Upload app.py

app.py

@@ -0,0 +1,197 @@
+import json
+import time
+import pickle
+import joblib
+
+import hopsworks 
+import streamlit as st
+from geopy import distance
+
+import plotly.express as px
+import folium
+from streamlit_folium import st_folium
+
+from functions import *
+
+
+
+def print_fancy_header(text, font_size=22, color="#ff5f27"):
+    res = f'<span style="color:{color}; font-size: {font_size}px;">{text}</span>'
+    st.markdown(res, unsafe_allow_html=True)  
+
+@st.cache_data()
+def get_batch_data_from_fs(td_version, date_threshold):
+    st.write(f"Retrieving the Batch data since {date_threshold}")
+    feature_view.init_batch_scoring(training_dataset_version=td_version)
+
+    batch_data = feature_view.get_batch_data(start_time=date_threshold)
+    return batch_data
+
+
+@st.cache_data()
+def download_model(name="air_quality_xgboost_model", version=1):
+    mr = project.get_model_registry()
+    retrieved_model = mr.get_model(
+        name="air_quality_xgboost_model",
+        version=1
+    )
+    saved_model_dir = retrieved_model.download()
+    return saved_model_dir
+
+
+
+def plot_pm2_5(df):
+    # create figure with plotly express
+    fig = px.line(df, x='date', y='pm2_5', color='city_name')
+
+    # customize line colors and styles
+    fig.update_traces(mode='lines+markers')
+    fig.update_layout({
+        'plot_bgcolor': 'rgba(0, 0, 0, 0)',
+        'paper_bgcolor': 'rgba(0, 0, 0, 0)',
+        'legend_title': 'City',
+        'legend_font': {'size': 12},
+        'legend_bgcolor': 'rgba(0, 0, 0, 0)',
+        'xaxis': {'title': 'Date'},
+        'yaxis': {'title': 'PM2.5'},
+        'shapes': [{
+            'type': 'line',
+            'x0': datetime.datetime.now().strftime('%Y-%m-%d'),
+            'y0': 0,
+            'x1': datetime.datetime.now().strftime('%Y-%m-%d'),
+            'y1': df['pm2_5'].max(),
+            'line': {'color': 'red', 'width': 2, 'dash': 'dashdot'}
+        }]
+    })
+
+    # show plot
+    st.plotly_chart(fig, use_container_width=True)
+
+
+with open('target_cities.json') as json_file:
+    target_cities = json.load(json_file)
+
+
+#########################
+st.title('🌫 Air Quality Prediction 🌦')
+
+st.write(3 * "-")
+print_fancy_header('\n📡 Connecting to Hopsworks Feature Store...')
+
+st.write("Logging... ")
+# (Attention! If the app has stopped at this step,
+# please enter your Hopsworks API Key in the commmand prompt.)
+project = hopsworks.login()
+fs = project.get_feature_store()
+st.write("✅ Logged in successfully!")
+
+st.write("Getting the Feature View...")
+feature_view = fs.get_feature_view(
+    name = 'air_quality_fv',
+    version = 1
+)
+st.write("✅ Success!")
+
+# I am going to load data for of last 60 days (for feature engineering)
+today = datetime.date.today()
+date_threshold = today - datetime.timedelta(days=60)
+
+st.write(3 * "-")
+print_fancy_header('\n☁️ Retriving batch data from Feature Store...')
+batch_data = get_batch_data_from_fs(td_version=1,
+                                    date_threshold=date_threshold)
+
+st.write("Batch data:")
+st.write(batch_data.sample(5))            
+
+
+saved_model_dir = download_model(
+    name="air_quality_xgboost_model",
+    version=1
+)
+
+pipeline = joblib.load(saved_model_dir + "/xgboost_pipeline.pkl")
+st.write("\n")
+st.write("✅ Model was downloaded and cached.")
+
+st.write(3 * '-')
+st.write("\n")
+print_fancy_header(text="🖍 Select the cities using the form below. \
+                         Click the 'Submit' button at the bottom of the form to continue.",
+                   font_size=22)
+dict_for_streamlit = {}
+for continent in target_cities:
+        for city_name, coords in target_cities[continent].items():
+            dict_for_streamlit[city_name] = coords
+selected_cities_full_list = []
+
+with st.form(key="user_inputs"):
+    print_fancy_header(text='\n🗺 Here you can choose cities from the drop-down menu',
+                       font_size=20, color="#00FFFF")
+    
+    cities_multiselect = st.multiselect(label='',
+                                        options=dict_for_streamlit.keys())
+    selected_cities_full_list.extend(cities_multiselect)
+    st.write("_" * 3)
+    print_fancy_header(text="\n📌 To add a city using the interactive map, click somewhere \
+                             (for the coordinates to appear)",
+                       font_size=20, color="#00FFFF")
+    
+    my_map = folium.Map(location=[42.57, -44.092], zoom_start=2)
+    # Add markers for each city
+    for city_name, coords in dict_for_streamlit.items():
+        folium.CircleMarker(
+            location=coords
+        ).add_to(my_map)
+
+    my_map.add_child(folium.LatLngPopup())
+    res_map = st_folium(my_map, width=640, height=480)
+    
+    try:
+        new_lat, new_long = res_map["last_clicked"]["lat"], res_map["last_clicked"]["lng"]
+
+        # Calculate the distance between the clicked location and each city
+        distances = {city: distance.distance(coord, (new_lat, new_long)).km for city, coord in dict_for_streamlit.items()}
+
+        # Find the city with the minimum distance and print its name
+        nearest_city = min(distances, key=distances.get)
+        print_fancy_header(text=f"You have selected {nearest_city} using map", font_size=18, color="#52fa23")
+        
+        selected_cities_full_list.append(nearest_city)
+        st.write(label_encoder.transform([nearest_city])[0])
+
+    except Exception as err:
+        print(err)
+        pass
+    
+    submit_button = st.form_submit_button(label='Submit')
+
+if submit_button:
+    st.write('Selected cities:', selected_cities_full_list)
+
+    st.write(3*'-')
+   
+    dataset = batch_data
+  
+    dataset = dataset.sort_values(by=["city_name", "date"])
+
+    st.write("\n")
+    print_fancy_header(text='\n🧠 Predicting PM2.5 for selected cities...',
+                       font_size=18, color="#FDF4F5")
+    st.write("")
+    preds = pd.DataFrame(columns=dataset.columns)
+    for city_name in selected_cities_full_list:
+        st.write(f"\t * {city_name}...")
+        features = dataset.loc[dataset['city_name'] == city_name]
+        print(features.head())
+        features['pm2_5'] = pipeline.predict(features)
+        preds = pd.concat([preds, features])
+    
+    st.write("")
+    print_fancy_header(text="📈Results 📉",
+                       font_size=22)
+    plot_pm2_5(preds[preds['city_name'].isin(selected_cities_full_list)])
+
+    st.write(3 * "-")
+    st.subheader('\n🎉 📈 🤝 App Finished Successfully 🤝 📈 🎉')
+    st.button("Re-run")