import streamlit as st
import requests
from bs4 import BeautifulSoup
import pandas as pd
import re
import urllib3

urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)

st.set_page_config(page_title="Faculty Scraper", layout="wide")

st.title("🎓 Universal Faculty Scraper (Robust Mode)")
st.write("Handles even badly structured faculty pages.")

# -------- REGEX --------
EMAIL_REGEX = r"[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}"
PHONE_REGEX = r"\+?\d[\d\s\-]{8,15}"

BAD_NAMES = [
    "welcome", "faculty", "central", "activities",
    "department", "areas of interest", "important links"
]

# -------- NAME FROM EMAIL --------
def name_from_email(email):
    name_part = email.split("@")[0]
    name_part = name_part.replace(".", " ").replace("_", " ")
    return " ".join([w.capitalize() for w in name_part.split()])

# -------- MAIN SCRAPER --------
def scrape_faculty(url):
    headers = {"User-Agent": "Mozilla/5.0"}

    try:
        response = requests.get(url, headers=headers, verify=False, timeout=10)
    except:
        return pd.DataFrame(), "❌ Failed to load page"

    soup = BeautifulSoup(response.text, "html.parser")

    # -------- METHOD 1: BLOCK PARSING --------
    blocks = soup.find_all(["div", "li", "tr", "section"])
    raw_data = []

    for block in blocks:
        text = block.get_text(" ", strip=True)

        if len(text) < 30:
            continue

        email_match = re.search(EMAIL_REGEX, text)
        if not email_match:
            continue

        email = email_match.group()

        phone_match = re.search(PHONE_REGEX, text)
        phone = phone_match.group() if phone_match else "Not Available"

        name = None
        for tag in block.find_all(["h1", "h2", "h3", "h4", "strong", "b"]):
            candidate = tag.get_text(strip=True)

            if (
                3 < len(candidate) < 60
                and not re.search(EMAIL_REGEX, candidate)
                and not any(b in candidate.lower() for b in BAD_NAMES)
            ):
                name = candidate
                break

        raw_data.append([name, email, phone])

    df = pd.DataFrame(raw_data, columns=["Name", "Email", "Phone"])

    # -------- METHOD 2: FALLBACK (GLOBAL EXTRACTION) --------
    if df.empty or len(df) < 5:
        text = soup.get_text(" ", strip=True)

        emails = re.findall(EMAIL_REGEX, text)
        phones = re.findall(PHONE_REGEX, text)

        emails = list(set(emails))
        phones = list(set(phones))

        data = []

        for i, email in enumerate(emails):
            name = name_from_email(email)

            phone = phones[i] if i < len(phones) else "Not Available"

            data.append([name, email, phone])

        df = pd.DataFrame(data, columns=["Name", "Email", "Phone"])

    # -------- CLEANING --------
    cleaned_data = []

    for email, group in df.groupby("Email"):
        names = group["Name"].dropna().tolist()
        phones = group["Phone"].dropna().tolist()

        best_name = None

        for n in names:
            if n and not any(b in n.lower() for b in BAD_NAMES):
                if any(t in n.lower() for t in ["dr", "mr", "ms", "prof"]):
                    best_name = n
                    break

        if not best_name and names:
            valid = [n for n in names if not any(b in str(n).lower() for b in BAD_NAMES)]
            if valid:
                best_name = max(valid, key=len)

        if not best_name:
            best_name = name_from_email(email)

        best_phone = "Not Available"
        for p in phones:
            if p != "Not Available":
                best_phone = p
                break

        cleaned_data.append([best_name, email, best_phone])

    final_df = pd.DataFrame(cleaned_data, columns=["Name", "Email", "Phone"])

    return final_df, "✅ Data Extracted Successfully"


# -------- UI --------
url = st.text_input("🔗 Enter Faculty Page URL")

if st.button("🚀 Scrape Data"):
    if not url:
        st.warning("Please enter a URL")
    else:
        with st.spinner("Scraping..."):
            df, status = scrape_faculty(url)

        st.success(status)

        if not df.empty:
            st.dataframe(df, use_container_width=True)

            csv = df.to_csv(index=False).encode("utf-8")

            st.download_button(
                "📥 Download CSV",
                csv,
                "faculty_data.csv",
                "text/csv"
            )
        else:
            st.error("No data found.")