Hugging Face's logo

Spaces:
kk90ujhun
/
oslo_air-quality
Sleeping

App Files Community
1
oslo_air-quality
File size: 4,457 Bytes 
2f6db43
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
141
142
143
144
145
146
147
148
149
 
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()
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)

# 1662652800000
X = feature_view.get_batch_data(start_time=1662652800000)
progress_bar.progress(50)

print(X.date.values)

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)
print("X is \n %s" % X)

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],
                 ("Kyiv", "Ukraine"): [50.450001, 30.523333]}

# if "Kyiv" in data_to_display["city"]:
#     cities_coords[("Kyiv", "Ukraine")]: [50.450001, 30.523333]
#     pass

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()]
print("cities are %s" % cities)

next_day_date = datetime.today() + timedelta(days=1)
next_day = next_day_date.strftime ('%d/%m/%Y')
print("preds is %s" % preds)
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")