Update app.py

app.py

@@ -30,28 +30,31 @@ today = datetime.date.today()
 city = "vienna"
 weekly_data = get_weather_data_weekly(city, today)
 
-progress_bar.progress(50)
-
+fancy_header('\n Acquired data!')
 progress_bar.progress(60)
 
 st.write(36 * "-")
 
+fancy_header('\n Loading the XGBoost model from the Hopsworks Model Registry')
+
 mr = project.get_model_registry()
 model = mr.get_best_model("aqi_model", "rmse", "min")
 model_dir = model.download()
 model = joblib.load(model_dir + "/aqi_model.pkl")
 
+fancy_header('\n Model loaded. Let's make predictions!')
+
 progress_bar.progress(80)
-st.sidebar.write("-" * 36)
 
+st.sidebar.write("-" * 36)
 
 preds = model.predict(data_encoder(weekly_data)).astype(int)
 poll_level = get_aplevel(preds.T.reshape(-1, 1))
 
-next_week = [(today + timedelta(days=d)).strftime('%A') for d in range(1, 7)]
+next_week = [[(today + timedelta(days=d)).strftime('%Y-%m-%d'),(today + timedelta(days=d)).strftime('%A')] for d in range(1, 7)]
 
-df = pd.DataFrame(data=preds, index=["eg"], columns=[f"AQI Predictions for {next_day}" for next_day in next_week], dtype=int)
+df = pd.DataFrame(data=[preds, poll_level], index=["AQI", "Air pollution level"], columns=[f"AQI Predictions for {next_day}" for next_day in next_week])
 
-st.sidebar.write(df)
+st.write(df)
 progress_bar.progress(100)
 st.button("Re-run")