Spaces:
Sleeping
Sleeping
| """ | |
| MIT License | |
| Copyright (c) 2023 Khor Kean Teng, Ang Zhi Nuo, Connie Hui Kang Yi, Ling Sing Cheng, Tan Yu Jing | |
| Permission is hereby granted, free of charge, to any person obtaining a copy | |
| of this software and associated documentation files (the "Software"), to deal | |
| in the Software without restriction, including without limitation the rights | |
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| copies of the Software, and to permit persons to whom the Software is | |
| furnished to do so, subject to the following conditions: | |
| The above copyright notice and this permission notice shall be included in all | |
| copies or substantial portions of the Software. | |
| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| SOFTWARE. | |
| """ | |
| # load packages | |
| import pandas as pd | |
| import numpy as np | |
| import streamlit as st | |
| import geopandas as gpd | |
| from backend.functions import * | |
| try: | |
| from backend.configs import * | |
| except ImportError: | |
| pass | |
| import matplotlib.pyplot as plt | |
| from datetime import * | |
| import google.generativeai as palm | |
| # website settings | |
| # turn off the side bar by default | |
| st.set_page_config(layout="wide", initial_sidebar_state="auto") | |
| ## customize the side bar | |
| st.sidebar.title("🌍 Geo-Sustainable Jobs Solution") | |
| st.sidebar.caption("A Job Solution Prototype") | |
| # user input | |
| with st.sidebar: | |
| with st.expander("PaLM-2 API Configuration", expanded=True): | |
| st.caption(":red[You must enable API and input your own API key when using Streamlit web]") | |
| web_toggle = st.toggle('Enable API') | |
| api_input = st.text_input("Your Google API Token", type = "password", placeholder="Enter your API key here") | |
| st.caption("You can get your API key from https://developers.generativeai.google/") | |
| location_input = st.text_input( | |
| "Enter a location (Malaysia Only):", "University Malaya" | |
| ) | |
| state_input = st.selectbox( | |
| "Select a state:", | |
| [ | |
| "W.P Kuala Lumpur", | |
| "Johor", | |
| "Kedah", | |
| "Kelantan", | |
| "Melaka", | |
| "Negeri Sembilan", | |
| "Pahang", | |
| "Perak", | |
| "Perlis", | |
| "Pulau Pinang", | |
| "Sabah", | |
| "Sarawak", | |
| "Selangor", | |
| "Terengganu", | |
| "W.P Labuan", | |
| "W.P Putrajaya", | |
| ], | |
| ) | |
| # add exapander | |
| with st.expander("Your Job Profile", expanded=False): | |
| user_skills = st.text_input( | |
| "Enter Your Skills, Desired Sector & Qualifications:", | |
| "English, Leadership, Problem Solving, Malay, Program Planning", | |
| ) | |
| user_qualification = st.selectbox( | |
| "Enter Your Qualification:", | |
| ( | |
| "1-Skill Certificate 1", | |
| "2-Skill Certificate 2", | |
| "3-Skill Certificate 3", | |
| "4-Diploma", | |
| "5-Advanced Diploma", | |
| "6-Bachelor Degree", | |
| "7-Master Degree", | |
| "8-Doctorate Degree", | |
| ), | |
| ) | |
| user_sector = st.text_input("Enter Your Desired Sector:", "Teacher") | |
| submit = st.button("Compute", type="primary") | |
| st.subheader("📈 OpenDOSM Statistics") | |
| st.caption("Source: Labour Market Review, 2023 Q2") | |
| # setup the layout with columns | |
| col1, col2 = st.columns(spec=[0.7, 0.3], gap="small") | |
| # load data | |
| df = gpd.read_file("data/shapefile/polbnda_mys.shp") | |
| jobdata = pd.read_csv("data/job_posting_cleaned.csv") | |
| dosm_supply = pd.ExcelFile("data/dosm_supply.xlsx") | |
| dosm_demand = pd.ExcelFile("data/dosm_demand.xlsx") | |
| airports = gpd.read_file("data/airports/hotosm_mys_airports_polygons.shp") | |
| edu_fac = gpd.read_file( | |
| "data/edu_facilities/hotosm_mys_education_facilities_polygons.shp" | |
| ) | |
| financial = gpd.read_file( | |
| "data/financial_service/hotosm_mys_financial_services_polygons.shp" | |
| ) | |
| health = gpd.read_file( | |
| "data/health_facilities/hotosm_mys_health_facilities_polygons.shp" | |
| ) | |
| point_of_interest = gpd.read_file( | |
| "data/point_of_interest/hotosm_mys_points_of_interest_polygons.shp" | |
| ) | |
| seaports = gpd.read_file("data/sea_ports/hotosm_mys_sea_ports_polygons.shp") | |
| qualification_data = pd.read_excel("data/qualification level.xlsx") | |
| sectors_data = pd.read_excel("data/skill by sector.xlsx") | |
| course_suggestions_data = pd.read_excel("data/course suggestion.xlsx") | |
| vacancy_rate = pd.read_csv("data/Vacancy Rate.csv", index_col=[0], parse_dates=[0]) | |
| unemployment_rate = pd.read_csv("data/Unemployment Rate.csv") | |
| # configure geodataframe | |
| airports = gpd.GeoDataFrame(airports, geometry=airports.geometry, crs="EPSG:4326") | |
| edu_fac = gpd.GeoDataFrame(edu_fac, geometry=edu_fac.geometry, crs="EPSG:4326") | |
| financial = gpd.GeoDataFrame(financial, geometry=financial.geometry, crs="EPSG:4326") | |
| health = gpd.GeoDataFrame(health, geometry=health.geometry, crs="EPSG:4326") | |
| point_of_interest = gpd.GeoDataFrame( | |
| point_of_interest, geometry=point_of_interest.geometry, crs="EPSG:4326" | |
| ) | |
| seaports = gpd.GeoDataFrame(seaports, geometry=seaports.geometry, crs="EPSG:4326") | |
| # tidy up the data | |
| airports = airports[["name", "aeroway", "geometry"]] | |
| edu_fac = edu_fac[["name", "amenity", "geometry"]] | |
| financial = financial[["name", "amenity", "geometry"]] | |
| health = health[["healthcare", "name", "amenity", "geometry"]] | |
| point_of_interest = point_of_interest[ | |
| ["tourism", "name", "amenity", "shop", "geometry"] | |
| ] | |
| seaports = seaports[["name", "amenity", "geometry"]] | |
| vacancy_rate = vacancy_rate.dropna(axis=1, how="all") | |
| vacancy_rate.dropna(inplace=True) | |
| unemployment_rate = unemployment_rate.dropna(axis=1, how="all") | |
| # change the type of vacancy_rate_employment as data frame | |
| unemployment_rate = pd.DataFrame(unemployment_rate) | |
| unemployment_rate.iloc[:, 1:] = unemployment_rate.iloc[:, 1:].astype(float) | |
| unemployment_rate.iloc[:, 0] = pd.to_datetime(unemployment_rate.iloc[:, 0]) | |
| unemployment_rate = unemployment_rate.set_index("Date") | |
| # temp map display | |
| placeholder = st.empty() | |
| with placeholder.container(): | |
| m = leafmap.Map(center=[3.8, 101.4], zoom=7, google_map="HYBRID") | |
| m.to_streamlit(height=700) | |
| ############################## | |
| # forecast engine | |
| def dateofforecast(data): | |
| forecast_date = [ | |
| "2023-04-01", | |
| "2023-07-01", | |
| "2023-10-01", | |
| "2024-01-01", | |
| "2024-04-01", | |
| "2024-07-01", | |
| "2024-10-01", | |
| "2025-01-01", | |
| ] | |
| forecast_date = pd.to_datetime(forecast_date) | |
| data.index = forecast_date | |
| # ETS prediction | |
| VETS_Kedah = ets_fore_a(vacancy_rate["Kedah"], 6) | |
| VETS_Melaka = ets_fore_a(vacancy_rate["Melaka"], 6) | |
| VETS_Negeri_Sembilan = ets_fore_c(vacancy_rate["Negeri Sembilan"], 6) | |
| VETS_Pahang = ets_fore_a(vacancy_rate["Pahang"], 6) | |
| VETS_Perlis = ets_fore_c(vacancy_rate["Perlis"], 6) | |
| VETS_Terengganu = ets_fore_a(vacancy_rate["Terengganu"], 6) | |
| VETS_Sabah = ets_fore_a(vacancy_rate["Sabah"], 6) | |
| VETS_Sarawak = ets_fore_a(vacancy_rate["Sarawak"], 6) | |
| VETS_Putrajaya = ets_fore_a(vacancy_rate["W.P Putrajaya"], 6) | |
| # ARIMA prediction | |
| VETS_Johor = ARIMA_fore(vacancy_rate["Johor"]) | |
| VETS_Kelantan = ARIMA_fore(vacancy_rate["Kelantan"]) | |
| VETS_Penang = ARIMA_fore(vacancy_rate["Pulau Pinang"]) | |
| VETS_Perak = ARIMA_fore(vacancy_rate["Perak"]) | |
| VETS_Selangor = ARIMA_fore(vacancy_rate["Selangor"]) | |
| VETS_Kuala_Lumpur = ARIMA_fore(vacancy_rate["W.P Kuala Lumpur"]) | |
| VETS_Labuan = ARIMA_fore(vacancy_rate["W.P Labuan"]) | |
| VETS_Total = ARIMA_fore(vacancy_rate["Total"]) | |
| # Change the Starting date of the forecast | |
| dateofforecast(VETS_Kedah) | |
| dateofforecast(VETS_Melaka) | |
| dateofforecast(VETS_Negeri_Sembilan) | |
| dateofforecast(VETS_Pahang) | |
| dateofforecast(VETS_Perlis) | |
| dateofforecast(VETS_Terengganu) | |
| dateofforecast(VETS_Sabah) | |
| dateofforecast(VETS_Sarawak) | |
| dateofforecast(VETS_Putrajaya) | |
| dateofforecast(VETS_Johor) | |
| dateofforecast(VETS_Kelantan) | |
| dateofforecast(VETS_Penang) | |
| dateofforecast(VETS_Perak) | |
| dateofforecast(VETS_Selangor) | |
| dateofforecast(VETS_Kuala_Lumpur) | |
| dateofforecast(VETS_Labuan) | |
| dateofforecast(VETS_Total) | |
| # ETS Model | |
| Johor_vpred = ets_fore_a(unemployment_rate[["Johor"]], 6) | |
| Kedah_pred = ets_fore_d(unemployment_rate[["Kedah"]], 6) | |
| Kelantan_pred = ets_fore_d(unemployment_rate[["Kelantan"]], 6) | |
| Melaka_pred = ets_fore_b(unemployment_rate[["Melaka"]], 6) | |
| Pahang_pred = ets_fore_c(unemployment_rate[["Pahang"]], 6) | |
| Perak_pred = ets_fore_c(unemployment_rate[["Perak"]], 6) | |
| Perlis_pred = ets_fore_c(unemployment_rate[["Perlis"]], 6) | |
| Sabah_pred = ets_fore_a(unemployment_rate[["Sabah"]], 6) | |
| Sarawak_pred = ets_fore_d(unemployment_rate[["Sarawak"]], 6) | |
| Selangor_pred = ets_fore_d(unemployment_rate[["Selangor"]], 6) | |
| Terengganu_pred = ets_fore_b(unemployment_rate[["Terengganu"]], 6) | |
| Labuan_pred = ets_fore_d(unemployment_rate["W.P Labuan"], 6) | |
| # ARIMA Model | |
| UARIMA_N9_pred = ARIMA_fore(unemployment_rate[["Negeri Sembilan"]]) | |
| UARIMA_Penang_pred = ARIMA_fore(unemployment_rate[["Pulau Pinang"]]) | |
| UARIMA_KL_pred = ARIMA_fore(unemployment_rate[["W.P Kuala Lumpur"]]) | |
| UARIMA_Putrajaya_pred = ARIMA_fore(unemployment_rate[["W.P Putrajaya"]]) | |
| UARIMA_Malaysia_pred = ARIMA_fore(unemployment_rate[["Total"]]) | |
| # Change the Starting date of the forecast | |
| dateofforecast(Johor_vpred) | |
| dateofforecast(Kedah_pred) | |
| dateofforecast(Kelantan_pred) | |
| dateofforecast(Melaka_pred) | |
| dateofforecast(Pahang_pred) | |
| dateofforecast(Perak_pred) | |
| dateofforecast(Perlis_pred) | |
| dateofforecast(Sabah_pred) | |
| dateofforecast(Sarawak_pred) | |
| dateofforecast(Selangor_pred) | |
| dateofforecast(Terengganu_pred) | |
| dateofforecast(Labuan_pred) | |
| dateofforecast(UARIMA_N9_pred) | |
| dateofforecast(UARIMA_Penang_pred) | |
| dateofforecast(UARIMA_KL_pred) | |
| dateofforecast(UARIMA_Putrajaya_pred) | |
| dateofforecast(UARIMA_Malaysia_pred) | |
| vacancy_rate.dateformat = vacancy_rate.index.strftime("%Y-%m-%d") | |
| unemployment_rate.dateformat = unemployment_rate.index.strftime("%Y-%d-%m") | |
| plt.rcParams["figure.figsize"] = (20, 12) | |
| def forecast_plot(state): | |
| state = state.title() | |
| if state == "Kedah": | |
| fig, ax1 = plt.subplots() | |
| plt.figure(figsize=(12, 8)) | |
| ax1.plot(vacancy_rate.index, vacancy_rate["Kedah"], label="Vacancy Rate") | |
| ax1.plot(VETS_Kedah, label="Forecast of Vacancy Rate") | |
| ax1.legend(loc="upper left") | |
| ax1.set_xlabel("Date") | |
| ax1.set_ylabel("Vacancy Rate") | |
| ax2 = ax1.twinx() | |
| ax2.plot( | |
| unemployment_rate.index, | |
| unemployment_rate["Kedah"], | |
| "r", | |
| label="Unemployment Rate", | |
| ) | |
| ax2.plot(Kedah_pred, "b", label="Forecast of Unemployment Rate") | |
| ax2.legend(loc="upper left", bbox_to_anchor=(0, 0.95)) | |
| ax2.set_ylabel("Unemployment Rate") | |
| plt.rcParams["figure.figsize"] = (20, 12) | |
| st.pyplot(fig) | |
| elif state == "Johor": | |
| fig, ax1 = plt.subplots() | |
| plt.figure(figsize=(12, 8)) | |
| ax1.plot(vacancy_rate.index, vacancy_rate["Johor"], label="Vacancy Rate") | |
| ax1.plot(VETS_Johor, label="Forecast of Vacancy Rate") | |
| ax1.legend(loc="upper left") | |
| ax1.set_xlabel("Date") | |
| ax1.set_ylabel("Vacancy Rate") | |
| ax2 = ax1.twinx() | |
| ax2.plot( | |
| unemployment_rate.index, | |
| unemployment_rate["Johor"], | |
| "r", | |
| label="Unemployment Rate", | |
| ) | |
| ax2.plot(Johor_vpred, "b", label="Forecast of Unemployment Rate") | |
| ax2.legend(loc="upper left", bbox_to_anchor=(0, 0.95)) | |
| ax2.set_ylabel("Unemployment Rate") | |
| plt.rcParams["figure.figsize"] = (20, 12) | |
| st.pyplot(fig) | |
| elif state == "Kelantan": | |
| fig, ax1 = plt.subplots() | |
| plt.figure(figsize=(12, 8)) | |
| ax1.plot(vacancy_rate.index, vacancy_rate["Kelantan"], label="Vacancy Rate") | |
| ax1.plot(VETS_Kelantan, label="Forecast of Vacancy Rate") | |
| ax1.legend(loc="upper left") | |
| ax1.set_xlabel("Date") | |
| ax1.set_ylabel("Vacancy Rate") | |
| ax2 = ax1.twinx() | |
| ax2.plot( | |
| unemployment_rate.index, | |
| unemployment_rate["Kelantan"], | |
| "r", | |
| label="Unemployment Rate", | |
| ) | |
| ax2.plot(Kelantan_pred, "b", label="Forecast of Unemployment Rate") | |
| ax2.legend(loc="upper left", bbox_to_anchor=(0, 0.95)) | |
| ax2.set_ylabel("Unemployment Rate") | |
| plt.rcParams["figure.figsize"] = (20, 12) | |
| st.pyplot(fig) | |
| elif state == "Melaka": | |
| fig, ax1 = plt.subplots() | |
| plt.figure(figsize=(12, 8)) | |
| ax1.plot(vacancy_rate.index, vacancy_rate["Melaka"], label="Vacancy Rate") | |
| ax1.plot(VETS_Melaka, label="Forecast of Vacancy Rate") | |
| ax1.legend(loc="upper left") | |
| ax1.set_xlabel("Date") | |
| ax1.set_ylabel("Vacancy Rate") | |
| ax2 = ax1.twinx() | |
| ax2.plot( | |
| unemployment_rate.index, | |
| unemployment_rate["Melaka"], | |
| "r", | |
| label="Unemployment Rate", | |
| ) | |
| ax2.plot(Melaka_pred, "b", label="Forecast of Unemployment Rate") | |
| ax2.legend(loc="upper left", bbox_to_anchor=(0, 0.95)) | |
| ax2.set_ylabel("Unemployment Rate") | |
| plt.rcParams["figure.figsize"] = (20, 12) | |
| st.pyplot(fig) | |
| elif state == "Negeri Sembilan": | |
| fig, ax1 = plt.subplots() | |
| plt.figure(figsize=(12, 8)) | |
| ax1.plot( | |
| vacancy_rate.index, vacancy_rate["Negeri Sembilan"], label="Vacancy Rate" | |
| ) | |
| ax1.plot(VETS_Negeri_Sembilan, label="Forecast of Vacancy Rate") | |
| ax1.legend(loc="upper left") | |
| ax1.set_xlabel("Date") | |
| ax1.set_ylabel("Vacancy Rate") | |
| ax2 = ax1.twinx() | |
| ax2.plot( | |
| unemployment_rate.index, | |
| unemployment_rate["Negeri Sembilan"], | |
| "r", | |
| label="Unemployment Rate", | |
| ) | |
| ax2.plot(UARIMA_N9_pred, "b", label="Forecast of Unemployment Rate") | |
| ax2.legend(loc="upper left", bbox_to_anchor=(0, 0.95)) | |
| ax2.set_ylabel("Unemployment Rate") | |
| plt.rcParams["figure.figsize"] = (20, 12) | |
| st.pyplot(fig) | |
| elif state == "Pahang": | |
| fig, ax1 = plt.subplots() | |
| plt.figure(figsize=(12, 8)) | |
| ax1.plot(vacancy_rate.index, vacancy_rate["Pahang"], label="Vacancy Rate") | |
| ax1.plot(VETS_Pahang, label="Forecast of Vacancy Rate") | |
| ax1.legend(loc="upper left") | |
| ax1.set_xlabel("Date") | |
| ax1.set_ylabel("Vacancy Rate") | |
| ax2 = ax1.twinx() | |
| ax2.plot( | |
| unemployment_rate.index, | |
| unemployment_rate["Pahang"], | |
| "r", | |
| label="Unemployment Rate", | |
| ) | |
| ax2.plot(Pahang_pred, "b", label="Forecast of Unemployment Rate") | |
| ax2.legend(loc="upper left", bbox_to_anchor=(0, 0.95)) | |
| ax2.set_ylabel("Unemployment Rate") | |
| plt.rcParams["figure.figsize"] = (20, 12) | |
| st.pyplot(fig) | |
| elif state == "Penang": | |
| fig, ax1 = plt.subplots() | |
| plt.figure(figsize=(12, 8)) | |
| ax1.plot(vacancy_rate.index, vacancy_rate["Pulau Pinang"], label="Vacancy Rate") | |
| ax1.plot(VETS_Penang, label="Forecast of Vacancy Rate") | |
| ax1.legend(loc="upper left") | |
| ax1.set_xlabel("Date") | |
| ax1.set_ylabel("Vacancy Rate") | |
| ax2 = ax1.twinx() | |
| ax2.plot( | |
| unemployment_rate.index, | |
| unemployment_rate["Pulau Pinang"], | |
| "r", | |
| label="Unemployment Rate", | |
| ) | |
| ax2.plot(UARIMA_Penang_pred, "b", label="Forecast of Unemployment Rate") | |
| ax2.legend(loc="upper left", bbox_to_anchor=(0, 0.95)) | |
| ax2.set_ylabel("Unemployment Rate") | |
| plt.rcParams["figure.figsize"] = (20, 12) | |
| st.pyplot(fig) | |
| elif state == "Perak": | |
| fig, ax1 = plt.subplots() | |
| plt.figure(figsize=(12, 8)) | |
| ax1.plot(vacancy_rate.index, vacancy_rate["Perak"], label="Vacancy Rate") | |
| ax1.plot(VETS_Perak, label="Forecast of Vacancy Rate") | |
| ax1.legend(loc="upper left") | |
| ax1.set_xlabel("Date") | |
| ax1.set_ylabel("Vacancy Rate") | |
| ax2 = ax1.twinx() | |
| ax2.plot( | |
| unemployment_rate.index, | |
| unemployment_rate["Perak"], | |
| "r", | |
| label="Unemployment Rate", | |
| ) | |
| ax2.plot(Perak_pred, "b", label="Forecast of Unemployment Rate") | |
| ax2.legend(loc="upper left", bbox_to_anchor=(0, 0.95)) | |
| ax2.set_ylabel("Unemployment Rate") | |
| plt.rcParams["figure.figsize"] = (20, 12) | |
| st.pyplot(fig) | |
| elif state == "Perlis": | |
| fig, ax1 = plt.subplots() | |
| plt.figure(figsize=(12, 8)) | |
| ax1.plot(vacancy_rate.index, vacancy_rate["Perlis"], label="Vacancy Rate") | |
| ax1.plot(VETS_Perlis, label="Forecast of Vacancy Rate") | |
| ax1.legend(loc="upper left") | |
| ax1.set_xlabel("Date") | |
| ax1.set_ylabel("Vacancy Rate") | |
| ax2 = ax1.twinx() | |
| ax2.plot(Perlis_pred, "b", label="Forecast of Unemployment Rate") | |
| ax2.plot( | |
| unemployment_rate.index, | |
| unemployment_rate["Perlis"], | |
| "r", | |
| label="Unemployment Rate", | |
| ) | |
| ax2.legend(loc="upper left", bbox_to_anchor=(0, 0.95)) | |
| ax2.set_ylabel("Unemployment Rate") | |
| plt.rcParams["figure.figsize"] = (20, 12) | |
| st.pyplot(fig) | |
| elif state == "Sabah": | |
| fig, ax1 = plt.subplots() | |
| plt.figure(figsize=(12, 8)) | |
| ax1.plot(vacancy_rate.index, vacancy_rate["Sabah"], label="Vacancy Rate") | |
| ax1.plot(VETS_Sabah, label="Forecast of Vacancy Rate") | |
| ax1.legend(loc="upper left") | |
| ax1.set_xlabel("Date") | |
| ax1.set_ylabel("Vacancy Rate") | |
| ax2 = ax1.twinx() | |
| ax2.plot(Sabah_pred, "b", label="Forecast of Unemployment Rate") | |
| ax2.plot( | |
| unemployment_rate.index, | |
| unemployment_rate["Sabah"], | |
| "r", | |
| label="Unemployment Rate", | |
| ) | |
| ax2.legend(loc="upper left", bbox_to_anchor=(0, 0.95)) | |
| ax2.set_ylabel("Unemployment Rate") | |
| plt.rcParams["figure.figsize"] = (20, 12) | |
| st.pyplot(fig) | |
| elif state == "Sarawak": | |
| fig, ax1 = plt.subplots() | |
| plt.figure(figsize=(12, 8)) | |
| ax1.plot(vacancy_rate.index, vacancy_rate["Sarawak"], label="Vacancy Rate") | |
| ax1.plot(VETS_Sarawak, label="Forecast of Vacancy Rate") | |
| ax1.legend(loc="upper left") | |
| ax1.set_xlabel("Date") | |
| ax1.set_ylabel("Vacancy Rate") | |
| ax2 = ax1.twinx() | |
| ax2.plot(Sarawak_pred, "b", label="Forecast of Unemployment Rate") | |
| ax2.plot( | |
| unemployment_rate.index, | |
| unemployment_rate["Sarawak"], | |
| "r", | |
| label="Unemployment Rate", | |
| ) | |
| ax2.legend(loc="upper left", bbox_to_anchor=(0, 0.95)) | |
| ax2.set_ylabel("Unemployment Rate") | |
| plt.rcParams["figure.figsize"] = (20, 12) | |
| st.pyplot(fig) | |
| elif state == "Selangor": | |
| fig, ax1 = plt.subplots() | |
| plt.figure(figsize=(12, 8)) | |
| ax1.plot(vacancy_rate.index, vacancy_rate["Selangor"], label="Vacancy Rate") | |
| ax1.plot(VETS_Selangor, label="Forecast of Vacancy Rate") | |
| ax1.legend(loc="upper left") | |
| ax1.set_xlabel("Date") | |
| ax1.set_ylabel("Vacancy Rate") | |
| ax2 = ax1.twinx() | |
| ax2.plot(Selangor_pred, "b", label="Forecast of Unemployment Rate") | |
| ax2.plot( | |
| unemployment_rate.index, | |
| unemployment_rate["Selangor"], | |
| "r", | |
| label="Unemployment Rate", | |
| ) | |
| ax2.legend(loc="upper left", bbox_to_anchor=(0, 0.95)) | |
| ax2.set_ylabel("Unemployment Rate") | |
| plt.rcParams["figure.figsize"] = (20, 12) | |
| st.pyplot(fig) | |
| elif state == "Terengganu": | |
| fig, ax1 = plt.subplots() | |
| plt.figure(figsize=(12, 8)) | |
| ax1.plot(vacancy_rate.index, vacancy_rate["Terengganu"], label="Vacancy Rate") | |
| ax1.plot(VETS_Terengganu, label="Forecast of Vacancy Rate") | |
| ax1.legend(loc="upper left") | |
| ax1.set_xlabel("Date") | |
| ax1.set_ylabel("Vacancy Rate") | |
| ax2 = ax1.twinx() | |
| ax2.plot(Terengganu_pred, "b", label="Forecast of Unemployment Rate") | |
| ax2.plot( | |
| unemployment_rate.index, | |
| unemployment_rate["Terengganu"], | |
| "r", | |
| label="Unemployment Rate", | |
| ) | |
| ax2.legend(loc="upper left", bbox_to_anchor=(0, 0.95)) | |
| ax2.set_ylabel("Unemployment Rate") | |
| plt.rcParams["figure.figsize"] = (20, 12) | |
| st.pyplot(fig) | |
| elif state == "W.P Kuala Lumpur": | |
| fig, ax1 = plt.subplots() | |
| plt.figure(figsize=(12, 8)) | |
| ax1.plot( | |
| vacancy_rate.index, vacancy_rate["W.P Kuala Lumpur"], label="Vacancy Rate" | |
| ) | |
| ax1.plot(VETS_Kuala_Lumpur, label="Forecast of Vacancy Rate") | |
| ax1.legend(loc="upper left") | |
| ax1.set_xlabel("Date") | |
| ax1.set_ylabel("Vacancy Rate") | |
| ax2 = ax1.twinx() | |
| ax2.plot(UARIMA_KL_pred, "b", label="Forecast of Unemployment Rate") | |
| ax2.plot( | |
| unemployment_rate.index, | |
| unemployment_rate["W.P Kuala Lumpur"], | |
| "r", | |
| label="Unemployment Rate", | |
| ) | |
| ax2.legend(loc="upper left", bbox_to_anchor=(0, 0.95)) | |
| ax2.set_ylabel("Unemployment Rate") | |
| plt.rcParams["figure.figsize"] = (20, 12) | |
| st.pyplot(fig) | |
| elif state == "W.P Labuan": | |
| fig, ax1 = plt.subplots() | |
| plt.figure(figsize=(12, 8)) | |
| ax1.plot(vacancy_rate.index, vacancy_rate["W.P Labuan"], label="Vacancy Rate") | |
| ax1.plot(VETS_Labuan, label="Forecast of Vacancy Rate") | |
| ax1.legend(loc="upper left") | |
| ax1.set_xlabel("Date") | |
| ax1.set_ylabel("Vacancy Rate") | |
| ax2 = ax1.twinx() | |
| ax2.plot(Labuan_pred, "b", label="Forecast of Unemployment Rate") | |
| ax2.plot( | |
| unemployment_rate.index, | |
| unemployment_rate["W.P Labuan"], | |
| "r", | |
| label="Unemployment Rate", | |
| ) | |
| ax2.legend(loc="upper left", bbox_to_anchor=(0, 0.95)) | |
| ax2.set_ylabel("Unemployment Rate") | |
| plt.rcParams["figure.figsize"] = (20, 12) | |
| st.pyplot(fig) | |
| elif state == "W.P Putrajaya": | |
| fig, ax1 = plt.subplots() | |
| plt.figure(figsize=(12, 8)) | |
| ax1.plot( | |
| vacancy_rate.index, vacancy_rate["W.P Putrajaya"], label="Vacancy Rate" | |
| ) | |
| ax1.plot(VETS_Putrajaya, label="Forecast of Vacancy Rate") | |
| ax1.legend(loc="upper left") | |
| ax1.set_xlabel("Date") | |
| ax1.set_ylabel("Vacancy Rate") | |
| ax2 = ax1.twinx() | |
| ax2.plot(UARIMA_Putrajaya_pred, "b", label="Forecast of Unemployment Rate") | |
| ax2.plot( | |
| unemployment_rate.index, | |
| unemployment_rate["W.P Putrajaya"], | |
| "r", | |
| label="Unemployment Rate", | |
| ) | |
| ax2.legend(loc="upper left", bbox_to_anchor=(0, 0.95)) | |
| ax2.set_ylabel("Unemployment Rate") | |
| plt.rcParams["figure.figsize"] = (20, 12) | |
| st.pyplot(fig) | |
| elif state == "Malaysia": | |
| fig, ax1 = plt.subplots() | |
| plt.figure(figsize=(20, 12)) | |
| ax1.plot(vacancy_rate.index, vacancy_rate["Total"], label="Vacancy Rate") | |
| ax1.plot(VETS_Total, label="Forecast of Vacancy Rate") | |
| ax1.legend(loc="upper left") | |
| ax1.set_xlabel("Date") | |
| ax1.set_ylabel("Vacancy Rate") | |
| ax2 = ax1.twinx() | |
| ax2.plot(UARIMA_Malaysia_pred, "b", label="Forecast of Unemployment Rate") | |
| ax2.plot( | |
| unemployment_rate.index, | |
| unemployment_rate["Total"], | |
| "r", | |
| label="Unemployment Rate", | |
| ) | |
| ax2.legend(loc="upper left", bbox_to_anchor=(0, 0.95)) | |
| ax2.set_ylabel("Unemployment Rate") | |
| plt.rcParams["figure.figsize"] = (20, 12) | |
| st.pyplot(fig) | |
| ############################# | |
| # visualization part for the selected state | |
| if state_input: | |
| with st.sidebar: | |
| with st.expander("Vacancy & Unemployment Rate (%) Projection", expanded=False): | |
| forecast_plot(state_input) | |
| with st.expander("State Labour Supply ('000)", expanded=False): | |
| supply_state = dosm_supply.parse("state") | |
| supply_state = supply_state[supply_state["state"] == state_input] | |
| st.dataframe(supply_state, hide_index=True) | |
| with st.expander("By Ethnic Group ('000)", expanded=False): | |
| ethnic_state = dosm_supply.parse("ethnic") | |
| st.dataframe(ethnic_state, hide_index=True) | |
| with st.expander("By Age Group ('000)", expanded=False): | |
| age_state = dosm_supply.parse("age") | |
| st.dataframe(age_state, hide_index=True) | |
| with st.expander("By Education Level ('000)", expanded=False): | |
| edu_state = dosm_supply.parse("edu") | |
| st.dataframe(edu_state, hide_index=True) | |
| with st.expander("Vacancy Ads Count", expanded=False): | |
| vacancy_ads_state = dosm_demand.parse("vacancy_ads_state") | |
| vacancy_ads_state = vacancy_ads_state[ | |
| vacancy_ads_state["state"] == state_input | |
| ] | |
| st.dataframe(vacancy_ads_state, hide_index=True) | |
| with st.expander("Sector Performance", expanded=False): | |
| sector_wage_prob = dosm_demand.parse("sector_wage_prob") | |
| st.dataframe(sector_wage_prob, hide_index=True) | |
| # submit button is clicked | |
| if submit: | |
| # clear the map | |
| placeholder.empty() | |
| with st.spinner("Hang on for a second..."): | |
| with col1: | |
| ## map generation | |
| input_df = geocoder(location_input) | |
| if input_df["Latitude"][0] == None: | |
| st.error("Location not found. Please try again.") | |
| st.stop() | |
| intersected_df = intersection_check(input_df, df) | |
| m = leafmap.Map( | |
| center=[input_df["Latitude"][0], input_df["Longitude"][0]], | |
| zoom=15, | |
| google_map="HYBRID", | |
| ) | |
| style = { | |
| "stroke": True, | |
| "color": "#0000ff", | |
| "weight": 2, | |
| "opacity": 1, | |
| "fill": True, | |
| "fillColor": "#0000ff", | |
| "fillOpacity": 0.1, | |
| } | |
| style_extras = { | |
| "stroke": True, | |
| "color": "#d82c02", | |
| "weight": 2, | |
| "opacity": 1, | |
| "fill": True, | |
| "fillColor": "#d82c02", | |
| "fillOpacity": 0.1, | |
| } | |
| m.add_points_from_xy( | |
| input_df, | |
| x="Longitude", | |
| y="Latitude", | |
| icon_names=["gear", "map", "leaf", "globe"], | |
| ) | |
| m.add_gdf(intersected_df, layer_name="Region of Interest", style=style) | |
| m.add_gdf(df, layer_name="Region of Interest", style=style) | |
| m.add_gdf(airports, layer_name="Airports", style=style_extras) | |
| m.add_gdf(edu_fac, layer_name="Education Facilities", style=style_extras) | |
| m.add_gdf(financial, layer_name="Financial Services", style=style_extras) | |
| m.add_gdf(health, layer_name="Health Facilities", style=style_extras) | |
| m.add_gdf( | |
| point_of_interest, layer_name="Point of Interest", style=style_extras | |
| ) | |
| m.add_gdf(seaports, layer_name="Sea Ports", style=style_extras) | |
| m.to_streamlit(height=700) | |
| st.sidebar.expander("ℹ️ About", expanded=True) | |
| with col2: | |
| # configure the API | |
| if web_toggle: | |
| configure_api(api_key=api_input) | |
| else: | |
| configure_api(api_key=PALM_TOKEN) | |
| # get the skill list | |
| skill_list = skill_suggest_model(location_input) | |
| # get the job list | |
| job_list = job_suggest_model(skill_list) | |
| # cleaning | |
| skill_list = list_cleaning(skill_list) | |
| job_list = list_cleaning(job_list) | |
| st.subheader("🧪 Skill List:") | |
| st.write( | |
| "Based on the location of interest, we suggest the following skills:" | |
| + " " | |
| + skill_list | |
| ) | |
| st.divider() | |
| st.subheader("💼 Job List:") | |
| st.write( | |
| "Based on the skills, we suggest the following jobs:" + " " + job_list | |
| ) | |
| # job recommendation engine | |
| job_list = job_list.split(", ") | |
| result_df = [] | |
| for i in range(0, len(job_list)): | |
| job_match = job_matcher( | |
| jobdata, column="title", string_to_match=str(job_list[i]) | |
| ) | |
| if job_match.empty: | |
| continue | |
| else: | |
| job_key = job_match["title"][0] | |
| # result = job_recom_engine(jobdata, job_key=job_key) | |
| sig = job_recom_engine(jobdata) | |
| result = give_rec( | |
| titlename=job_key, sig=sig, jobdata=jobdata | |
| ).sort_values(by="View", ascending=False) | |
| result_df.append(result) | |
| # if the result is empty we will return empty dataframe | |
| if len(result_df) == 0: | |
| pass | |
| else: | |
| result_df = pd.concat([df for df in result_df], ignore_index=True) | |
| with st.expander("Job Recommendation", expanded=False): | |
| st.table(result_df) | |
| # resume recommendation engine | |
| qualification_dict = dict( | |
| zip( | |
| qualification_data["qualification"], qualification_data["mqf level"] | |
| ) | |
| ) | |
| matching_sectors = [] | |
| for _, row in sectors_data.iterrows(): | |
| sector = row["sector"] | |
| sector_skills = row["skills"] | |
| min_qualification = row["qualification"] | |
| # compute the similarity score between user skills and all sector skills | |
| similarity_score = compare_skills(user_skills, sector_skills) | |
| # check if the similarity score is above 0 and if the user's qualification is above the minimum qualification level | |
| if similarity_score > 0 and int(user_qualification[0:1]) >= int( | |
| min_qualification | |
| ): | |
| if not user_sector or user_sector.lower() in sector.lower(): | |
| matching_sectors.append(sector) | |
| # output the results | |
| with st.expander("Job Profile Analysis", expanded=False): | |
| if matching_sectors: | |
| # the matches sector could be more than one, so we need to loop through all of them | |
| for sector in matching_sectors: | |
| sector_row = sectors_data.loc[ | |
| sectors_data["sector"] == sector | |
| ].iloc[0] | |
| required_skills = set(sector_row["skills"].split(",")) | |
| user_input_skills = set(user_skills.lower().split(",")) | |
| matching_skills = user_input_skills.intersection( | |
| required_skills | |
| ) | |
| lacking_skills = required_skills.difference(user_input_skills) | |
| st.write(f"**Sector:** {sector.title()}") | |
| st.write( | |
| "**Matching Skills:**", ", ".join(matching_skills).title() | |
| ) | |
| st.write( | |
| "**Lacking Skills:**", ", ".join(lacking_skills)[2:].title() | |
| ) | |
| st.write( | |
| f"**Minimum Qualification:** MQF Level {sector_row['qualification']}" | |
| ) | |
| course_suggestions = course_suggestions_data.loc[ | |
| course_suggestions_data["sector"] == sector | |
| ] | |
| if not course_suggestions.empty: | |
| st.write("**Course Suggestions:**") | |
| for _, suggestion_row in course_suggestions.iterrows(): | |
| suggestion_row = ( | |
| pd.DataFrame(suggestion_row) | |
| .transpose() | |
| .reset_index(drop=True) | |
| ) | |
| text1 = str(suggestion_row["course suggestion 1"][0]) | |
| suggestion_link1 = f"<a href='{suggestion_row['link_1'][0]}'>{text1}</a>" | |
| text2 = str(suggestion_row["course suggestion 2"][0]) | |
| suggestion_link2 = f"<a href='{suggestion_row['link_2'][0]}'>{text2}</a>" | |
| text3 = str(suggestion_row["course suggestion 3"][0]) | |
| suggestion_link3 = f"<a href='{suggestion_row['link_3'][0]}'>{text3}</a>" | |
| st.markdown( | |
| "- " + suggestion_link1, unsafe_allow_html=True | |
| ) | |
| st.markdown( | |
| "- " + suggestion_link2, unsafe_allow_html=True | |
| ) | |
| st.markdown( | |
| "- " + suggestion_link3, unsafe_allow_html=True | |
| ) | |
| if len(lacking_skills) > 0: | |
| st.write("**Role & Responsibilities:**") | |
| job_description = sector_row["job description"].split(";") | |
| for desc in job_description: | |
| st.write(desc.strip()) | |
| st.divider() | |
| # if no match found, output this message | |
| else: | |
| st.write( | |
| "Sorry, no matching sectors found in our database for your skills and qualification level." | |
| ) | |
| # regional analysis | |
| airport_count = [] | |
| edu_fac_count = [] | |
| financial_count = [] | |
| health_count = [] | |
| seaports_count = [] | |
| intersected_df = intersected_df.reset_index() | |
| for index in range(len(airports)): | |
| if ( | |
| intersected_df["geometry"][0].contains(airports["geometry"][index]) | |
| == True | |
| ): | |
| airport_count.append(airports["name"][index]) | |
| for index in range(len(edu_fac)): | |
| if ( | |
| intersected_df["geometry"][0].contains(edu_fac["geometry"][index]) | |
| == True | |
| ): | |
| edu_fac_count.append(edu_fac["name"][index]) | |
| for index in range(len(financial)): | |
| if ( | |
| intersected_df["geometry"][0].contains(financial["geometry"][index]) | |
| == True | |
| ): | |
| financial_count.append(financial["name"][index]) | |
| for index in range(len(health)): | |
| if ( | |
| intersected_df["geometry"][0].contains(health["geometry"][index]) | |
| == True | |
| ): | |
| health_count.append(health["name"][index]) | |
| for index in range(len(seaports)): | |
| if ( | |
| intersected_df["geometry"][0].contains(seaports["geometry"][index]) | |
| == True | |
| ): | |
| seaports_count.append(seaports["name"][index]) | |
| poi_name = [] | |
| poi_tourism = [] | |
| poi_amenity = [] | |
| poi_shop = [] | |
| for index in range(len(point_of_interest)): | |
| if ( | |
| intersected_df["geometry"][0].contains( | |
| point_of_interest["geometry"][index] | |
| ) | |
| == True | |
| ): | |
| poi_name.append(point_of_interest["name"][index]) | |
| poi_tourism.append(point_of_interest["tourism"][index]) | |
| poi_amenity.append(point_of_interest["amenity"][index]) | |
| poi_shop.append(point_of_interest["shop"][index]) | |
| # make a | |
| poi = pd.DataFrame( | |
| { | |
| "name": poi_name, | |
| "Tourism": poi_tourism, | |
| "Amenity": poi_amenity, | |
| "Shop": poi_shop, | |
| } | |
| ) | |
| # find the location of interest | |
| no_hotel = len( | |
| poi[(poi["Tourism"] == "hotel") | (poi["Tourism"] == "hostel")] | |
| ) | |
| no_theme_park = len(poi[(poi["Tourism"] == "theme_park")]) | |
| no_food_place = len( | |
| poi[ | |
| (poi["Amenity"] == "restaurant") | |
| | (poi["Amenity"] == "cafe") | |
| | (poi["Amenity"] == "fast_food") | |
| ] | |
| ) | |
| no_fuel = len(poi[(poi["Amenity"] == "fuel")]) | |
| no_marketplace = len(poi[poi["Shop"] == "marketplace"]) | |
| no_supermarket = len( | |
| poi[(poi["Shop"] == "supermarket") | (poi["Shop"] == "convenience")] | |
| ) | |
| no_cloth_jewelry = len( | |
| poi[(poi["Shop"] == "clothes") | (poi["Shop"] == "jewelry")] | |
| ) | |
| no_mall = len( | |
| poi[(poi["Shop"] == "mall") | (poi["Shop"] == "department_store")] | |
| ) | |
| no_repair = len( | |
| poi[(poi["Shop"] == "car_repair") | poi["Shop"] == "motorcycle"] | |
| ) | |
| no_hair = len(poi[poi["Shop"] == "hairdresser"]) | |
| no_telco = len( | |
| poi[ | |
| (poi["Shop"] == "telecommunication") | |
| | (poi["Shop"] == "electronics") | |
| ] | |
| ) | |
| # make a dataframe | |
| poi_df = pd.DataFrame( | |
| { | |
| "Place": [ | |
| "Airport", | |
| "Education Facilities", | |
| "Financial Facilities", | |
| "Health Facilities", | |
| "Seaports", | |
| "Hotel", | |
| "Theme Park", | |
| "Food Place", | |
| "Fuel Station", | |
| "Marketplace", | |
| "Supermarket", | |
| "Cloth & Jewelry", | |
| "Mall", | |
| "Repair Shops", | |
| "Hair Salon", | |
| "Telco", | |
| ], | |
| "Count": [ | |
| len(airport_count), | |
| len(edu_fac_count), | |
| len(financial_count), | |
| len(health_count), | |
| len(seaports), | |
| no_hotel, | |
| no_theme_park, | |
| no_food_place, | |
| no_fuel, | |
| no_marketplace, | |
| no_supermarket, | |
| no_cloth_jewelry, | |
| no_mall, | |
| no_repair, | |
| no_hair, | |
| no_telco, | |
| ], | |
| } | |
| ) | |
| with st.sidebar: | |
| st.subheader("📍 Location Analysis") | |
| with st.expander("Point of Interest", expanded=False): | |
| st.table(poi_df) | |
| # sidebar footer | |
| st.sidebar.caption( | |
| "MIT License 2023 © Isekai Truck: Ang Zhi Nuo, Connie Hui Kang Yi, Khor Kean Teng, Ling Sing Cheng, Tan Yu Jing" | |
| ) | |