Create app.py

app.py

@@ -0,0 +1,32 @@
+import streamlit as st
+import hopsworks
+import joblib
+import pandas as pd
+import datetime
+from functions import get_weather_data_weekly, data_encoder, get_aplevel, get_color
+
+
+today = datetime.date.today()
+city = "Paris"
+weekly_data = get_weather_data_weekly(city, today)
+
+mr = project.get_model_registry()
+model = mr.get_model("gradient_boost_model", version=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/model.pkl")
+
+
+preds = model.predict(data_encoder(weekly_data)).astype(int)
+
+air_pollution_level = ['Good', 'Moderate', 'Unhealthy for sensitive Groups','Unhealthy' ,'Very Unhealthy', 'Hazardous']
+poll_level = get_aplevel(preds.T.reshape(-1, 1), air_pollution_level)
+
+next_week_datetime = [today + datetime.timedelta(days=d) for d in range(7)]
+next_week_str = [f"{days.strftime('%A')}, {days.strftime('%Y-%m-%d')}" for days in next_week_datetime]
+
+df = pd.DataFrame(data=[preds, poll_level], index=["AQI", "Air pollution level"], columns=next_week_str)
+
+st.write("Here they are!")
+st.dataframe(df.style.apply(get_color, subset=(["Air pollution level"], slice(None))))
+
+st.button("Re-run")