import gradio as gr
import numpy as np
from PIL import Image
import requests
from functions import decode_features, get_model
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.pkl")
model = get_model(project=project,
                  model_name="xgboost_model",
                  evaluation_metric="f1_score",
                  sort_metrics_by="max")

def forecast():
    
    x = [ 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(np.asarray(x).reshape(-1, 1))
    
    return model_dir

demo = gr.Interface(
    fn=forecast,
    title="Air Quality Prediction",
    description="Get aqi value",
    allow_flagging="never",
    inputs=[],
    outputs=gr.Textbox(label="Result: "))

demo.launch()