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

# Check Chrome version
chrome_version = subprocess.getoutput("google-chrome --version")
chrome_version_number = chrome_version.split()[-1]  # Extract only the version number

# Construct the download URL
url = f"https://chromedriver.storage.googleapis.com/{chrome_version_number}/chromedriver_{platform.system().lower()}64.zip"

# Download ChromeDriver
response = requests.get(url)

# Check if the download was successful
if response.status_code == 200:
    # Save the zip file
    with open("chromedriver.zip", "wb") as file:
        file.write(response.content)

    # Unzip the downloaded file
    with zipfile.ZipFile("chromedriver.zip", 'r') as zip_ref:
        zip_ref.extractall("drivers")  # Extract to a folder named 'drivers'

    # Clean up the zip file
    os.remove("chromedriver.zip")
else:
    raise Exception(f"Failed to download ChromeDriver: {response.status_code} - {response.text}")
#Settings for using the driver without a UI
# Set up Chrome options
options = webdriver.ChromeOptions()
options.add_argument('--headless')  # Run in headless mode
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
# Initialize the ChromeDriver
service = Service('drivers/chromedriver') 
driver = webdriver.Chrome(service=service, options=options)

from wuzzuf_scraper import Wuzzuf_scrapping
from linkedin_scraper import LINKEDIN_Scrapping
from data_analysis import map_bubble,linkedin_exp,wuzzuf_exp

####################### stream lit app ################################


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 Communcation 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("images.jfif", use_column_width=True) 
       # st_lottie(lottie_coding, height=300, key="coding")



import streamlit as st
from streamlit_option_menu import option_menu

#with st.sidebar:
   # selected = option_menu("Main Menu", ["select website", 'search job','numbers of jobs'], icons=['linkedin', 'search','123'], menu_icon="cast", default_index=1)
    
webs =["Wuzzuf","Linkedin"]
jobs =["Machine Learning","AI Engineer","Data Analysis","Software Testing"]
nums = np.arange(1,1000)

#with st.sidebar:
  #if selected == "select website":
site = st.sidebar.selectbox("select one website", webs)
  #elif selected == "search job":
job = st.sidebar.selectbox("select one job", jobs)
  #elif selected == "numbers of jobs":
num_jobs = st.sidebar.selectbox("select num of jobs you want to scrap", nums)



import streamlit.components.v1 as components

import hydralit_components as hc
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)
            n1 = Wuzzuf_scrapping(job ,num_jobs )
            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)


  if 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)
            n1 = LINKEDIN_Scrapping(job ,num_jobs )
            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)  # WILL CHANGE