Update app.py

app.py

@@ -15,7 +15,7 @@ import random
 
 # Configuring the web page and setting the page title and icon
 st.set_page_config(
-  page_title='Parking Occupacy Detection',
+  page_title='Parking Occupacy Detection System',
   page_icon='🅿️',
   initial_sidebar_state='expanded')
 
@@ -23,14 +23,14 @@ st.set_page_config(
 warnings.filterwarnings("ignore")
 
 # Setting the title and adding text
-st.title('Parking Occupancy Detection')
+st.title('Parking Occupancy Detection System')
 
 # 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'])
 
 with tab1:
     # Logging in to Hopsworks and loading the feature store
-    project = hopsworks.login(project = "miknie20", api_key_value=os.environ['HOPSWORKS_API_KEY'])
+    project = hopsworks.login(project = "annikaijak", api_key_value=os.environ['HOPSWORKS_API_KEY'])
     fs = project.get_feature_store()
 
     col1, col2 = st.columns(2)
@@ -38,75 +38,53 @@ with tab1:
     with col1:
         st.subheader("Parking place near building:")
 
-        # Function to load the building model
+        # Function to load the building models
         
         @st.cache_data()
-        def get_building_model(project=project):
+        def get_building_mag_model(project=project):
             mr = project.get_model_registry()
-            building_model = mr.get_model("building_hist_model", version = 2)
-            building_model_dir = building_model.download()
-            return joblib.load(building_model_dir + "/building_hist_model.pkl")
+            building_mag_model = mr.get_model("building_mag_hist_model", version = 2)
+            building_mag_model_dir = building_mag_model.download()
+            return joblib.load(building_mag_model_dir + "/building_mag_hist_model.pkl")
 
         # Retrieving model
-        building_hist_model = get_building_model()
+        building_mag_hist_model = get_building_mag_model()
+
+        @st.cache_data()
+        def get_building_rad_model(project=project):
+            mr = project.get_model_registry()
+            building_rad_model = mr.get_model("building_rad_hist_model", version = 2)
+            building_rad_model_dir = building_rad_model.download()
+            return joblib.load(building_rad_model_dir + "/building_rad_hist_model.pkl")
+
+        # Retrieving model
+        building_rad_hist_model = get_building_rad_model()
     
         # Loading the feature group with latest data for building
-        api_building_newest_fg = fs.get_feature_group(name = 'api_building_newest', version = 1)
+        new_building_fg = fs.get_feature_group(name = 'new_building_fg', version = 1)
 
         # Function to loading the feature group with latest data for building as a dataset
         @st.cache_data()
-        def retrieve_building(feature_group=api_building_newest_fg):
-            api_building_newest_fg = feature_group.select(["time", "x", "y", "z"])
-            df_building = api_building_newest_fg.read(read_options={"use_hive": True})           
-            return df_building
+        def retrieve_building(feature_group=new_building_fg):
+            new_building_fg = feature_group.select_all()
+            df_building_new = new_building_fg.read(read_options={"use_hive": True})           
+            return df_building_new
 
         # Retrieving building data
         building_new = retrieve_building()
 
         # Making the predictions and getting the latest data
-        building_most_recent_prediction = building_new[['x', 'y', 'z']]
-        building_most_recent_prediction = building_hist_model.predict(building_most_recent_prediction)
-        building_new['Status'] = building_most_recent_prediction
+        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(5))
+        st.dataframe(building_new[['Status']].tail(3))       
 
     with col2:
         st.subheader("Parking place near bikelane:")
             
-        # Function to load the bikelane model
-        @st.cache_data()
-        def get_bikelane_model(project=project):
-            mr = project.get_model_registry()
-            bikelane_model = mr.get_model("bikelane_hist_model", version = 1)
-            bikelane_model_dir = bikelane_model.download()
-            return joblib.load(bikelane_model_dir + "/bikelane_hist_model.pkl")
-
-        # Retrieving model
-        bikelane_hist_model = get_bikelane_model() 
-
-        # Loading the feature group with latest data for bikelane
-        api_bikelane_newest_fg = fs.get_feature_group(name = 'api_bikelane_newest', version = 1)
-
-        # Function to loading the feature group with latest data for building as a dataset
-        @st.cache_data()
-        def retrieve_bikelane(feature_group=api_bikelane_newest_fg):
-            api_bikelane_newest_fg = feature_group.select(["time", "x", "y", "z"])
-            df_bikelane = api_bikelane_newest_fg.read(read_options={"use_hive": True})
-            return df_bikelane
-
-        # Retrieving building data
-        bikelane_new = retrieve_bikelane()
-        
-        # Making the predictions and getting the latest data
-        bikelane_most_recent_prediction = bikelane_new[['x', 'y', 'z']]
-        bikelane_most_recent_prediction = bikelane_hist_model.predict(bikelane_most_recent_prediction)
-        bikelane_new['Status'] = bikelane_most_recent_prediction
-        bikelane_new['Status'].replace(['detection', 'no_detection'], ['Vehicle detected', 'No vehicle detected'], inplace=True)
-        bikelane_new = bikelane_new.rename(columns={'time': 'Time'})
-        bikelane_new = bikelane_new.set_index(['Time'])
-        st.dataframe(bikelane_new[['Status']].tail(5)) 
 
     # Update button
     if st.button("Update application"):
@@ -116,40 +94,6 @@ with tab1:
         st.experimental_rerun()
         
 with tab2:
-    # Defining a prediction function
-    def explore_magnetic_field(model, x, y, z):
-        input_list = [x, y, z]
-        res = model.predict(np.asarray(input_list).reshape(1,-1))
-        explorer_prediction = res[0]
-        if explorer_prediction == 'detection':
-            label = "Vehicle detected"
-        else:
-            label = "No vehicle detected"
-        return label
-
-    # Creating sliders for 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)
-
-    # Making a prediction button for building model
-    if st.button("Predict building input"):
-        building_input_prediction = explore_magnetic_field(building_hist_model, x_input_building, y_input_building, z_input_building)
-        st.write(building_input_prediction)
-    
-    st.divider()
-
-    # Creating sliders for 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)
-
-    # Making a prediction button for bikelane model
-    if st.button("Predict bikelane input"):
-        bikelane_input_prediction = explore_magnetic_field(bikelane_hist_model, x_input_bikelane, y_input_bikelane, z_input_bikelane)
-        st.write(bikelane_input_prediction)
 
 with tab3:
     st.subheader('About the application:')
@@ -162,74 +106,7 @@ with tab3:
     st.markdown('*   **Model Performance:** The fifth tab explains how the underlying Machine Learning Model performs and how the predictor works.')
 
 with tab4:
-    # Loading the feature group with historic data for building
-    api_building_detection_features_fg = fs.get_feature_group(name = 'api_building_detection_features', version = 1)
-
-    # Function to loading the feature group with latest data for building as a dataset
-    @st.cache_data()
-    def retrieve_building_historic(feature_group=api_building_detection_features_fg):
-        api_building_detection_features_fg = feature_group.select_all()
-        df_building_historic = api_building_detection_features_fg.read(read_options={"use_hive": True})           
-        return df_building_historic
-
-    # Retrieving building data
-    building_historic = retrieve_building_historic()
-
-    # Display historic building dataset overview
-    st.subheader("Overview of the historic dataframe for the parking place near the building")
-    st.dataframe(building_historic.head())
-
-    st.markdown('Here we can see an overview of the columns in the historic dataframe for the building parking place. There is some missing data in the radar columns, but that does not affect our detection model, as it is built using the magnetic field data.')
-    st.markdown('We have looked a bit into when there is most activity in the parking place. Looking at the two visualisations below, we can see that the parking place near the building has most changes between 03:00-06:00 and 10:00-15:00. We can also see that there is most activity on weekdays, which is as expected, as this parking place is outside an office.')    
-    st.image('building_dist_hour.png')
-    st.image('building_dist_week.png')
-
-    st.markdown('The labels used to train the detection model are made on the basis of UML clustering. The clusters for the building parking place can be seen below.')
-    st.image('building_cluster.png')
-    
-    st.divider()
-    
-    # Loading the feature group with historic data for bikelane
-    api_bikelane_detection_features_fg = fs.get_feature_group(name = 'api_bikelane_detection_features', version = 1)
-
-    # Function to loading the feature group with latest data for building as a dataset
-    @st.cache_data()
-    def retrieve_bikelane_historic(feature_group=api_bikelane_detection_features_fg):
-        api_bikelane_detection_features_fg = feature_group.select_all()
-        df_bikelane_historic = api_bikelane_detection_features_fg.read(read_options={"use_hive": True})           
-        return df_bikelane_historic
-
-    # Retrieving building data
-    bikelane_historic = retrieve_bikelane_historic()
-
-    # Display historic building dataset overview
-    st.subheader("Overview of the historic dataset for the parking place near the bikelane")
-    st.dataframe(bikelane_historic.head())  
-
-    st.markdown('Looking at the historic dataframe for the bikelane parking place, we can see that it is quite similar to the one for the building parking place.')   
-    st.markdown('There is a bit of difference in the hourly distribution of activity, as the parking place near the bikelane has most changes between 03:00-06:00 and 12:00-17:00. However, the weekly distribution shows the same.')    
-    st.image('bikelane_dist_hour.png')
-    st.image('bikelane_dist_week.png')
-
-    st.markdown('Here we can see the clusters used to label the bikelane parking place.')
-    st.image('bikelane_cluster.png')
-    
+ 
 with tab5:
 
-    # Model performance of building model
-    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()
-
-    # Model performance of bikelane model
-    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')
+