Update app.py

app.py

@@ -25,6 +25,13 @@ warnings.filterwarnings("ignore")
 # Setting the title and adding text
 st.title('Parking Occupancy Detection System')
 
+# Defining functions
+def fill_nan_with_zero(value):
+    if pd.isna(value):
+        return 0
+    else:
+        return value
+
 # Creating tabs for the different features of the application
 tab1,tab2,tab3,tab4, tab5 = st.tabs(['Parking lot status', 'Magnetic Field Explorer', 'About', 'Dataset and visualisations', 'Model performance'])
 
@@ -73,26 +80,9 @@ with tab1:
         # Retrieving building data
         building_new = retrieve_building()
 
-        building_mag_prediction_data = building_new[['time', 'x', 'y', 'z', 'temperature', 'et0_fao_evapotranspiration']]
-
-        def fill_nan_with_zero(value):
-            if pd.isna(value):
-                return 0
-            else:
-                return value
-                
-        def add_small_value_if_zero(value):
-            if value == 0:
-                return value + 0.0001
-            else:
-                return value
-        
+        # Making the predictions and getting the latest data for magnetic field data
+        building_mag_prediction_data = building_new[['time', 'x', 'y', 'z', 'temperature', 'et0_fao_evapotranspiration']]                       
         building_mag_prediction_data['et0_fao_evapotranspiration'] = building_mag_prediction_data['et0_fao_evapotranspiration'].apply(fill_nan_with_zero)
-        #building_mag_prediction_data['et0_fao_evapotranspiration'] = building_mag_prediction_data['et0_fao_evapotranspiration'].apply(add_small_value_if_zero)
-        
-        st.dataframe(building_mag_prediction_data)
-
-        # Making the predictions and getting the latest data
         building_mag_most_recent_prediction = building_mag_prediction_data[['x', 'y', 'z', 'temperature', 'et0_fao_evapotranspiration']]
         building_mag_most_recent_prediction = building_mag_hist_model.predict(building_mag_most_recent_prediction)
         building_mag_prediction_data['Status'] = building_mag_most_recent_prediction
@@ -101,6 +91,17 @@ with tab1:
         building_mag_prediction_data = building_mag_prediction_data.set_index(['Time'])
         st.dataframe(building_mag_prediction_data[['Status']].tail(3))
 
+        # 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)
+        building_rad_most_recent_prediction = building_rad_prediction_data[['radar_0', 'radar_1', 'radar_2', 'radar_3', 'radar_4', 'radar_5', 'radar_6', 'radar_7', 'temperature', 'et0_fao_evapotranspiration']]
+        building_rad_most_recent_prediction = building_rad_hist_model.predict(building_rad_most_recent_prediction)
+        building_rad_prediction_data['Status'] = building_rad_most_recent_prediction
+        building_rad_prediction_data['Status'].replace(['detection', 'no_detection'], ['Vehicle detected', 'No vehicle detected'], inplace=True)
+        building_rad_prediction_data = building_rad_prediction_data.rename(columns={'time': 'Time'})
+        building_rad_prediction_data = building_rad_prediction_data.set_index(['Time'])
+        st.dataframe(building_rad_prediction_data[['Status']].tail(3))
+
 
     with col2:
         st.subheader("Parking place near bikelane:")