| import streamlit as st | |
| import hopsworks | |
| import joblib | |
| from datetime import date | |
| import pandas as pd | |
| from datetime import timedelta, datetime | |
| from functions import * | |
| def fancy_header(text, font_size=24): | |
| res = f'<p style="color:#ff5f72; font-size: {font_size}px; text-align:center;">{text}</p>' | |
| st.markdown(res, unsafe_allow_html=True) | |
| st.set_page_config(layout="wide") | |
| st.title('Air Quality Prediction Project🌩') | |
| st.write(9 * "-") | |
| fancy_header('\n Connecting to Hopsworks Feature Store...') | |
| project = hopsworks.login() | |
| st.write("Successfully connected!✔️") | |
| st.write(18 * "-") | |
| fancy_header('\n Getting data from Feature Store...') | |
| today = date.today() | |
| city = "Beijing" | |
| df_weather = get_weather_data_weekly(city, today) | |
| df_weather.date = df_weather.date.apply(timestamp_2_time) | |
| df_weather_x = df_weather.drop(columns=["date"]).fillna(0) | |
| st.write(27 * "-") | |
| mr = project.get_model_registry() | |
| model = mr.get_model("air_quality_modal_choosed", version=1) | |
| model_dir = model.download() | |
| model = joblib.load(model_dir + "/air_quality_model_choosed.pkl") | |
| st.write("-" * 36) | |
| preds = model.predict(df_weather_x).astype(int) | |
| poll_level = get_aplevel(preds.T.reshape(-1, 1)) | |
| next_week = [f"{(today + timedelta(days=d)).strftime('%Y-%m-%d')},{(today + timedelta(days=d)).strftime('%A')}" for d in range(8)] | |
| df = pd.DataFrame(data=[preds, poll_level], index=["AQI", "Air pollution level"], columns=next_week) | |
| st.write(df) | |
| st.button("Re-run") |