Update app.py

app.py

@@ -40,7 +40,7 @@ with tab1:
     col1, col2 = st.columns(2)
 
     with col1:
-        st.markdown("**Parking place near building:**")
+        st.subheader("Parking place near building:")
 
         # Function to load the building model
         
@@ -69,12 +69,14 @@ with tab1:
     
         building_most_recent_prediction = building_new[['x', 'y', 'z']]
         building_most_recent_prediction = building_hist_model.predict(building_most_recent_prediction)
-        building_new['prediction'] = building_most_recent_prediction
-        building_new = building_new.set_index(['time'])
-        st.dataframe(building_new[['prediction']].tail(5))
+        building_new['Status'] = building_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(5))
 
     with col2:
-        st.markdown("**Parking place near bikelane:**")
+        st.subheader("Parking place near bikelane:")
             
         # Function to load the bikelane model
         @st.cache_data()
@@ -134,7 +136,7 @@ with tab2:
             label = "No vehicle detected"
         return label
         
-    st.markdown('**Experiment with building model:**')
+    st.subheader('Experiment with building model:')
     x_input_building = st.slider("Choose your x-value", -232, 909, 0)
     y_input_building = st.slider("Choose your y-value", -1112, 435, 0)
     z_input_building = st.slider("Choose your z-value", -1648, 226, 0)
@@ -145,7 +147,7 @@ with tab2:
     
     st.divider()
 
-    st.markdown('**Experiment with bikelane model:**')
+    st.subheader('Experiment with bikelane model:')
     x_input_bikelane = st.slider("Choose your x-value", -547, 288, 0)
     y_input_bikelane = st.slider("Choose your y-value", -1007, 786, 0)
     z_input_bikelane = st.slider("Choose your z-value", -1475, 16, 0)
@@ -155,6 +157,7 @@ with tab2:
         st.write(bikelane_input_prediction)
 
 with tab3:
+    st.subheader('About the application:')
     st.markdown('This application is made as part of the module "Data Engineering and Machine Learning Operations in Business - F2024" in Business Data Science 2nd Semester at Aalborg University Business School.')
     st.markdown('The application is made by Annika and Mikkel and is divided into 4 tabs:')
     st.markdown('*   **Parking lot status:** The first tab includes the actual interface, where the goal has been to make a simple UI which shows if 3 parking spaces are occupied or available.')
@@ -184,16 +187,21 @@ with tab4:
     #st.dataframe(batch_data.head())
   
 with tab5:
-    st.markdown('The predictions are made on the basis of two KNearestNeighbours models.')
 
-    st.write('**Model to predict parking place near building:**')
+    st.subheader('Model to predict parking place near building:')
+    st.markdown('The predictions for the parking place near the building are made on the basis of a KNearestNeighbours model')
     st.write(building_hist_model)
+    st.markdown('The accuracy if the bikelane model is 100%')
+    st.write('**Confusion matrix:**')
     st.image('building_hist_confusion_matrix.png', caption='Confusion matrix for building model')
 
     st.divider()
 
-    st.write('**Model to predict parking place near bikelane:**')
+    st.subheader('Model to predict parking place near bikelane:')
+    st.markdown('Just like with the other model, the predictions for the parking place near the bikelane are made on the basis of a KNearestNeighbours model')
     st.write(bikelane_hist_model)
+    st.markdown('The accuracy if the building model is 99%')
+    st.write('**Confusion matrix:**')
     st.image('bikelane_hist_confusion_matrix.png', caption='Confusion matrix for bikelane model')