Update app.py

app.py

@@ -75,7 +75,7 @@ with tab1:
     col1, col2 = st.columns(2)
 
     with col1:
-        st.subheader("Prediction from magnetic field data on building:")
+        st.subheader("Magnetic field prediction")
         
         # Retrieving building data
         building_new = retrieve_building()
@@ -92,7 +92,7 @@ with tab1:
         st.dataframe(building_mag_prediction_data[['Status']].tail(3))
 
     with col2:
-        st.subheader("Prediction from radar data on building:")
+        st.subheader("Radar prediction")
         
         # 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']]                       
@@ -152,7 +152,7 @@ with tab2:
     col1, col2 = st.columns(2)
 
     with col1:    
-        st.subheader("Prediction from magnetic field data on bikelane:")
+        st.subheader("Magnetic field prediction")
         # 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)
@@ -165,7 +165,7 @@ with tab2:
         st.dataframe(bikelane_mag_prediction_data[['Status']].tail(3))
 
     with col2:  
-        st.subheader("Prediction from radar data on bikelane:")
+        st.subheader("Radar prediction")
         # 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)