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import streamlit as st |
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import hopsworks |
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import joblib |
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import pandas as pd |
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import numpy as np |
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import folium |
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from streamlit_folium import st_folium, folium_static |
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import json |
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import time |
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from datetime import timedelta, datetime |
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from branca.element import Figure |
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from functions import decode_features, get_model |
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def fancy_header(text, font_size=24): |
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res = f'<span style="color:#ff5f27; font-size: {font_size}px;">{text}</span>' |
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st.markdown(res, unsafe_allow_html=True ) |
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st.title('⛅️Air Quality Prediction Project🌩') |
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progress_bar = st.sidebar.header('⚙️ Working Progress') |
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progress_bar = st.sidebar.progress(0) |
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st.write(36 * "-") |
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fancy_header('\n📡 Connecting to Hopsworks Feature Store...') |
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project = hopsworks.login(api_key_value="0rdWXlLgEd3mkGOg.iRZ7TtAkWGPlJHNQcAEph6Qbokoaq7QTBRI9ckwWUki8tIYGyBvrKhJvtLoUOGQ4") |
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fs = project.get_feature_store() |
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feature_view = fs.get_feature_view( |
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name = 'air_quality_fv', |
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version = 1 |
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) |
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st.write("Successfully connected!✔️") |
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progress_bar.progress(20) |
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st.write(36 * "-") |
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fancy_header('\n☁️ Getting batch data from Feature Store...') |
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start_date = datetime.now() - timedelta(days=1) |
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start_time = int(start_date.timestamp()) * 1000 |
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X = feature_view.get_batch_data(start_time=start_time) |
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progress_bar.progress(50) |
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latest_date_unix = str(X.date.values[0])[:10] |
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latest_date = time.ctime(int(latest_date_unix)) |
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st.write(f"⏱ Data for {latest_date}") |
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X = X.drop(columns=["date"]).fillna(0) |
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data_to_display = decode_features(X, feature_view=feature_view) |
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progress_bar.progress(60) |
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st.write(36 * "-") |
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fancy_header(f"🗺 Processing the map...") |
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fig = Figure(width=550,height=350) |
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my_map = folium.Map(location=[58, 20], zoom_start=3.71) |
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fig.add_child(my_map) |
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folium.TileLayer('Stamen Terrain').add_to(my_map) |
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folium.TileLayer('Stamen Toner').add_to(my_map) |
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folium.TileLayer('Stamen Water Color').add_to(my_map) |
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folium.TileLayer('cartodbpositron').add_to(my_map) |
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folium.TileLayer('cartodbdark_matter').add_to(my_map) |
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folium.LayerControl().add_to(my_map) |
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data_to_display = data_to_display[["city", "temp", "humidity", |
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"conditions", "aqi"]] |
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cities_coords = {("Sundsvall", "Sweden"): [62.390811, 17.306927], |
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("Stockholm", "Sweden"): [59.334591, 18.063240], |
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("Malmo", "Sweden"): [55.604981, 13.003822]} |
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if "Kyiv" in data_to_display["city"]: |
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cities_coords[("Kyiv", "Ukraine")]: [50.450001, 30.523333] |
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data_to_display = data_to_display.set_index("city") |
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cols_names_dict = {"temp": "Temperature", |
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"humidity": "Humidity", |
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"conditions": "Conditions", |
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"aqi": "AQI"} |
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data_to_display = data_to_display.rename(columns=cols_names_dict) |
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cols_ = ["Temperature", "Humidity", "AQI"] |
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data_to_display[cols_] = data_to_display[cols_].apply(lambda x: round(x, 1)) |
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for city, country in cities_coords: |
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text = f""" |
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<h4 style="color:green;">{city}</h4> |
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<h5 style="color":"green"> |
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<table style="text-align: right;"> |
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<tr> |
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<th>Country:</th> |
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<td><b>{country}</b></td> |
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</tr> |
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""" |
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for column in data_to_display.columns: |
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text += f""" |
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<tr> |
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<th>{column}:</th> |
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<td>{data_to_display.loc[city][column]}</td> |
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</tr>""" |
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text += """</table> |
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</h5>""" |
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folium.Marker( |
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cities_coords[(city, country)], popup=text, tooltip=f"<strong>{city}</strong>" |
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).add_to(my_map) |
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folium_static(my_map) |
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progress_bar.progress(80) |
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st.sidebar.write("-" * 36) |
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model = get_model(project=project, |
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model_name="gradient_boost_model", |
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evaluation_metric="f1_score", |
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sort_metrics_by="max") |
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preds = model.predict(X) |
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cities = [city_tuple[0] for city_tuple in cities_coords.keys()] |
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next_day_date = datetime.today() + timedelta(days=1) |
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next_day = next_day_date.strftime ('%d/%m/%Y') |
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df = pd.DataFrame(data=preds, index=cities, columns=[f"AQI Predictions for {next_day}"], dtype=int) |
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st.sidebar.write(df) |
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progress_bar.progress(100) |
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st.button("Re-run") |
