Update app.py

app.py

@@ -0,0 +1,163 @@
+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.markdown(
+    f'''
+        <style>
+            .sidebar .sidebar-content {{
+                width: 1000px;
+            }}
+        </style>
+    ''',
+    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...')
+feature_view.init_batch_scoring(training_dataset_version=4)
+
+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.loc[X['date'] == max (X['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[[ "temp", "humidity",
+                                            "conditions", "aqi"]]
+
+cities_coords = {("Amsterdam", "Netherlands"): [52.377956, 4.897070]
+                 }
+
+
+
+
+
+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[0][column]}</td>
+                    </tr>"""
+    text += """</table>
+                    </h5>""" 
+
+city='Amsterdam'
+country='Netherlands'
+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="LSTM_model",
+                  evaluation_metric="mse",
+                  sort_metrics_by="min")
+
+X=np.reshape(np.array(X),(len(X),1,len(X.columns)))
+
+preds = model.predict(X)
+
+cities = [city_tuple[0] for city_tuple in cities_coords.keys()]
+
+next_day_date = datetime.today() + timedelta(days=1)
+last_day_date = datetime.today() + timedelta(days=7)
+
+next_day = next_day_date.strftime ('%d/%m/%Y')
+last_day=last_day_date.strftime ('%d/%m/%Y')
+days=list()
+temp_sec=int(latest_date_unix)
+for i in range(0,7):
+    temp_sec=temp_sec+(3600*24)
+
+    days.append(time.ctime(temp_sec)[4:-14])
+CQ=['Estimated AQI']
+
+df = pd.DataFrame(data=preds,index=CQ, columns=days, dtype=int)
+
+st.sidebar.write(df)
+progress_bar.progress(100)
+st.button("Re-run")
+#, columns=[f"AQI Predictions from {next_day} to {last_day}"]