# Loading packages from datetime import datetime, timedelta import joblib import pandas as pd import numpy as np import matplotlib.pyplot as plt import warnings import hopsworks import streamlit as st import json import os import seaborn as sns import time import random from sklearn.preprocessing import StandardScaler # Configuring the web page and setting the page title and icon st.set_page_config( page_title='Parking Occupacy Detection System', page_icon='🅿️', initial_sidebar_state='expanded') # Ignoring filtering warnings warnings.filterwarnings("ignore") # Setting the title and adding text st.title('Parking Occupancy Detection System') # Defining functions def fill_nan_with_zero(value): if pd.isna(value): return 0 else: return value # Creating tabs for the different features of the application tab1,tab2 = st.tabs(['Parking place near Building', 'Parking place near Bikelane']) with tab1: # Logging in to Hopsworks and loading the feature store project = hopsworks.login(project = "annikaij", api_key_value=os.environ['HOPSWORKS_API_KEY']) fs = project.get_feature_store() # Function to load the building models @st.cache_data() def get_building_mag_model(project=project): mr = project.get_model_registry() 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_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 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=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() col1, col2 = st.columns(2) with col1: st.subheader("Magnetic field prediction") # Making the predictions and getting the latest data for magnetic field data building_mag_prediction_data = building_new[['time', 'x', 'y', 'z', 'temperature', 'et0_fao_evapotranspiration']] building_mag_prediction_data['et0_fao_evapotranspiration'] = building_mag_prediction_data['et0_fao_evapotranspiration'].apply(fill_nan_with_zero) building_mag_most_recent_prediction = building_mag_prediction_data[['x', 'y', 'z', 'temperature', 'et0_fao_evapotranspiration']] building_mag_most_recent_prediction = building_mag_hist_model.predict(building_mag_most_recent_prediction) 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: st.subheader("Radar prediction") # Making the predictions and getting the latest data for radar data building_rad_prediction_data = building_new[['time', 'radar_0', 'radar_1', 'radar_2', 'radar_3', 'radar_4', 'radar_5', 'radar_6', 'radar_7', 'temperature', 'et0_fao_evapotranspiration']] building_rad_prediction_data['et0_fao_evapotranspiration'] = building_rad_prediction_data['et0_fao_evapotranspiration'].apply(fill_nan_with_zero) building_rad_most_recent_prediction = building_rad_prediction_data[['radar_0', 'radar_1', 'radar_2', 'radar_3', 'radar_4', 'radar_5', 'radar_6', 'radar_7', 'temperature', 'et0_fao_evapotranspiration']] building_rad_most_recent_prediction = building_rad_hist_model.predict(building_rad_most_recent_prediction) building_rad_prediction_data['Status'] = building_rad_most_recent_prediction building_rad_prediction_data['Status'].replace(['detection', 'no_detection'], ['Vehicle detected', 'No vehicle detected'], inplace=True) building_rad_prediction_data = building_rad_prediction_data.rename(columns={'time': 'Time'}) building_rad_prediction_data = building_rad_prediction_data.set_index(['Time']) st.dataframe(building_rad_prediction_data[['Status']].tail(3)) # Update button if st.button("Update Building"): # Clear cached data st.cache_data.clear() # Immediately rerun the application st.experimental_rerun() now = datetime.now() # Get current time today = now yesterday = today - timedelta(days=1) df_specific_time_range = building_new[(building_new['time'] >= yesterday) & (building_new['time'] <= now)] data_to_normalize = df_specific_time_range[['x', 'y', 'z']] # Applying StandardScaler scaler = StandardScaler() normalized_data = scaler.fit_transform(data_to_normalize) # Adding normalized data back to the DataFrame df_specific_time_range[['x', 'y', 'z']] = normalized_data # Streamlit plotting st.title('Normalized values of x, y, z from yesterday to today') # Converting the time column to string for better readability in Streamlit plots df_specific_time_range['time'] = df_specific_time_range['time'].astype(str) # Plotting using Streamlit's line chart st.line_chart(df_specific_time_range.set_index('time')[['x', 'y', 'z']]) with tab2: # Function to load the bikelane models @st.cache_data() def get_bikelane_mag_model(project=project): mr = project.get_model_registry() bikelane_mag_model = mr.get_model("bikelane_mag_hist_model", version = 2) bikelane_mag_model_dir = bikelane_mag_model.download() return joblib.load(bikelane_mag_model_dir + "/bikelane_mag_hist_model.pkl") # Retrieving model bikelane_mag_hist_model = get_bikelane_mag_model() @st.cache_data() def get_bikelane_rad_model(project=project): mr = project.get_model_registry() bikelane_rad_model = mr.get_model("bikelane_rad_hist_model", version = 2) bikelane_rad_model_dir = bikelane_rad_model.download() return joblib.load(bikelane_rad_model_dir + "/bikelane_rad_hist_model.pkl") # Retrieving model bikelane_rad_hist_model = get_bikelane_rad_model() # Loading the feature group with latest data for bikelane new_bikelane_fg = fs.get_feature_group(name = 'new_bikelane_fg', version = 1) # Function to loading the feature group with latest data for bikelane as a dataset @st.cache_data() def retrieve_bikelane(feature_group=new_bikelane_fg): new_bikelane_fg = feature_group.select_all() df_bikelane_new = new_bikelane_fg.read(read_options={"use_hive": True}) return df_bikelane_new # Retrieving bikelane data bikelane_new = retrieve_bikelane() col1, col2 = st.columns(2) with col1: st.subheader("Magnetic field prediction") # Making the predictions and getting the latest data for magnetic field data bikelane_mag_prediction_data = bikelane_new[['time', 'x', 'y', 'z', 'temperature', 'et0_fao_evapotranspiration']] bikelane_mag_prediction_data['et0_fao_evapotranspiration'] = bikelane_mag_prediction_data['et0_fao_evapotranspiration'].apply(fill_nan_with_zero) bikelane_mag_most_recent_prediction = bikelane_mag_prediction_data[['x', 'y', 'z', 'temperature', 'et0_fao_evapotranspiration']] bikelane_mag_most_recent_prediction = bikelane_mag_hist_model.predict(bikelane_mag_most_recent_prediction) bikelane_mag_prediction_data['Status'] = bikelane_mag_most_recent_prediction bikelane_mag_prediction_data['Status'].replace(['detection', 'no_detection'], ['Vehicle detected', 'No vehicle detected'], inplace=True) bikelane_mag_prediction_data = bikelane_mag_prediction_data.rename(columns={'time': 'Time'}) bikelane_mag_prediction_data = bikelane_mag_prediction_data.set_index(['Time']) st.dataframe(bikelane_mag_prediction_data[['Status']].tail(3)) with col2: st.subheader("Radar prediction") # Making the predictions and getting the latest data for radar data bikelane_rad_prediction_data = bikelane_new[['time', 'radar_0', 'radar_1', 'radar_2', 'radar_3', 'radar_4', 'radar_5', 'radar_6', 'radar_7', 'temperature', 'et0_fao_evapotranspiration']] bikelane_rad_prediction_data['et0_fao_evapotranspiration'] = bikelane_rad_prediction_data['et0_fao_evapotranspiration'].apply(fill_nan_with_zero) bikelane_rad_most_recent_prediction = bikelane_rad_prediction_data[['radar_0', 'radar_1', 'radar_2', 'radar_3', 'radar_4', 'radar_5', 'radar_6', 'radar_7', 'temperature', 'et0_fao_evapotranspiration']] bikelane_rad_most_recent_prediction = bikelane_rad_hist_model.predict(bikelane_rad_most_recent_prediction) bikelane_rad_prediction_data['Status'] = bikelane_rad_most_recent_prediction bikelane_rad_prediction_data['Status'].replace(['detection', 'no_detection'], ['Vehicle detected', 'No vehicle detected'], inplace=True) bikelane_rad_prediction_data = bikelane_rad_prediction_data.rename(columns={'time': 'Time'}) bikelane_rad_prediction_data = bikelane_rad_prediction_data.set_index(['Time']) st.dataframe(bikelane_rad_prediction_data[['Status']].tail(3)) # Update button if st.button("Update Bikelane"): # Clear cached data st.cache_data.clear() # Immediately rerun the application st.experimental_rerun()