import requests
from bs4 import BeautifulSoup
import re
from urllib.parse import urljoin
import gradio as gr


# ---------------------------------
# Validate proper email
# ---------------------------------
def is_valid_email(email):
    return re.match(r"^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$", email)


# ---------------------------------
# Clean text (remove garbage)
# ---------------------------------
def is_clean_text(text):
    if not text:
        return False

    if len(text) > 100:
        return False

    if any(char in text for char in ["�", "\x00"]):
        return False

    if any(word in text.lower() for word in [
        "obj", "endobj", "stream", "pdf"
    ]):
        return False

    return True


# ---------------------------------
# Validate real name
# ---------------------------------
def is_valid_name(text):
    text_lower = text.lower()

    if any(word in text_lower for word in [
        "iit", "university", "college",
        "department", "school",
        "faculty", "staff", "research",
        "principal", "administrator"
    ]):
        return False

    if "@" in text:
        return False

    words = text.split()

    return 2 <= len(words) <= 5


# ---------------------------------
# Email → Name fallback
# ---------------------------------
def name_from_email(email):
    username = email.split("@")[0]

    parts = re.split(r"[._]", username)

    if len(parts) == 1:
        parts = re.findall('[A-Za-z][^A-Z]*', username)

    return " ".join([p.capitalize() for p in parts if p])


# ---------------------------------
# Find closest name to email
# ---------------------------------
def find_name_near_email(text_node):
    parent = text_node.parent

    # Check previous siblings (closest first)
    for sibling in parent.previous_siblings:
        if hasattr(sibling, "get_text"):
            text = sibling.get_text(strip=True)

            if is_valid_name(text) and is_clean_text(text):
                return text

    # Check inside same parent
    text = parent.get_text("\n", strip=True)
    lines = text.split("\n")

    for line in lines:
        line = line.strip()

        if is_valid_name(line) and is_clean_text(line):
            return line

    return None


# ---------------------------------
# Main scraper
# ---------------------------------
def get_professor_details(college_url):
    try:
        headers = {"User-Agent": "Mozilla/5.0"}

        response = requests.get(college_url, headers=headers, timeout=10)
        soup = BeautifulSoup(response.text, "html.parser")

        # Find faculty-related links
        keywords = ["faculty", "people", "staff", "directory"]
        faculty_links = []

        for link in soup.find_all("a", href=True):
            href = link["href"].lower()
            if any(word in href for word in keywords):
                faculty_links.append(urljoin(college_url, link["href"]))

        pages_to_scan = faculty_links if faculty_links else [college_url]

        results = []
        seen = set()

        for page in pages_to_scan:
            try:
                res = requests.get(page, headers=headers, timeout=10)
                page_soup = BeautifulSoup(res.text, "html.parser")

                # Find all text nodes containing emails
                for text_node in page_soup.find_all(string=re.compile(r"@")):
                    emails = re.findall(r"[\w\.-]+@[\w\.-]+", text_node)

                    for email in emails:
                        if not is_valid_email(email):
                            continue

                        if email in seen:
                            continue

                        seen.add(email)

                        # Try to find real name near email
                        name = find_name_near_email(text_node)

                        # Fallback
                        if not name:
                            name = name_from_email(email)

                        results.append({
                            "name": name,
                            "email": email
                        })

            except:
                continue

        if not results:
            return "No data found."

        # Format output
        output = ""
        for r in results:
            output += f"Name: {r['name']}\nEmail: {r['email']}\n\n"

        return output

    except Exception as e:
        return f"Error: {str(e)}"


# ---------------------------------
# Gradio UI
# ---------------------------------
iface = gr.Interface(
    fn=get_professor_details,
    inputs=gr.Textbox(label="Enter College Website URL"),
    outputs=gr.Textbox(label="Professor Details"),
    title="🎓 College Professor Scraper (Final Version)",
    description="Extracts professor names and emails using hybrid intelligent scraping"
)

iface.launch()