# 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)