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import streamlit as st | |
import hopsworks | |
import joblib | |
import pandas as pd | |
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🌩') | |
st.write(36 * "-") | |
fancy_header('\n Connecting to Hopsworks Feature Store...') | |
project = hopsworks.login() | |
st.write("Successfully connected!✔️") | |
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) | |
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") | |
st.sidebar.write("-" * 36) | |
preds = model.predict(data_encoder(weekly_data)).astype(int) | |
poll_level = get_aplevel(preds.T.reshape(-1, 1)) | |
next_week = [[(today + timedelta(days=d)).strftime('%Y-%m-%d'),(today + timedelta(days=d)).strftime('%A')] for d in range(1, 7)] | |
df = pd.DataFrame(data=[preds, poll_level], index=["AQI", "Air pollution level"], columns=[f"AQI Predictions for {next_day}" for next_day in next_week]) | |
st.write(df) | |
st.button("Re-run") |