn=tb_aurora,
title="Aurora Predictive Analytics",
description="Predict aurora 0 for not occur and 1 for occur. ",
inputs=[
gr.inputs.Number(default=0.0, label="Kp_index"),
gr.inputs.Number(default=0.0, label="visibility"),
gr.inputs.Dropdown(['clear_day', 'clear_night', 'cloudy', 'fog', 'partly_cloudy_day', 'partly_cloudy_night', 'rain',
'snow', 'wind'], label="icon"),
],
outputs=gr.Image(type="pil"))
demo.launch() |