Update app.py

app.py

@@ -88,18 +88,18 @@ with tab1:
                 return value
         
         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)
+        #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_new[['x', 'y', 'z', 'temperature', 'et0_fao_evapotranspiration']]
+        building_mag_most_recent_prediction = building_mag_prediction_data[['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['Status'] = building_mag_most_recent_prediction
+        building_mag_prediction_data['Status'].replace(['detection', 'no_detection'], ['Vehicle detected', 'No vehicle detected'], inplace=True)
+        building_mag_prediction_data = building_mag_prediction_data.rename(columns={'time': 'Time'})
+        building_mag_prediction_data = building_mag_prediction_data.set_index(['Time'])
+        st.dataframe(building_mag_prediction_data[['Status']].tail(3))
 
 
     with col2: