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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="0rdWXlLgEd3mkGOg.iRZ7TtAkWGPlJHNQcAEph6Qbokoaq7QTBRI9ckwWUki8tIYGyBvrKhJvtLoUOGQ4")
fs = project.get_feature_store()
mr = project.get_model_registry()
model = mr.get_model("xgboost_model", version=1)
# model_dir = model.download()
# model = joblib.load(model_dir + "/model.pkl")
def forecast():
x = 0
# input_list = [ 0. , 24 , -0.68645433, -0.06804887, -0.31264014,
# -0.13749569, -0.32063957, -0.2942814 , -0.18460245, -0.41253886,
# 0.06395449, 0.71276574, -0.36466156, -1.03879548, -0.65985627,
# 0 , 0 , 0.12254366, 0.39172671, 0.34205118,
# 0.21383452, -1.0216134 , 0.40277851, -0.34577169, -0.36832646,
# -0.7210296 , 0 ]
#res = model.predict(x)
return 10
demo = gr.Interface(
fn=forecast,
title="Air Quality Prediction",
description="Get aqi value",
allow_flagging="never",
outputs=gr.Textbox(label="Result: "))
demo.launch() |