Update app.py

app.py

@@ -40,9 +40,6 @@ with tab1:
     project = hopsworks.login(project = "annikaij", api_key_value=os.environ['HOPSWORKS_API_KEY'])
     fs = project.get_feature_store()
 
-
-    st.subheader("Parking place near building:")
-
     # Function to load the building models
         
     @st.cache_data()
@@ -78,6 +75,8 @@ with tab1:
     col1, col2 = st.columns(2)
 
     with col1:
+        st.subheader("Prediction from magnetic field data on building:")
+        
         # Retrieving building data
         building_new = retrieve_building()
 
@@ -93,6 +92,8 @@ with tab1:
         st.dataframe(building_mag_prediction_data[['Status']].tail(3))
 
     with col2:
+        st.subheader("Prediction from radar data on building:")
+        
         # Making the predictions and getting the latest data for radar data
         building_rad_prediction_data = building_new[['time', 'radar_0', 'radar_1', 'radar_2', 'radar_3', 'radar_4', 'radar_5', 'radar_6', 'radar_7', 'temperature', 'et0_fao_evapotranspiration']]                       
         building_rad_prediction_data['et0_fao_evapotranspiration'] = building_rad_prediction_data['et0_fao_evapotranspiration'].apply(fill_nan_with_zero)
@@ -112,7 +113,6 @@ with tab1:
         st.experimental_rerun()
         
 with tab2:
-    st.subheader("Parking place near bikelane:")
 
     # Function to load the bikelane models
         
@@ -152,6 +152,7 @@ with tab2:
     col1, col2 = st.columns(2)
 
     with col1:    
+        st.subheader("Prediction from magnetic field data on bikelane:")
         # Making the predictions and getting the latest data for magnetic field data
         bikelane_mag_prediction_data = bikelane_new[['time', 'x', 'y', 'z', 'temperature', 'et0_fao_evapotranspiration']]                       
         bikelane_mag_prediction_data['et0_fao_evapotranspiration'] = bikelane_mag_prediction_data['et0_fao_evapotranspiration'].apply(fill_nan_with_zero)
@@ -163,7 +164,8 @@ with tab2:
         bikelane_mag_prediction_data = bikelane_mag_prediction_data.set_index(['Time'])
         st.dataframe(bikelane_mag_prediction_data[['Status']].tail(3))
 
-    with col2:          
+    with col2:  
+        st.subheader("Prediction from radar data on bikelane:")
         # Making the predictions and getting the latest data for radar data
         bikelane_rad_prediction_data = bikelane_new[['time', 'radar_0', 'radar_1', 'radar_2', 'radar_3', 'radar_4', 'radar_5', 'radar_6', 'radar_7', 'temperature', 'et0_fao_evapotranspiration']]                       
         bikelane_rad_prediction_data['et0_fao_evapotranspiration'] = bikelane_rad_prediction_data['et0_fao_evapotranspiration'].apply(fill_nan_with_zero)