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import requests
from bs4 import BeautifulSoup
from prettytable import PrettyTable
import pandas as pd
from datetime import datetime, timedelta
import os
import time
import asyncio
from telegram import Bot
import matplotlib.pyplot as plt
import seaborn as sns
bot_token = '6380703806:AAEkDw_JAveSyWoz99pfgD_D0Kjgv8BjzaQ'
channel_id = '@carpointaz'
async def send_text_to_telegram(message):
bot = Bot(token=bot_token)
await bot.send_message(chat_id=channel_id, text=message, parse_mode='Markdown')
time.sleep(15) # add delay to avoid hitting Telegram's rate limits
async def send_image_to_telegram(message, image_path):
bot = Bot(token=bot_token)
await bot.send_photo(chat_id=channel_id, photo=open(image_path, 'rb'), caption=message)
time.sleep(15) # add delay to avoid hitting Telegram's rate limits
# Excel file path
excel_file_path = 'turbo_cars.xlsx'
# Function to scrape and process the website
def job():
global url
# URL of the page to scrape
url = 'https://turbo.az/autos?q%5Bsort%5D=&q%5Bmake%5D%5B%5D=1&q%5Bmodel%5D%5B%5D=&q%5Bmodel%5D%5B%5D=99&q%5Bused%5D=&q%5Bregion%5D%5B%5D=&q%5Bprice_from%5D=&q%5Bprice_to%5D=&q%5Bcurrency%5D=azn&q%5Bloan%5D=0&q%5Bbarter%5D=0&q%5Bcategory%5D%5B%5D=&q%5Byear_from%5D=2015&q%5Byear_to%5D=2016&q%5Bcolor%5D%5B%5D=&q%5Bfuel_type%5D%5B%5D=&q%5Bfuel_type%5D%5B%5D=5&q%5Bfuel_type%5D%5B%5D=6&q%5Bgear%5D%5B%5D=&q%5Btransmission%5D%5B%5D=&q%5Bengine_volume_from%5D=&q%5Bengine_volume_to%5D=&q%5Bpower_from%5D=&q%5Bpower_to%5D=&q%5Bmileage_from%5D=&q%5Bmileage_to%5D=&q%5Bonly_shops%5D=&q%5Bprior_owners_count%5D%5B%5D=&q%5Bseats_count%5D%5B%5D=&q%5Bmarket%5D%5B%5D=&q%5Bcrashed%5D=1&q%5Bpainted%5D=1&q%5Bfor_spare_parts%5D=0' # Set your URL here
# Load last results if exist
last_results = pd.read_excel(excel_file_path) if os.path.exists(excel_file_path) else None
# Creating a PrettyTable with the specified columns
table = PrettyTable()
table.field_names = ["Avtomobil", "İl", "Mühərrik", "Yürüş", "Qiymət", "Şəhər", "Yerləşdirilib", "Saytdakı vaxtı", "Link", "Vəziyyəti"]
# List to store data for Excel export
excel_data = []
product_count = 0
# Headers for the request
headers = {
'User-Agent': 'Mozilla/5.0',
}
while url:
# Sending a GET request to fetch the HTML content
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.text, 'html.parser')
# Manually identify the part of the HTML that contains the product listings
elanlar_section = soup.find('p', class_='section-title_name', string='ELANLAR')
products_section = elanlar_section.find_next('div', class_='products')
if products_section is None:
print("Failed to find product listings.")
break
# Extracting the required details for each product within the products section
for product_div in products_section.find_all('div', class_='products-i'):
try:
avtomobil = product_div.find('div', class_='products-i__name products-i__bottom-text').get_text().strip()
il_muherrik_yurus = product_div.find('div', class_='products-i__attributes products-i__bottom-text').get_text().strip()
il, muherrik, yurus = il_muherrik_yurus.split(', ')
qiymet = product_div.find('div', class_='product-price').get_text().strip()
sheher, yerlesdirilib = product_div.find('div', class_='products-i__datetime').get_text().strip().split(' ', 1)
sheher = sheher.rstrip(',')
# Replacing specific words with formatted dates
today = datetime.today()
if 'bugün' in yerlesdirilib:
yerlesdirilib = yerlesdirilib.replace('bugün', today.strftime('%d.%m.%Y'))
elif 'dünən' in yerlesdirilib:
yesterday = today - timedelta(days=1)
yerlesdirilib = yerlesdirilib.replace('dünən', yesterday.strftime('%d.%m.%Y'))
# Extracting date from the "Yerləşdirilib" column and calculating "Saytdakı vaxtı"
yerlesdirilib_date_str = yerlesdirilib.split(' ')[0]
yerlesdirilib_date = datetime.strptime(yerlesdirilib_date_str, '%d.%m.%Y')
saytdaki_vaxti = (today - yerlesdirilib_date).days
relative_link = product_div.find('a', class_='products-i__link')['href']
link = "https://turbo.az" + relative_link
# Adding the extracted details to the table
row_data = [avtomobil, il, muherrik, yurus, qiymet, sheher, yerlesdirilib, saytdaki_vaxti, link]
# Check the "Vəziyyəti" of the product
if last_results is not None:
if link in last_results['Link'].values:
vaziyyeti = "-"
else:
vaziyyeti = "Yeni"
else:
vaziyyeti = "Yeni"
row_data.append(vaziyyeti)
table.add_row(row_data)
excel_data.append(row_data)
product_count += 1
except AttributeError:
# Handle cases where the expected elements are not found
continue
# Find the next page URL
next_link = soup.find('a', rel='next')
url = "https://turbo.az" + next_link['href'] if next_link else None
# Update "Vəziyyəti" of the products that are not in new results but in the last results
if last_results is not None:
last_links = set(last_results['Link'].values)
new_links = set([row[-2] for row in excel_data])
sold_links = last_links - new_links
for link in sold_links:
row_index = last_results[last_results['Link'] == link].index[0]
last_results.loc[row_index, 'Vəziyyəti'] = 'Yeni Satılıb'
# Append the sold products to new results
sold_products = last_results[last_results['Vəziyyəti'] == 'Yeni Satılıb']
excel_data.extend(sold_products.values.tolist())
# Printing the PrettyTable
print(table)
# Printing the product count
print(f"Total products found: {product_count}")
# Creating a DataFrame and saving to Excel (using Excel format)
columns = ["Avtomobil", "İl", "Mühərrik", "Yürüş", "Qiymət", "Şəhər", "Yerləşdirilib", "Saytdakı vaxtı", "Link", "Vəziyyəti"]
df = pd.DataFrame(excel_data, columns=columns)
df.to_excel(excel_file_path, index=False, engine='openpyxl')
def clean_excel_data():
df = pd.read_excel('turbo_cars.xlsx')
df.drop_duplicates(subset='Link', keep='first', inplace=True)
df.to_excel('turbo_cars.xlsx', index=False)
def update_veziyyeti():
df = pd.read_excel('turbo_cars.xlsx')
df.loc[df['Vəziyyəti'] == 'Yeni Satılıb', 'Vəziyyəti'] = 'Satılıb'
df.to_excel('turbo_cars.xlsx', index=False)
def read_excel_data():
clean_excel_data() # Clean the Excel data before reading
df = pd.read_excel('turbo_cars.xlsx')
return df
def create_plot(df, plot_num):
df_count = df.groupby(["Avtomobil", "Vəziyyəti"]).size().reset_index(name="count")
plt.figure(figsize=(10,5))
sns.barplot(y="Avtomobil", x="count", hue="Vəziyyəti", data=df_count)
plt.title('Avtomobil Statistics')
plt.savefig(f'plot_{plot_num}.png') # Save the plot as an image
plt.close() # Close the plot to free up memory
async def main():
# Create a set to store the links of cars that have been sent
sent_cars = set()
while True:
print("Starting a new run...")
await send_text_to_telegram("Starting a new run...")
# Run the job function inside the while loop
job()
car_data = read_excel_data()
yeni_count = 0
yeni_satilib_count = 0
total_cars = len(car_data)
for index, row in car_data.iterrows():
if row['Vəziyyəti'] in ['Yeni', 'Yeni Satılıb'] and row['Link'] not in sent_cars:
message = f"*{row['Avtomobil']}*\nİl: {row['İl']}\nMühərrik: {row['Mühərrik']}\nYürüş: {row['Yürüş']}\nQiymət: {row['Qiymət']}\nŞəhər: {row['Şəhər']}\nYerləşdirilib: {row['Yerləşdirilib']}\nSaytdakı vaxtı: {row['Saytdakı vaxtı']}\nLink: [Baxmaq üçün basın]({row['Link']})\nVəziyyəti: {row['Vəziyyəti']}\n-------------------"
await send_text_to_telegram(message)
sent_cars.add(row['Link'])
if row['Vəziyyəti'] == 'Yeni':
yeni_count += 1
elif row['Vəziyyəti'] == 'Yeni Satılıb':
yeni_satilib_count += 1
update_veziyyeti() # Update the status in the Excel file
print(f"Sent {yeni_count} new cars and {yeni_satilib_count} new sold cars to Telegram.")
await send_text_to_telegram(f"Sent {yeni_count} new cars and {yeni_satilib_count} new sold cars to Telegram.")
# Create and send the plot
create_plot(car_data, 1)
await send_image_to_telegram("Here is the plot for this run:", "plot_1.png")
time.sleep(10) # Wait for an hour before the next run
if __name__ == "__main__":
asyncio.run(main())
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