import streamlit as st import requests import numpy as np from PIL import Image import warnings warnings.filterwarnings("ignore") import requests import pandas as pd import numpy as np from bs4 import BeautifulSoup import bs4 from urllib.request import urlopen import time import re import time import matplotlib.pyplot as plt import seaborn as sns import matplotlib as mpl import plotly import plotly.express as px import plotly.graph_objs as go import plotly.offline as py from plotly.offline import iplot from plotly.subplots import make_subplots import plotly.figure_factory as ff from selenium.webdriver.common.desired_capabilities import DesiredCapabilities from selenium import webdriver from selenium.webdriver.common.by import By from selenium.webdriver.common.keys import Keys from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.webdriver.chrome.service import Service import requests import platform import zipfile import os import subprocess import streamlit as st import numpy as np import pandas as pd import time from selenium import webdriver from selenium.webdriver.chrome.service import Service from selenium.webdriver.chrome.options import Options from webdriver_manager.chrome import ChromeDriverManager from wuzzuf_scraper import Wuzzuf_scrapping from linkedin_scraper import LINKEDIN_Scrapping from data_analysis import map_bubble, linkedin_exp, wuzzuf_exp # Set up Streamlit page configuration st.set_page_config(page_title="My Web_Scrap Page", page_icon=":tada:", layout="wide") # ---- HEADER SECTION ---- with st.container(): left_column, right_column = st.columns(2) with left_column: st.subheader("Hi! I am Yassmen :wave:") st.title("An Electronics and Communication Engineer") st.write("In this app we will scrap jobs from LinkedIn and Wuzzuf websites, let's get it started :boom:") st.write("[Reach me >](https://www.linkedin.com/in/yassmen-youssef-48439a166/)") with right_column: st.image("im.gif", use_column_width=True) # Sidebar selections webs = ["Wuzzuf", "Linkedin"] jobs = ["Machine Learning", "AI Engineer", "Data Analysis", "Software Testing"] nums = np.arange(1, 1000) site = st.sidebar.selectbox("Select one website", webs) job = st.sidebar.selectbox("Select one job", jobs) num_jobs = st.sidebar.selectbox("Select number of jobs you want to scrap", nums) # Function to get Selenium driver from selenium import webdriver from selenium.webdriver.firefox.service import Service as FirefoxService from webdriver_manager.firefox import GeckoDriverManager @st.cache_resource def get_driver(): options = webdriver.ChromeOptions() options.add_argument("--headless") # Run in headless mode options.add_argument("--no-sandbox") options.add_argument("--disable-dev-shm-usage") try: driver = webdriver.Chrome(options=options) return driver except Exception as e: st.error(f"Error initializing WebDriver: {e}") return None import streamlit as st from streamlit_option_menu import option_menu import streamlit.components.v1 as components n2 = pd.DataFrame() if st.sidebar.button('Start Scrapping'): if site =="Wuzzuf": with st.container(): st.write("---") tab1, tab2 ,tab3= st.tabs([" Data", " Bubble Map","Data Exploration"]) with tab1 : with st.spinner('✨Now loading...' ): time.sleep(5) driver = get_driver() # Initialize the driver n1 = Wuzzuf_scrapping(job, num_jobs, driver) # Pass driver to the scraping function driver.quit() # Clean up the driver try: tab1.dataframe(n1) except: try: tab1.write(n1.astype(str).set_index(n1.index.astype(str))) # Success except: tab1.table(n1) with tab2: map_bubble(n1) with tab3: #tab3.plotly_chart(wuzzuf_exp(n1)) wuzzuf_exp(n1) elif site =="Linkedin": with st.container(): st.write("---") tab1, tab2 ,tab3= st.tabs([" Data", " Bubble Map","Data Exploration"]) with tab1 : with st.spinner('✨Now loading...' ): time.sleep(5) driver = get_driver() n1 = LINKEDIN_Scrapping(job ,num_jobs,driver ) driver.quit() # Clean up the driver try: tab1.dataframe(n1) except: try: tab1.write(n1.astype(str).set_index(n1.index.astype(str))) # Success except: tab1.table(n1) with tab2: map_bubble(n1) with tab3: linkedin_exp(n1)