File size: 2,222 Bytes
6473f64 | 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 | # scraper.py
import requests, re
from bs4 import BeautifulSoup
import pandas as pd
from urllib.parse import urljoin
HEADERS = {
"User-Agent": "Mozilla/5.0 (compatible; TenderScraper/1.0; +https://huggingface.co/spaces)"
}
def parse_value(text: str) -> str:
"""Normalise value text like '15 Lakhs' → '15 Lakhs' (strip ₹ icon)."""
return re.sub(r"[^\d.,A-Za-z ]+", "", text).strip()
def scrape_tender_list(url: str) -> pd.DataFrame:
"""Return a DataFrame with one row per <div class='tender_row'>."""
resp = requests.get(url, headers=HEADERS, timeout=20)
resp.raise_for_status()
soup = BeautifulSoup(resp.text, "html.parser")
rows = []
for tr in soup.select("div.tender_row"):
# Authority | City | State
header = tr.select_one("h2.workDesc strong")
if not header:
continue
parts = [t.strip() for t in header.stripped_strings if t.strip()]
authority = parts[0] if parts else ""
city = parts[1][2:] if len(parts) > 1 else "" # "- Raisen" → "Raisen"
state = parts[2][2:] if len(parts) > 2 else ""
# Tender ID + work description
a = tr.select_one("a.m-brief")
tender_id = a.select_one("span.m-tender-id").text.strip() if a else ""
work = a.get_text(" ", strip=True).replace(tender_id, "").strip() if a else ""
tender_url = urljoin(url, a["href"]) if a else ""
# Due date
date_span = tr.select_one("span.m-due-date")
if date_span:
due_text = " ".join(
s.get_text(strip=True)
for s in date_span.parent.select("span")[1:]
)
else:
due_text = ""
# Tender value
value_span = tr.select_one("span.m-value")
value_text = parse_value(value_span.parent.get_text(" ", strip=True)) if value_span else ""
rows.append({
"Authority": authority,
"City": city,
"State": state,
"Tender ID": tender_id,
"Work Description": work,
"Due Date": due_text,
"Estimated Value": value_text,
"Notice URL": tender_url,
})
return pd.DataFrame(rows)
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