import streamlit as st
import pandas as pd
from bs4 import BeautifulSoup
import requests

# Streamlit UI
st.set_page_config(page_title="IndiaMART Scraper", layout="centered")
st.title("📦 IndiaMART Product Scraper")

# Input fields
query = st.text_input("🔍 Enter Product Search Query", value="infrared lamp sensor")
total_pages = st.number_input("📄 Number of Pages to Scrape", value=2, min_value=1)

if st.button("🚀 Start Scraping"):
    product_names, prices, cities, addresses, companies = [], [], [], [], []

    for page in range(1, total_pages + 1):
        url = f"https://dir.indiamart.com/search.mp?ss={query.replace(' ', '+')}&page={page}"
        st.info(f"🌐 Scraping Page {page}: {url}")

        headers = {
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"
        }

        try:
            res = requests.get(url, headers=headers, timeout=10)
            soup = BeautifulSoup(res.text, "html.parser")
            cards = soup.find_all("div", class_="card")

            if not cards:
                st.warning(f"⚠️ No product cards found on Page {page}")
                continue

            for card in cards:
                name = card.find("a", class_="cardlinks")
                price = card.find("p", class_="price")
                city_span = card.find("span", class_="elps elps1")
                address_p = card.find("p", class_="tac wpw")
                company = card.find("a", {"data-click": "^CompanyName"})

                product_names.append(name.text.strip() if name else "No Name")
                prices.append(price.text.strip() if price else "N/A")
                cities.append(city_span.text.strip() if city_span else "N/A")
                addresses.append(address_p.text.strip() if address_p else "N/A")
                companies.append(company.text.strip() if company else "N/A")

        except Exception as e:
            st.error(f"❌ Failed to load page {page}: {e}")

    # Create DataFrame and Excel
    if product_names:
        df = pd.DataFrame({
            "Product Name": product_names,
            "Price": prices,
            "City": cities,
            "Address": addresses,
            "Company": companies
        })

        filename = f"{query.replace(' ', '_')}_indiamart_results.xlsx"
        df.to_excel(filename, index=False)
        st.success("✅ Scraping complete!")

        with open(filename, "rb") as f:
            st.download_button("📥 Download Excel", f, file_name=filename)
    else:
        st.warning("⚠️ No data extracted. Try a different query or increase pages.")