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app.py
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import os
import re
import time
from datetime import datetime
import gspread
import pandas as pd
import pytz
import requests
from bs4 import BeautifulSoup
from gspread_dataframe import set_with_dataframe
# We create a dictionary of the markets we want to track.
markets = {
"San Antonio": {
"url": "https://www.expressnews.com",
"timezone": "US/Central",
"spreadsheet": "https://docs.google.com/spreadsheets/d/1F073i7iMDEU0q2B8K3nG881YDr-f1bCXzc2h24V_dOs/edit?usp=sharing",
},
"Houston": {
"url": "https://www.houstonchronicle.com",
"timezone": "US/Central",
"spreadsheet": "https://docs.google.com/spreadsheets/d/19IZkVDucvXYT2EyHQ-8Yu2MSrknYK3Co_HI3TAd_hBA/edit#gid=0",
},
"San Francisco": {
"url": "https://www.sfchronicle.com",
"timezone": "US/Pacific",
"spreadsheet": "https://docs.google.com/spreadsheets/d/1YhvmHOeT5RQLmoef6zZhWfwLD87JIAFiGqzS_llbTu8/edit#gid=0",
},
"Albany": {
"url": "https://www.timesunion.com",
"timezone": "US/Eastern",
"spreadsheet": "https://docs.google.com/spreadsheets/d/1NREkjXsMslgsl_8XaS-9W_3t3gU67jfmptoN9-9yt1k/edit#gid=1676782739",
},
"Connecticut Insider": {
"url": "https://www.ctinsider.com",
"timezone": "US/Eastern",
"spreadsheet": "https://docs.google.com/spreadsheets/d/10V626AzMp1NXaW4wOnUq_VArl79XpCdbJQkbIk9esGA/edit#gid=964675505",
},
"Connecticut Post": {
"url": "https://www.ctpost.com",
"timezone": "US/Eastern",
"spreadsheet": "https://docs.google.com/spreadsheets/d/1wMvD70EZO27TyzFY80cOxZPdFTCSqnB4YMarwuf-1vI/edit#gid=0",
},
}
# We grab our service account from a Github secret
SERVICE_ACCOUNT = os.environ.get("SERVICE_ACCOUNT")
ACCESS_TOKEN = os.environ.get("ACCESS_TOKEN")
# Create a temporary json file based on the SERVICE_ACCOUNT env variable
with open("service_account.json", "w") as f:
f.write(SERVICE_ACCOUNT)
# We authenticate with Google using the service account json we created earlier.
gc = gspread.service_account(filename="service_account.json")
def remove_duplicate_prefix(url, prefix):
if url.startswith(prefix * 2):
url = url.replace(prefix, "", 1)
return url
# This handy dandy function will retry the api call if it fails.
def api_call_handler(func):
# Number of retries
for i in range(0, 6):
try:
return func()
except Exception as e:
print(f"🤦♂️ {e}")
print(f"🤷♂️ Retrying in {2 ** i} seconds...")
time.sleep(2**i)
print("🤬 Giving up...")
raise SystemError
def getSoup(url):
"""
This function takes a URL and returns a BeautifulSoup object.
"""
headers = {
"x-px-access-token": ACCESS_TOKEN,
}
page = requests.get(url, headers=headers)
soup = BeautifulSoup(page.content, "html.parser")
return soup
def get_san_antonio_headlines():
"""
This function scrapes the San Antonio Express-News homepage
"""
print("📰 Scraping San Antonio Express-News...")
market_url = "https://www.expressnews.com"
wcm_url = "https://wcm.hearstnp.com/index.php?_wcmAction=business/collection&id="
# Get the HTML content of the homepage
soup = getSoup(market_url)
## --- CENTERPIECE HEADLINES --- ##
# Find all the divs with a class of "centerpiece-tab--main-headline"
cp_headlines = soup.find_all("div", class_="centerpiece-tab--main-headline")
# We extract the text from the headlines and strip the whitespace.
cp_headline = cp_headlines[0].text.strip()
tab2_headline = cp_headlines[1].text.strip()
tab3_headline = cp_headlines[2].text.strip()
tab4_headline = cp_headlines[3].text.strip()
tab5_headline = cp_headlines[4].text.strip()
tab6_headline = cp_headlines[5].text.strip()
# We extract the URLs from the headlines and strip the whitespace. The href is in the a tag, so we need to use a for loop to extract the URLs.
cp_url = remove_duplicate_prefix(
market_url + cp_headlines[0].find("a")["href"], market_url
)
tab2_url = remove_duplicate_prefix(
market_url + cp_headlines[1].find("a")["href"], market_url
)
tab3_url = remove_duplicate_prefix(
market_url + cp_headlines[2].find("a")["href"], market_url
)
tab4_url = remove_duplicate_prefix(
market_url + cp_headlines[3].find("a")["href"], market_url
)
tab5_url = remove_duplicate_prefix(
market_url + cp_headlines[4].find("a")["href"], market_url
)
tab6_url = remove_duplicate_prefix(
market_url + cp_headlines[5].find("a")["href"], market_url
)
# Find all the divs on the page that have a class that contains the strings "hdnce-collection-" AND "-dynamic_centerpiece_tab"
# Example: class="hide-rss-link hdnce-e hdnce-collection-105803-dynamic_centerpiece_tab"
cp_tabs = soup.find_all(
"div", class_=re.compile("hdnce-collection-.*-dynamic_centerpiece_tab")
)
cp_tab_id_list = []
for tab in cp_tabs:
# Get the string of digits in the collection
# Example input: hdnce-collection-111490-dynamic_centerpiece_tab
# Example output: 111490
tab_id = re.search(r"\d+", tab["class"][2]).group()
cp_tab_id_list.append(f"{wcm_url}{tab_id}")
(
cp_order,
tab2_order,
tab3_order,
tab4_order,
tab5_order,
tab6_order,
) = cp_tab_id_list
## --- BREAKING NEWS --- ##
# Find all the headlines in the breaking news bar.
breaking_headlines = soup.find_all("a", class_="breakingNow--item-headline")
# We extract the text from the breaking news headlines and strip the whitespace. There isn't always a breaking news bar, so we use a try/except block to handle the error.
try:
breaking1_headline = breaking_headlines[0].text.strip()
breaking1_url = remove_duplicate_prefix(
market_url + breaking_headlines[0]["href"], market_url
)
except:
breaking1_headline = None
breaking1_url = None
try:
breaking2_headline = breaking_headlines[1].text.strip()
breaking2_url = remove_duplicate_prefix(
market_url + breaking_headlines[1]["href"], market_url
)
except:
breaking2_headline = None
breaking2_url = None
try:
just_in_headline = soup.find(
"a", {"class": "justNow--item-headline"}
).text.strip()
just_in_url = (
f'{market_url}{soup.find("a", {"class": "justNow--item-headline"})["href"]}'
)
except:
just_in_headline = None
just_in_url = None
# Find all the divs on the page that have a class that contains the strings "hdnce-collection-" AND "-dynamic_breaking_now_tab"
# Example: class="hdnce-collection-114722-dynamic_breaking_now_tab"
breaking_tabs = soup.find_all(
"div", class_=re.compile("hdnce-collection-.*-dynamic_breaking_now_tab")
)
breaking_tab_id_list = []
for tab in breaking_tabs:
# Get the string of digits in the collection
# Example input: hdnce-collection-111490-dynamic_centerpiece_tab
# Example output: 111490
tab_id = re.search(r"\d+", tab["class"][2]).group()
breaking_tab_id_list.append(f"{wcm_url}{tab_id}")
try:
breaking1_order = breaking_tab_id_list[0]
except:
breaking1_order = ""
try:
breaking2_order = breaking_tab_id_list[1]
except:
breaking2_order = ""
# Find the div on the page that has a class that contains the strings "hdnce-collection-" AND "-dynamic_breaking_now" that resides inside of a div with an id of "zoneAL"
# Example: class="hdnce-collection-107158-dynamic_breaking_now"
try:
just_in_order = soup.find("div", id="zoneAL").find(
"div", class_=re.compile("hdnce-collection-.*-dynamic_breaking_now")
)
just_in_id = re.search(r"\d+", just_in_order["class"][2]).group()
just_in_order = f"{wcm_url}{just_in_id}"
except:
just_in_order = ""
## --- TOP HEADLINES --- ##
# Find the top headlines list and extract the text from the headlines, stripping the whitespace.
top_headlines_list = soup.find("ul", class_="coreHeadlineList--items")
headline_list = top_headlines_list.find_all(
"div", class_="coreHeadlineList--item-headline"
)
top1 = headline_list[0].text.strip()
top2 = headline_list[1].text.strip()
top3 = headline_list[2].text.strip()
top4 = headline_list[3].text.strip()
top5 = headline_list[4].text.strip()
top1_url = remove_duplicate_prefix(
market_url + headline_list[0].find("a")["href"], market_url
)
top2_url = remove_duplicate_prefix(
market_url + headline_list[1].find("a")["href"], market_url
)
top3_url = remove_duplicate_prefix(
market_url + headline_list[2].find("a")["href"], market_url
)
top4_url = remove_duplicate_prefix(
market_url + headline_list[3].find("a")["href"], market_url
)
top5_url = remove_duplicate_prefix(
market_url + headline_list[4].find("a")["href"], market_url
)
# Find the div on the page that has a class that contains the strings hdnce-collection" AND "-dynamic_headline_list"
# Example: class="hdnce-collection-105799-dynamic_headline_list"
top_headline_order = soup.find(
"div", class_=re.compile("hdnce-collection-.*-dynamic_headline_list")
)
top_headline_id = re.search(r"\d+", top_headline_order["class"][2]).group()
top_headline_order = f"{wcm_url}{top_headline_id}"
# Create a new variable called date and store the current date in the following format: YYYY-MM-DD
date = datetime.now(pytz.timezone(markets["San Antonio"]["timezone"])).strftime(
"%Y-%m-%d"
)
# Create a new variable called time and store the current time in the appropriate timezone in a 12-hour format without a leading zero
time = datetime.now(pytz.timezone(markets["San Antonio"]["timezone"])).strftime(
"%-I:%M %p"
)
# Print out all the headlines in a formatted string.
# print(
# f"""
# 📰 Breaking 1: {breaking1_headline}
# 📰 Breakinhg 2: {breaking2_headline}
# 📰 Just in: {just_in_headline}
# 📰 CP: {cp_headline}
# 📰 Tab 2: {tab2_headline}
# 📰 Tab 3: {tab3_headline}
# 📰 Tab 4: {tab4_headline}
# 📰 Tab 5: {tab5_headline}
# 📰 Tab 6: {tab6_headline}
# 📰 Headline 1: {top1}
# 📰 Headline 2: {top2}
# 📰 Headline 3: {top3}
# 📰 Headline 4: {top4}
# 📰 Headline 5: {top5}"""
# )
# # Print the tab order
# print(
# f"""
# 🎌 Breaking 1 Order: {breaking1_order}
# 🎌 Breaking 2 Order: {breaking2_order}
# 🎌 Just In Order: {just_in_order}
# 🎌 CP Order: {cp_order}
# 🎌 Tab 2 Order: {tab2_order}
# 🎌 Tab 3 Order: {tab3_order}
# 🎌 Tab 4 Order: {tab4_order}
# 🎌 Tab 5 Order: {tab5_order}
# 🎌 Tab 6 Order: {tab6_order}
# 🎌 Top Headlines Order: {top_headline_order}
# """
# )
# # Print out all the headlines
# print(
# f"""
# 🔗 Breaking 1 URL: {breaking1_url}
# 🔗 Breaking 2 URL: {breaking2_url}
# 🔗 Just In URL: {just_in_url}
# 🔗 CP URL: {cp_url}
# 🔗 Tab 2 URL: {tab2_url}
# 🔗 Tab 3 URL: {tab3_url}
# 🔗 Tab 4 URL: {tab4_url}
# 🔗 Tab 5 URL: {tab5_url}
# 🔗 Tab 6 URL: {tab6_url}
# 🔗 Top 1 URL: {top1_url}
# 🔗 Top 2 URL: {top2_url}
# 🔗 Top 3 URL: {top3_url}
# 🔗 Top 4 URL: {top4_url}
# 🔗 Top 5 URL: {top5_url}
# """
# )
# Create a dataframe out of the centerpiece headlines and the top headlines.
latest_headlines_df = pd.DataFrame(
{
"Date": date,
"Time": time,
"Breaking 1": breaking1_headline,
"Breaking 2": breaking2_headline,
"Just In": just_in_headline,
"CP": cp_headline,
"Tab 2": tab2_headline,
"Tab 3": tab3_headline,
"Tab 4": tab4_headline,
"Tab 5": tab5_headline,
"Tab 6": tab6_headline,
"Top 1": top1,
"Top 2": top2,
"Top 3": top3,
"Top 4": top4,
"Top 5": top5,
},
index=[0],
)
latest_urls_df = pd.DataFrame(
{
"Date": date,
"Time": time,
"Breaking 1": breaking1_url,
"Breaking 2": breaking2_url,
"Just In": just_in_url,
"CP": cp_url,
"Tab 2": tab2_url,
"Tab 3": tab3_url,
"Tab 4": tab4_url,
"Tab 5": tab5_url,
"Tab 6": tab6_url,
"Top 1": top1_url,
"Top 2": top2_url,
"Top 3": top3_url,
"Top 4": top4_url,
"Top 5": top5_url,
},
index=[0],
)
latest_tab_order_df = pd.DataFrame(
{
"Date": date,
"Time": time,
"Breaking 1": breaking1_order,
"Breaking 2": breaking2_order,
"Just In": just_in_order,
"CP": cp_order,
"Tab 2": tab2_order,
"Tab 3": tab3_order,
"Tab 4": tab4_order,
"Tab 5": tab5_order,
"Tab 6": tab6_order,
"Top 1": top_headline_order,
"Top 2": top_headline_order,
"Top 3": top_headline_order,
"Top 4": top_headline_order,
"Top 5": top_headline_order,
},
index=[0],
)
return latest_headlines_df, latest_urls_df, latest_tab_order_df
def get_houston_headlines():
"""
This function scrapes the Houston Chronicle homepage
"""
# Get the HTML content of the Houston Chronicle homepage
market_url = "https://www.houstonchronicle.com"
wcm_url = "https://wcm.hearstnp.com/index.php?_wcmAction=business/collection&id="
soup = getSoup(market_url)
## --- CENTERPIECE HEADLINES --- ##
# Find all the divs with a class of "centerpiece-tab--main-headline"
cp_headlines = soup.find_all("div", class_="centerpiece-tab--main-headline")
# We extract the text from the headlines and strip the whitespace.
cp_headline = cp_headlines[0].text.strip()
tab2_headline = cp_headlines[1].text.strip()
tab3_headline = cp_headlines[2].text.strip()
tab4_headline = cp_headlines[3].text.strip()
tab5_headline = cp_headlines[4].text.strip()
tab6_headline = cp_headlines[5].text.strip()
# We extract the URLs from the headlines and strip the whitespace. The href is in the a tag, so we need to use a for loop to extract the URLs.
cp_url = remove_duplicate_prefix(
market_url + cp_headlines[0].find("a")["href"], market_url
)
tab2_url = remove_duplicate_prefix(
market_url + cp_headlines[1].find("a")["href"], market_url
)
tab3_url = remove_duplicate_prefix(
market_url + cp_headlines[2].find("a")["href"], market_url
)
tab4_url = remove_duplicate_prefix(
market_url + cp_headlines[3].find("a")["href"], market_url
)
tab5_url = remove_duplicate_prefix(
market_url + cp_headlines[4].find("a")["href"], market_url
)
tab6_url = remove_duplicate_prefix(
market_url + cp_headlines[5].find("a")["href"], market_url
)
# Find all the divs on the page that have a class that contains the strings "hdnce-collection-" AND "-dynamic_centerpiece_tab"
# Example: class="hide-rss-link hdnce-e hdnce-collection-105803-dynamic_centerpiece_tab"
cp_tabs = soup.find_all(
"div", class_=re.compile("hdnce-collection-.*-dynamic_centerpiece_tab")
)
cp_tab_id_list = []
for tab in cp_tabs:
# Get the string of digits in the collection
# Example input: hdnce-collection-111490-dynamic_centerpiece_tab
# Example output: 111490
tab_id = re.search(r"\d+", tab["class"][2]).group()
cp_tab_id_list.append(f"{wcm_url}{tab_id}")
(
cp_order,
tab2_order,
tab3_order,
tab4_order,
tab5_order,
tab6_order,
) = cp_tab_id_list
## --- BREAKING NEWS --- ##
# Find all the headlines in the breaking news bar.
breaking_headlines = soup.find_all("a", class_="breakingNow--item-headline")
# We extract the text from the breaking news headlines and strip the whitespace. There isn't always a breaking news bar, so we use a try/except block to handle the error.
try:
breaking1_headline = breaking_headlines[0].text.strip()
breaking1_url = remove_duplicate_prefix(
market_url + breaking_headlines[0]["href"], market_url
)
except:
breaking1_headline = None
breaking1_url = None
try:
breaking2_headline = breaking_headlines[1].text.strip()
breaking2_url = remove_duplicate_prefix(
market_url + breaking_headlines[1]["href"], market_url
)
except:
breaking2_headline = None
breaking2_url = None
try:
just_in_headline = soup.find(
"a", {"class": "justNow--item-headline"}
).text.strip()
just_in_url = (
f'{market_url}{soup.find("a", {"class": "justNow--item-headline"})["href"]}'
)
except:
just_in_headline = None
just_in_url = None
# Find all the divs on the page that have a class that contains the strings "hdnce-collection-" AND "-dynamic_breaking_now_tab"
# Example: class="hdnce-collection-114722-dynamic_breaking_now_tab"
breaking_tabs = soup.find_all(
"div", class_=re.compile("hdnce-collection-.*-dynamic_breaking_now_tab")
)
breaking_tab_id_list = []
for tab in breaking_tabs:
# Get the string of digits in the collection
# Example input: hdnce-collection-111490-dynamic_centerpiece_tab
# Example output: 111490
tab_id = re.search(r"\d+", tab["class"][2]).group()
breaking_tab_id_list.append(f"{wcm_url}{tab_id}")
try:
breaking1_order = breaking_tab_id_list[0]
except:
breaking1_order = ""
try:
breaking2_order = breaking_tab_id_list[1]
except:
breaking2_order = ""
# Find the div on the page that has a class that contains the strings "hdnce-collection-" AND "-dynamic_breaking_now" that resides inside of a div with an id of "zoneAL"
# Example: class="hdnce-collection-107158-dynamic_breaking_now"
try:
just_in_order = soup.find("div", id="zoneAL").find(
"div", class_=re.compile("hdnce-collection-.*-dynamic_breaking_now")
)
just_in_id = re.search(r"\d+", just_in_order["class"][2]).group()
just_in_order = f"{wcm_url}{just_in_id}"
except:
just_in_order = ""
## --- TOP HEADLINES --- ##
# Find the top headlines list and extract the text from the headlines, stripping the whitespace.
top_headlines_list = soup.find("ul", class_="coreHeadlineList--items")
headline_list = top_headlines_list.find_all(
"div", class_="coreHeadlineList--item-headline"
)
top1 = headline_list[0].text.strip()
top2 = headline_list[1].text.strip()
top3 = headline_list[2].text.strip()
top4 = headline_list[3].text.strip()
top5 = headline_list[4].text.strip()
top1_url = remove_duplicate_prefix(
market_url + headline_list[0].find("a")["href"], market_url
)
top2_url = remove_duplicate_prefix(
market_url + headline_list[1].find("a")["href"], market_url
)
top3_url = remove_duplicate_prefix(
market_url + headline_list[2].find("a")["href"], market_url
)
top4_url = remove_duplicate_prefix(
market_url + headline_list[3].find("a")["href"], market_url
)
top5_url = remove_duplicate_prefix(
market_url + headline_list[4].find("a")["href"], market_url
)
# Find the div on the page that has a class that contains the strings hdnce-collection" AND "-dynamic_headline_list"
# Example: class="hdnce-collection-105799-dynamic_headline_list"
top_headline_order = soup.find(
"div", class_=re.compile("hdnce-collection-.*-dynamic_headline_list")
)
top_headline_id = re.search(r"\d+", top_headline_order["class"][2]).group()
top_headline_order = f"{wcm_url}{top_headline_id}"
# Create a new variable called date and store the current date in the following format: YYYY-MM-DD
date = datetime.now(pytz.timezone(markets["Houston"]["timezone"])).strftime(
"%Y-%m-%d"
)
# Create a new variable called time and store the current time in the appropriate timezone in a 12-hour format without a leading zero
time = datetime.now(pytz.timezone(markets["Houston"]["timezone"])).strftime(
"%-I:%M %p"
)
# Create a dataframe out of the centerpiece headlines and the top headlines.
latest_headlines_df = pd.DataFrame(
{
"Date": date,
"Time": time,
"Breaking 1": breaking1_headline,
"Breaking 2": breaking2_headline,
"Just In": just_in_headline,
"CP": cp_headline,
"Tab 2": tab2_headline,
"Tab 3": tab3_headline,
"Tab 4": tab4_headline,
"Tab 5": tab5_headline,
"Tab 6": tab6_headline,
"Top 1": top1,
"Top 2": top2,
"Top 3": top3,
"Top 4": top4,
"Top 5": top5,
},
index=[0],
)
latest_urls_df = pd.DataFrame(
{
"Date": date,
"Time": time,
"Breaking 1": breaking1_url,
"Breaking 2": breaking2_url,
"Just In": just_in_url,
"CP": cp_url,
"Tab 2": tab2_url,
"Tab 3": tab3_url,
"Tab 4": tab4_url,
"Tab 5": tab5_url,
"Tab 6": tab6_url,
"Top 1": top1_url,
"Top 2": top2_url,
"Top 3": top3_url,
"Top 4": top4_url,
"Top 5": top5_url,
},
index=[0],
)
latest_tab_order_df = pd.DataFrame(
{
"Date": date,
"Time": time,
"Breaking 1": breaking1_order,
"Breaking 2": breaking2_order,
"Just In": just_in_order,
"CP": cp_order,
"Tab 2": tab2_order,
"Tab 3": tab3_order,
"Tab 4": tab4_order,
"Tab 5": tab5_order,
"Tab 6": tab6_order,
"Top 1": top_headline_order,
"Top 2": top_headline_order,
"Top 3": top_headline_order,
"Top 4": top_headline_order,
"Top 5": top_headline_order,
},
index=[0],
)
return latest_headlines_df, latest_urls_df, latest_tab_order_df
def get_albany_headlines():
"""
This function scrapes the Albany Times Union homepage
"""
market_url = "https://www.timesunion.com"
wcm_url = "https://wcm.hearstnp.com/index.php?_wcmAction=business/collection&id="
# Get the HTML content of the page
soup = getSoup(market_url)
## --- CENTERPIECE HEADLINES --- ##
# Find the headlines in the "Capital Region News" section
cp_headlines = soup.find_all("a", class_="dynamicSpotlight--item-header")
# Extract the text from the headlines and strip the whitespace
cp_headline = cp_headlines[0].text.strip()
tab2_headline = cp_headlines[1].text.strip()
tab3_headline = cp_headlines[2].text.strip()
tab4_headline = cp_headlines[3].text.strip()
tab5_headline = cp_headlines[4].text.strip()
tab6_headline = cp_headlines[5].text.strip()
tab7_headline = cp_headlines[6].text.strip()
# We extract the URLs from the headlines and strip the whitespace. The href is in the a tag, so we need to use a for loop to extract the URLs.
cp_headlines = soup.find_all("a", class_="dynamicSpotlight--item-header")
cp_url = remove_duplicate_prefix(market_url + cp_headlines[0]["href"], market_url)
tab2_url = remove_duplicate_prefix(market_url + cp_headlines[1]["href"], market_url)
tab3_url = remove_duplicate_prefix(market_url + cp_headlines[2]["href"], market_url)
tab4_url = remove_duplicate_prefix(market_url + cp_headlines[3]["href"], market_url)
tab5_url = remove_duplicate_prefix(market_url + cp_headlines[4]["href"], market_url)
tab6_url = remove_duplicate_prefix(market_url + cp_headlines[5]["href"], market_url)
tab7_url = remove_duplicate_prefix(market_url + cp_headlines[6]["href"], market_url)
# Replace "?IPID=Times-Union-HP-spotlight" with an empty string.
cp_url = cp_url.replace("?IPID=Times-Union-HP-spotlight", "")
tab2_url = tab2_url.replace("?IPID=Times-Union-HP-spotlight", "")
tab3_url = tab3_url.replace("?IPID=Times-Union-HP-spotlight", "")
tab4_url = tab4_url.replace("?IPID=Times-Union-HP-spotlight", "")
tab5_url = tab5_url.replace("?IPID=Times-Union-HP-spotlight", "")
tab6_url = tab6_url.replace("?IPID=Times-Union-HP-spotlight", "")
tab7_url = tab7_url.replace("?IPID=Times-Union-HP-spotlight", "")
cp_order = (
"https://wcm.hearstnp.com/index.php?_wcmAction=business/collection&id=116614"
)
tab2_order = (
"https://wcm.hearstnp.com/index.php?_wcmAction=business/collection&id=116614"
)
tab3_order = (
"https://wcm.hearstnp.com/index.php?_wcmAction=business/collection&id=116614"
)
tab4_order = (
"https://wcm.hearstnp.com/index.php?_wcmAction=business/collection&id=116614"
)
tab5_order = (
"https://wcm.hearstnp.com/index.php?_wcmAction=business/collection&id=116614"
)
tab6_order = (
"https://wcm.hearstnp.com/index.php?_wcmAction=business/collection&id=116614"
)
tab7_order = (
"https://wcm.hearstnp.com/index.php?_wcmAction=business/collection&id=116614"
)
## --- BREAKING NEWS --- ##
# Find all the headlines in the breaking news bar
breaking_headlines = soup.find_all("a", class_="breakingNow--item-headline")
# We extract the text from the breaking news headlines and strip the whitespace. There isn't always a breaking news bar, so we use a try/except block to handle the error.
try:
breaking1_headline = breaking_headlines[0].text.strip()
breaking1_url = f'{market_url}{breaking_headlines[0]["href"]}'
breaking1_url = breaking1_url.replace("?IPID=Times-Union-HP-breaking-bar", "")
breaking1_url = remove_duplicate_prefix(breaking1_url, market_url)
except:
breaking1_headline = None
breaking1_url = None
try:
breaking2_headline = breaking_headlines[1].text.strip()
breaking2_url = f'{market_url}{breaking_headlines[1]["href"]}'
breaking2_url = breaking2_url.replace("?IPID=Times-Union-HP-breaking-bar", "")
breaking2_url = remove_duplicate_prefix(breaking2_url, market_url)
except:
breaking2_headline = None
breaking2_url = None
try:
just_in_headline = soup.find(
"a", {"class": "justNow--item-headline"}
).text.strip()
just_in_url = (
f'{market_url}{soup.find("a", {"class": "justNow--item-headline"})["href"]}'
)
# Remove "?IPID=Times-Union-HP-just-in" from the URL
just_in_url = just_in_url.replace("?IPID=Times-Union-HP-just-in", "")
just_in_url = remove_duplicate_prefix(just_in_url, market_url)
except:
just_in_headline = None
just_in_url = None
# Find all the divs on the page that have a class that contains the strings "hdnce-collection-" AND "-dynamic_breaking_now_tab"
# Example: class="hdnce-collection-114722-dynamic_breaking_now_tab"
breaking_tabs = soup.find_all(
"div", class_=re.compile("hdnce-collection-.*-dynamic_breaking_now_tab")
)
breaking_tab_id_list = []
for tab in breaking_tabs:
# Get the string of digits in the collection
# Example input: hdnce-collection-111490-dynamic_centerpiece_tab
# Example output: 111490
tab_id = re.search(r"\d+", tab["class"][2]).group()
breaking_tab_id_list.append(f"{wcm_url}{tab_id}")
try:
breaking1_order = breaking_tab_id_list[0]
except:
breaking1_order = ""
try:
breaking2_order = breaking_tab_id_list[1]
except:
breaking2_order = ""
# Find the div on the page that has a class that contains the strings "hdnce-collection-" AND "-dynamic_breaking_now" that resides inside of a div with an id of "zoneAL"
# Example: class="hdnce-collection-107158-dynamic_breaking_now"
try:
just_in_order = soup.find("div", id="zoneAL").find(
"div", class_=re.compile("hdnce-collection-.*-dynamic_breaking_now")
)
just_in_id = re.search(r"\d+", just_in_order["class"][2]).group()
just_in_order = f"{wcm_url}{just_in_id}"
except:
just_in_order = ""
## --- TOP HEADLINES --- ##
# Find the top headlines in the "Capital Region News" section
top_headlines_list = soup.find("div", class_="thumbnail-list-wrapper")
top_headlines_list = top_headlines_list.find_all("li")
# Extract the text from the top headlines and strip the whitespace
top1 = top_headlines_list[0].text.strip()
top2 = top_headlines_list[1].text.strip()
top3 = top_headlines_list[2].text.strip()
top4 = top_headlines_list[3].text.strip()
top5 = top_headlines_list[4].text.strip()
top1_url = remove_duplicate_prefix(
market_url + top_headlines_list[0].find("a")["href"], market_url
)
top2_url = remove_duplicate_prefix(
market_url + top_headlines_list[1].find("a")["href"], market_url
)
top3_url = remove_duplicate_prefix(
market_url + top_headlines_list[2].find("a")["href"], market_url
)
top4_url = remove_duplicate_prefix(
market_url + top_headlines_list[3].find("a")["href"], market_url
)
top5_url = remove_duplicate_prefix(
market_url + top_headlines_list[4].find("a")["href"], market_url
)
# Replace "?IPID=Times-Union-HP-latest-news" with an empty string.
top1_url = top1_url.replace("?IPID=Times-Union-HP-latest-news", "")
top2_url = top2_url.replace("?IPID=Times-Union-HP-latest-news", "")
top3_url = top3_url.replace("?IPID=Times-Union-HP-latest-news", "")
top4_url = top4_url.replace("?IPID=Times-Union-HP-latest-news", "")
top5_url = top5_url.replace("?IPID=Times-Union-HP-latest-news", "")
# Find the div on the page that has a class that contains the strings hdnce-collection" AND "-dynamic_headline_list"
# Example: class="hdnce-collection-105799-dynamic_headline_list"
top_headline_order = soup.find(
"div", class_=re.compile("hdnce-collection-.*-dynamic_thumbnail_list")
)
top_headline_id = re.search(r"\d+", top_headline_order["class"][2]).group()
top_headline_order = f"{wcm_url}{top_headline_id}"
# Create a new variable called date and store the current date in the following format: YYYY-MM-DD
date = datetime.now(pytz.timezone(markets["Albany"]["timezone"])).strftime(
"%Y-%m-%d"
)
# Create a new variable called time and store the current time in the appropriate timezone in a 12-hour format without a leading zero
time = datetime.now(pytz.timezone(markets["Albany"]["timezone"])).strftime(
"%-I:%M %p"
)
# Create a dataframe out of the centerpiece headlines and the top headlines.
latest_headlines_df = pd.DataFrame(
{
"Date": date,
"Time": time,
"Breaking 1": breaking1_headline,
"Breaking 2": breaking2_headline,
"Just In": just_in_headline,
"CP": cp_headline,
"Tab 2": tab2_headline,
"Tab 3": tab3_headline,
"Tab 4": tab4_headline,
"Tab 5": tab5_headline,
"Tab 6": tab6_headline,
"Tab 7": tab7_headline,
"Top 1": top1,
"Top 2": top2,
"Top 3": top3,
"Top 4": top4,
"Top 5": top5,
},
index=[0],
)
latest_urls_df = pd.DataFrame(
{
"Date": date,
"Time": time,
"Breaking 1": breaking1_url,
"Breaking 2": breaking2_url,
"Just In": just_in_url,
"CP": cp_url,
"Tab 2": tab2_url,
"Tab 3": tab3_url,
"Tab 4": tab4_url,
"Tab 5": tab5_url,
"Tab 6": tab6_url,
"Tab 7": tab7_url,
"Top 1": top1_url,
"Top 2": top2_url,
"Top 3": top3_url,
"Top 4": top4_url,
"Top 5": top5_url,
},
index=[0],
)
latest_tab_order_df = pd.DataFrame(
{
"Date": date,
"Time": time,
"Breaking 1": breaking1_order,
"Breaking 2": breaking2_order,
"Just In": just_in_order,
"CP": cp_order,
"Tab 2": tab2_order,
"Tab 3": tab3_order,
"Tab 4": tab4_order,
"Tab 5": tab5_order,
"Tab 6": tab6_order,
"Tab 7": tab7_order,
"Top 1": top_headline_order,
"Top 2": top_headline_order,
"Top 3": top_headline_order,
"Top 4": top_headline_order,
"Top 5": top_headline_order,
},
index=[0],
)
# # Print the headlines
# print(
# f"""
# 📰 Breaking 1: {breaking1_headline}
# 📰 Breaking 2: {breaking2_headline}
# 📰 Just in: {just_in_headline}
# 📰 CP: {cp_headline}
# 📰 Tab 2: {tab2_headline}
# 📰 Tab 3: {tab3_headline}
# 📰 Tab 4: {tab4_headline}
# 📰 Tab 5: {tab5_headline}
# 📰 Tab 6: {tab6_headline}
# 📰 Tab 7: {tab7_headline}
# 📰 Headline 1: {top1}
# 📰 Headline 2: {top2}
# 📰 Headline 3: {top3}
# 📰 Headline 4: {top4}
# 📰 Headline 5: {top5}"""
# )
# # Print out all the headlines
# print(
# f"""
# 🔗 Breaking 1 URL: {breaking1_url}
# 🔗 Breaking 2 URL: {breaking2_url}
# 🔗 Just In URL: {just_in_url}
# 🔗 CP URL: {cp_url}
# 🔗 Tab 2 URL: {tab2_url}
# 🔗 Tab 3 URL: {tab3_url}
# 🔗 Tab 4 URL: {tab4_url}
# 🔗 Tab 5 URL: {tab5_url}
# 🔗 Tab 6 URL: {tab6_url}
# 🔗 Tab 7 URL: {tab7_url}
# 🔗 Top 1 URL: {top1_url}
# 🔗 Top 2 URL: {top2_url}
# 🔗 Top 3 URL: {top3_url}
# 🔗 Top 4 URL: {top4_url}
# 🔗 Top 5 URL: {top5_url}
# """
# )
# # Print the tab order
# print(
# f"""
# 🎌 Breaking 1 Order: {breaking1_order}
# 🎌 Breaking 2 Order: {breaking2_order}
# 🎌 Just In Order: {just_in_order}
# 🎌 CP Order: {cp_order}
# 🎌 Tab 2 Order: {tab2_order}
# 🎌 Tab 3 Order: {tab3_order}
# 🎌 Tab 4 Order: {tab4_order}
# 🎌 Tab 5 Order: {tab5_order}
# 🎌 Tab 6 Order: {tab6_order}
# 🎌 Tab 7 Order: {tab7_order}
# 🎌 Top Headlines Order: {top_headline_order}
# """
# )
return latest_headlines_df, latest_urls_df, latest_tab_order_df
def get_san_francisco_headlines():
"""
This function scrapes the San Francisco Chronicle homepage
"""
market_url = "https://www.sfchronicle.com"
wcm_url = "https://wcm.hearstnp.com/index.php?_wcmAction=business/collection&id="
# Get the HTML content of the page
soup = getSoup(market_url)
## --- CENTERPIECE HEADLINES --- ##
# Find all the divs with a class of "centerpiece-tab--main-headline"
cp_headlines = soup.find_all("div", class_="centerpiece-tab--main-headline")
# Certain markets, like the Albany Times Union, use a different template for their homepage. In this scenario, we need to use a different class to find the headlines.
if cp_headlines == []:
cp_headlines = soup.find_all("a", class_="dynamicSpotlight--item-header")
# We extract the text from the headlines and strip the whitespace.