Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import hopsworks | |
| import joblib | |
| import pandas as pd | |
| import datetime | |
| from functions import get_weather_data_weekly, data_encoder, get_aplevel | |
| from PIL import Image | |
| def fancy_header(text, font_size=24): | |
| res = f'<p style="color:#ff5f27; font-size: {font_size}px;text-align:center">{text}</p>' | |
| st.markdown(res, unsafe_allow_html=True) | |
| vienna_image = Image.open('vienna.jpg') | |
| st.title('Air Quality Prediction Project 🌩') | |
| st.image(vienna_image, use_column_width='auto') | |
| st.write(36 * "-") | |
| st.markdown("# This is a final project in the course ID2223 Scalable Machine Learning and Deep Learning :computer:") | |
| st.markdown("My task was to predict the Air Quality Index (AQI) for one city (I choose Vienna) based on different weather data (pressure, snow-and cloud-coverage, temperature, etc.).") | |
| st.markdown("For the full list of weather data, please click [here][https://visualcrossing.com/resources/documentation/weather-api/timeline-weather-api]") | |
| fancy_header('\n Connecting to Hopsworks Feature Store...') | |
| project = hopsworks.login() | |
| st.write("Successfully connected!✔️") | |
| st.write(36 * "-") | |
| fancy_header('\n Collecting the weather data from Vienna...') | |
| today = datetime.date.today() | |
| city = "vienna" | |
| weekly_data = get_weather_data_weekly(city, today) | |
| st.write("Successfully collected!✔️") | |
| st.write(36 * "-") | |
| fancy_header("Loading the fitted XGBoost model...") | |
| 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.write("Succesfully loaded!✔️") | |
| st.sidebar.write("-" * 36) | |
| fancy_header("Making AQI pedictions for the next week..") | |
| preds = model.predict(data_encoder(weekly_data)).astype(int) | |
| poll_level = get_aplevel(preds.T.reshape(-1, 1)) | |
| next_week_datetime = [today + datetime.timedelta(days=d) for d in range(7)] | |
| next_week_str = [f"{days.strftime('%Y-%m-%d')}, {days.strftime('%A')}" for days in next_week_datetime] | |
| df = pd.DataFrame(data=[preds, poll_level], index=["AQI", "Air pollution level"], columns=next_week_str) | |
| st.write("Here they are!") | |
| st.dataframe(df.style.apply) # ref to function color_aq | |
| st.button("Re-run") |