import requests
import random
import time
import pandas as pd
from bs4 import BeautifulSoup
from tqdm import tqdm
from seleniumbase import Driver
from seleniumbase import page_actions

import undetected_chromedriver as webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
import time
import pandas as pd

BASE_URL = "https://www.zoopla.co.uk/property/uprn/{uprn}/"


def initialize_driver():
    driver = Driver(headless=True, uc=True)  # Set headless to True if you want headless mode
    return driver


def app():
    # Read in the starting asset list
    asset_list = pd.read_excel("portfolio_epc_data_50m 28th May.xlsx")
    asset_list = asset_list[["UPRN", "ADDRESS", "POSTCODE"]]

    # asset_list.to_excel("property value.xlsx", index=False)

    # Generate the list of urls
    urls = [BASE_URL.format(uprn=uprn) for uprn in asset_list["UPRN"]]

    driver = webdriver.Chrome()

    driver = initialize_driver()
    driver.set_page_load_timeout(30)  # Increase page load timeout

    result = []
    for i, (url, uprn) in tqdm(enumerate(zip(urls, asset_list["UPRN"].tolist())), total=len(urls)):

        # Every 10 requests sleep for an extra 7 seconds
        if len(result) % 10 == 0 and len(result) != 0:
            time.sleep(7)

        try:

            driver.get(url)
            page_actions.wait_for_element_visible(driver, "p[data-testid='estimate-blurred']", timeout=30)

            price_element = driver.find_element("css selector", "p[data-testid='estimate-blurred']")
            price = price_element.get_text(strip=True)

            low_price_element = driver.find_element("css selector", "span[data-testid='low-estimate-blurred']")
            low_price = low_price_element.get_text(strip=True)

            high_price_element = driver.find_element("css selector", "span[data-testid='high-estimate-blurred']")
            high_price = high_price_element.get_text(strip=True)

            result.append(
                {
                    "UPRN": uprn,
                    "price": price,
                    "lower_estimate": low_price,
                    "upper_estimate": high_price
                }
            )

            # Sleep a random amount of time between 5 and 20 seconds
            sleep_time = 5 + (15 * random.random())
            time.sleep(sleep_time)

        except Exception as e:
            print(f"Failed to retrieve data for UPRN {uprn} at iteration {i}: {e}")

    # Store the result depending on where we are
    savepoint = pd.DataFrame(result)
    savepoint.to_csv(f"savepoint_index_{i}.csv", index=False)

    # TODO: Testing Jina AI - didn't work but maybe one of the alternatives might work:
    #       https://www.youtube.com/watch?v=QxHE4af5BQE
    response = requests.get("https://r.jina.ai/https://www.zoopla.co.uk/property/uprn/41222761/")
    response.text


if __name__ == "__main__":
    app()