Update app.py

app.py

@@ -37,68 +37,75 @@ with tab1:
     project = hopsworks.login(project = "miknie20", api_key_value=os.environ['HOPSWORKS_API_KEY'])
     fs = project.get_feature_store()
 
-    # 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()
-
-    # Function to load the building model
-    @st.cache_data()
-    def get_building_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")
-
-    # Retrieving model
-    building_hist_model = get_building_model()    
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.markdown("Parking place near building")
+
+        # Function to load the building model
+        
+        @st.cache_data()
+        def get_building_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")
+
+        # Retrieving model
+        building_hist_model = get_building_model()
     
-
-    # Loading the feature group with latest data for building
-    api_building_newest_fg = fs.get_feature_group(name = 'api_building_newest', 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
-
-    # Retrieving building data
-    building_new = retrieve_building()
+        # Loading the feature group with latest data for building
+        api_building_newest_fg = fs.get_feature_group(name = 'api_building_newest', 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
+
+        # Retrieving building data
+        building_new = retrieve_building()
     
-    st.markdown('Parking Space near Building:')
-    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)) 
-
-    # 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()
+        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))
+
+    with col2:
+        st.markdown("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()
     
-    st.markdown('Parking Space near Bikelane:')
-    bikelane_most_recent_prediction = bikelane_new[['x', 'y', 'z']]
-    bikelane_most_recent_prediction = bikelane_hist_model.predict(bikelane_most_recent_prediction)
-    bikelane_new['prediction'] = bikelane_most_recent_prediction
-    bikelane_new = bikelane_new.set_index(['time'])
-    st.dataframe(bikelane_new[['prediction']].tail(5)) 
+        st.markdown('Parking Space near Bikelane:')
+        bikelane_most_recent_prediction = bikelane_new[['x', 'y', 'z']]
+        bikelane_most_recent_prediction = bikelane_hist_model.predict(bikelane_most_recent_prediction)
+        bikelane_new['prediction'] = bikelane_most_recent_prediction
+        bikelane_new = bikelane_new.set_index(['time'])
+        st.dataframe(bikelane_new[['prediction']].tail(5)) 
 
     if st.button("Update status"):
         st.rerun()