Update app.py

app.py

@@ -72,16 +72,28 @@ with tab1:
 
         # Retrieving building data
         building_new = retrieve_building()
-        st.dataframe(building_new.tail(5))
 
-        ## Making the predictions and getting the latest data
-        #building_mag_most_recent_prediction = building_new[['x', 'y', 'z', 'temperature', 'et0_fao_evapotranspiration']]
-        #building_mag_most_recent_prediction_mag = building_mag_hist_model.predict(building_mag_most_recent_prediction)
-        #building_new['Status'] = building_mag_most_recent_prediction
-        #building_new['Status'].replace(['detection', 'no_detection'], ['Vehicle detected', 'No vehicle detected'], inplace=True)
-        #building_new = building_new.rename(columns={'time': 'Time'})
-        #building_new = building_new.set_index(['Time'])
-        #st.dataframe(building_new[['Status']].tail(3))       
+        building_mag_prediction_data = building_new[['time', 'x', 'y', 'z', 'temperature', 'et0_fao_evapotranspiration']]
+
+        def add_small_value_if_zero(value):
+            if value == 0.000000:
+                return value + 0.000001
+            else:
+                return value
+        
+        building_mag_prediction_data['et0_fao_evapotranspiration'] = building_mag_prediction_data['et0_fao_evapotranspiration'].apply(add_small_value_if_zero)
+
+        # Making the predictions and getting the latest data
+        building_mag_most_recent_prediction = building_new[['x', 'y', 'z', 'temperature', 'et0_fao_evapotranspiration']]
+        building_mag_most_recent_prediction_mag = building_mag_hist_model.predict(building_mag_most_recent_prediction)
+        building_new['Status'] = building_mag_most_recent_prediction
+        building_new['Status'].replace(['detection', 'no_detection'], ['Vehicle detected', 'No vehicle detected'], inplace=True)
+        building_new = building_new.rename(columns={'time': 'Time'})
+        building_new = building_new.set_index(['Time'])
+        st.dataframe(building_new[['Status']].tail(3))
+
+
+        st.dataframe(building_new.tail(5))
 
     with col2:
         st.subheader("Parking place near bikelane:")