File size: 4,294 Bytes
e85573a 1c24593 e85573a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 |
import streamlit as st
import hopsworks
import joblib
import pandas as pd
import numpy as np
import folium
from streamlit_folium import st_folium, folium_static
import json
import time
from datetime import timedelta, datetime
from branca.element import Figure
from functions import decode_features, get_model
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(api_key_value="0rdWXlLgEd3mkGOg.iRZ7TtAkWGPlJHNQcAEph6Qbokoaq7QTBRI9ckwWUki8tIYGyBvrKhJvtLoUOGQ4")
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 batch data from Feature Store...')
start_date = datetime.now() - timedelta(days=1)
start_time = int(start_date.timestamp()) * 1000
X = feature_view.get_batch_data(start_time=start_time)
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}")
X = X.drop(columns=["date"]).fillna(0)
data_to_display = decode_features(X, feature_view=feature_view)
progress_bar.progress(60)
st.write(36 * "-")
fancy_header(f"🗺 Processing the map...")
fig = Figure(width=550,height=350)
my_map = folium.Map(location=[58, 20], zoom_start=3.71)
fig.add_child(my_map)
folium.TileLayer('Stamen Terrain').add_to(my_map)
folium.TileLayer('Stamen Toner').add_to(my_map)
folium.TileLayer('Stamen Water Color').add_to(my_map)
folium.TileLayer('cartodbpositron').add_to(my_map)
folium.TileLayer('cartodbdark_matter').add_to(my_map)
folium.LayerControl().add_to(my_map)
data_to_display = data_to_display[["city", "temp", "humidity",
"conditions", "aqi"]]
cities_coords = {("Sundsvall", "Sweden"): [62.390811, 17.306927],
("Stockholm", "Sweden"): [59.334591, 18.063240],
("Malmo", "Sweden"): [55.604981, 13.003822]}
if "Kyiv" in data_to_display["city"]:
cities_coords[("Kyiv", "Ukraine")]: [50.450001, 30.523333]
data_to_display = data_to_display.set_index("city")
cols_names_dict = {"temp": "Temperature",
"humidity": "Humidity",
"conditions": "Conditions",
"aqi": "AQI"}
data_to_display = data_to_display.rename(columns=cols_names_dict)
cols_ = ["Temperature", "Humidity", "AQI"]
data_to_display[cols_] = data_to_display[cols_].apply(lambda x: round(x, 1))
for city, country in cities_coords:
text = f"""
<h4 style="color:green;">{city}</h4>
<h5 style="color":"green">
<table style="text-align: right;">
<tr>
<th>Country:</th>
<td><b>{country}</b></td>
</tr>
"""
for column in data_to_display.columns:
text += f"""
<tr>
<th>{column}:</th>
<td>{data_to_display.loc[city][column]}</td>
</tr>"""
text += """</table>
</h5>"""
folium.Marker(
cities_coords[(city, country)], popup=text, tooltip=f"<strong>{city}</strong>"
).add_to(my_map)
# call to render Folium map in Streamlit
folium_static(my_map)
progress_bar.progress(80)
st.sidebar.write("-" * 36)
model = get_model(project=project,
model_name="gradient_boost_model",
evaluation_metric="f1_score",
sort_metrics_by="max")
preds = model.predict(X)
cities = [city_tuple[0] for city_tuple in cities_coords.keys()]
next_day_date = datetime.today() + timedelta(days=1)
next_day = next_day_date.strftime ('%d/%m/%Y')
df = pd.DataFrame(data=preds, index=cities, columns=[f"AQI Predictions for {next_day}"], dtype=int)
st.sidebar.write(df)
progress_bar.progress(100)
st.button("Re-run") |