import streamlit as st
import hopsworks
import joblib
import pandas as pd
import numpy as np
from datetime import timedelta, datetime

from functions import *


def fancy_header(text, font_size=24):
    res = f'<span style="color:#ff5f27; font-size: {font_size}px;">{text}</span>'
    st.markdown(res, unsafe_allow_html=True )


st.title('Air Quality Prediction Project🌩')

progress_bar = st.sidebar.header('Working Progress')
progress_bar = st.sidebar.progress(0)
st.write(36 * "-")
fancy_header('\n Connecting to Hopsworks Feature Store...')

project = hopsworks.login()
fs = project.get_feature_store()
feature_view = fs.get_feature_view(
    name = 'air_quality_fv',
    version = 1
)

st.write("Successfully connected!✔️")
progress_bar.progress(20)

st.write(36 * "-")
fancy_header('\n Getting data from Feature Store...')

today = datetime.date.today()
city = "vienna"
weekly_data = get_weather_data_weekly(city, today)

progress_bar.progress(50)

#latest_date_unix = str(X.date.values[0])[:10]
#latest_date = time.ctime(int(latest_date_unix))

#st.write(f"Data for {latest_date}")

#data_to_display = decode_features(X, feature_view=feature_view)

progress_bar.progress(60)

st.write(36 * "-")

mr = project.get_model_registry()
model = mr.get_best_model("aqi_model", "rmse", "min")
model_dir = model.download()
model = joblib.load(model_dir + "/aqi_model.pkl")

progress_bar.progress(80)
st.sidebar.write("-" * 36)


preds = model.predict(data_encoder(weekly_data)).astype(int)
poll_level = get_aplevel(preds.T.reshape(-1, 1))

next_week = [(datetime.today() + timedelta(days=d)).strftime('%A') for d in range(1, 7)]

df = pd.DataFrame(data=preds, index=["eg"], columns=[f"AQI Predictions for {next_day}" for next_day in next_week], dtype=int)

st.sidebar.write(df)
progress_bar.progress(100)
st.button("Re-run")