File size: 5,482 Bytes
aecbded | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | import requests
from bs4 import BeautifulSoup
import time
import os
import csv
import pandas as pd
from tqdm import tqdm
import sys
import string
from IPython.display import clear_output
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..','..')))
from backend.config import MAYO_CSV
class main:
def __init__(self):
self.headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8"
}
self.retries = 3
self.delay = 5
def data_extractor(self, base_url):
diagnosis_treatment_link = ""
doctors_departments_link= ""
for attempt in range(self.retries):
try:
response = requests.get(base_url, headers=self.headers, timeout=20)
response.raise_for_status()
soup = BeautifulSoup(response.text, "html.parser")
content1 = soup.find('a', id="et_genericNavigation_diagnosis-treatment")
if not content1:
for a in soup.find_all('a'):
link_text = a.get_text(separator=' ').strip().lower()
if "diagnosis" in link_text and "treatment" in link_text:
content1 = a
break
if content1:
href1 = content1.get('href')
diagnosis_treatment_link = f"https://www.mayoclinic.org{href1}" if href1 and href1.startswith("/") else href1
content2 = soup.find('a', id="et_genericNavigation_doctors-departments")
if not content2:
# fallback: search by link text containing both words
for a in soup.find_all('a'):
link_text = a.get_text(separator=' ').strip().lower()
if "doctors" in link_text and "departments" in link_text:
content2 = a
break
if content2:
href2 = content2.get('href')
doctors_departments_link = f"https://www.mayoclinic.org{href2}" if href2 and href2.startswith("/") else href2
break # success, exit retry loop
except requests.exceptions.RequestException as e:
print(f"[Attempt {attempt + 1}] Error fetching {base_url}: {e}")
if attempt < self.retries - 1:
time.sleep(self.delay)
return diagnosis_treatment_link, doctors_departments_link
def web_scraping(self,base_url):
# Define the expected headers in order
expected_headers = ["disease", "main_link", "Diagnosis_treatment_link", "Doctors_departments_link"]
# Check if file exists and read existing headers if it does
file_exists = os.path.isfile(MAYO_CSV)
existing_headers = []
if file_exists:
with open(MAYO_CSV, "r", encoding="utf-8") as file:
reader = csv.reader(file)
existing_headers = next(reader, [])
# Determine if we need to write headers
write_headers = not file_exists or existing_headers != expected_headers
# Get the webpage content
response = requests.get(base_url)
if response.status_code != 200:
print("Failed to retrieve page")
exit()
soup = BeautifulSoup(response.text, "html.parser")
items = soup.select(".cmp-results-with-primary-name__see-link, .cmp-results-with-primary-name a")
with open(MAYO_CSV, "a", newline="", encoding="utf-8") as file:
writer = csv.writer(file)
# Write headers if needed
if write_headers:
writer.writerow(expected_headers)
for item in tqdm(items, desc="Scraping Diseases"):
disease_name = item.text.strip()
main_link = f"https://www.mayoclinic.org{item['href']}" if item['href'].startswith("/") else item['href']
link1, link2 = self.data_extractor(main_link)
# Create a row with all expected columns
row_data = {
"disease": disease_name,
"main_link": main_link,
"Diagnosis_treatment_link": link1,
"Doctors_departments_link": link2
}
# If appending to existing file with different headers, align data with existing headers
if file_exists and existing_headers:
row = [row_data.get(header, "") for header in existing_headers]
else:
row = [row_data[header] for header in expected_headers]
writer.writerow(row)
print("Scraping Completed! Data Saved")
if __name__ == "__main__":
scrapper = main()
for letter in string.ascii_uppercase:
print(f"working on this letter {letter} ")
scrapper.web_scraping(f"https://www.mayoclinic.org/diseases-conditions/index?letter={letter}")
clear_output(True)
df = pd.read_csv(MAYO_CSV)
df = df.drop_duplicates(subset=["disease"])
df.to_csv(MAYO_CSV)
# Example usage:
# web_scraping("https://www.mayoclinic.org/diseases-conditions") |