# File: main/app.py
# Purpose: Fetch a URL and return only relevant text (title, metadata, clean main text) and hyperlinks.
# Bonus: Special handling for Hacker News front page to list posts (rank, title, points, comments).
# UI: Gradio Blocks with Markdown + DataFrame outputs, suitable for MCP usage.
# Notes: Comments are in layman's terms to explain each section.

import gradio as gr                    # UI framework for the web app
import requests                        # HTTP client to fetch web pages
from bs4 import BeautifulSoup          # HTML parser to extract tags and text
from readability import Document       # Readability algorithm to find main content
from urllib.parse import urljoin, urlparse  # Tools to resolve relative/absolute URLs
from dataclasses import dataclass       # For neat, typed containers
from typing import List, Dict, Tuple
import re                               # Regular expressions for cleanup
from datetime import datetime           # For formatting dates in metadata safely


# =========================
# Helpers: small data shapes
# =========================

@dataclass
class PageMetadata:
    # Simple holder for high-level metadata we care about
    title: str = ""
    canonical_url: str = ""
    description: str = ""
    site_name: str = ""
    og_type: str = ""
    og_url: str = ""
    published_time: str = ""  # ISO-ish if detected


# =========================
# Network: fetch raw HTML
# =========================

def fetch_html(url: str, timeout: int = 12) -> str:
    """
    Downloads the HTML for a given URL using a browser-like User-Agent.
    Returns text or raises an HTTP/Request error if something fails.
    """
    headers = {
        # Pretend to be a modern desktop browser so we don't get blocked
        "User-Agent": (
            "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
            "AppleWebKit/537.36 (KHTML, like Gecko) "
            "Chrome/127.0.0.0 Safari/537.36"
        )
    }
    resp = requests.get(url, headers=headers, timeout=timeout)
    resp.raise_for_status()  # If it's 4xx/5xx, this throws; we catch it above in the Gradio fn
    return resp.text


# ===================================
# Generic extraction: metadata + text
# ===================================

def extract_metadata(soup: BeautifulSoup, base_url: str) -> PageMetadata:
    """
    Pulls common metadata from <title>, <meta>, and <link rel="canonical">.
    We check Open Graph and Twitter tags as fallbacks too.
    """
    md = PageMetadata()

    # Title from <title> or og:title/twitter:title
    title_tag = soup.find("title")
    md.title = (title_tag.get_text(strip=True) if title_tag else "").strip()

    # Meta helpers
    def meta(name=None, property=None):
        if name:
            tag = soup.find("meta", attrs={"name": name})
            if tag and tag.get("content"):
                return tag["content"].strip()
        if property:
            tag = soup.find("meta", attrs={"property": property})
            if tag and tag.get("content"):
                return tag["content"].strip()
        return ""

    # Description (prefer og:description > twitter:description > meta description)
    md.description = (
        meta(property="og:description")
        or meta(name="twitter:description")
        or meta(name="description")
        or ""
    ).strip()

    # Site name (if available)
    md.site_name = (meta(property="og:site_name") or "").strip()

    # OpenGraph URL + type (if available)
    md.og_url = (meta(property="og:url") or "").strip()
    md.og_type = (meta(property="og:type") or "").strip()

    # Canonical URL (normalize relative -> absolute)
    canon = soup.find("link", rel="canonical")
    if canon and canon.get("href"):
        md.canonical_url = urljoin(base_url, canon["href"].strip())
    else:
        # If no canonical, we may fallback to og:url if present
        md.canonical_url = md.og_url or base_url

    # Try some common publish-time signals
    published = (
        meta(property="article:published_time")
        or meta(name="pubdate")
        or meta(name="date")
        or ""
    ).strip()
    md.published_time = published

    # If no normal <title>, try OG or Twitter titles
    if not md.title:
        md.title = (meta(property="og:title") or meta(name="twitter:title") or "").strip()

    return md


def extract_main_text(html: str) -> Tuple[str, BeautifulSoup]:
    """
    Uses the readability library to find the 'main content' of an article-like page.
    Returns a clean text string and a BeautifulSoup of the main content HTML
    (so we can also extract links from just the relevant area).
    If readability fails/misfires (like index pages), we gracefully fallback to empty text.
    """
    try:
        doc = Document(html)                     # Run Readability on the HTML
        summary_html = doc.summary()             # This is the extracted main-content HTML
        # Parse the readability summary into a soup so we can pull out links cleanly
        summary_soup = BeautifulSoup(summary_html, "lxml")
        # Turn HTML to plain text: keep paragraphs and line breaks readable
        # Remove scripts/styles etc. if any slipped through
        for tag in summary_soup(["script", "style", "noscript"]):
            tag.decompose()
        text = summary_soup.get_text("\n", strip=True)
        text = re.sub(r"\n{3,}", "\n\n", text)   # Collapse superfluous line breaks
        return text, summary_soup
    except Exception:
        # If something goes wrong (e.g., not article-shaped), return empty content
        return "", BeautifulSoup("", "lxml")


def collect_links(soup: BeautifulSoup, base_url: str, only_content_area: bool, fallback_html: str) -> List[Dict]:
    """
    Finds hyperlinks. If we have a 'main content' soup and the user asked for
    content-only links, we grab links from there; otherwise, fall back to the whole page.
    We resolve relative URLs to absolute and skip junk (javascript:, #, mailto:).
    """
    anchors = []
    if soup and only_content_area:
        anchors = soup.find_all("a")
    else:
        full = BeautifulSoup(fallback_html, "lxml")
        anchors = full.find_all("a")

    results = []
    seen = set()
    for a in anchors:
        href = (a.get("href") or "").strip()
        text = a.get_text(" ", strip=True)
        if not href:
            continue
        # Skip empty, anchors, JS, and non-http links
        if href.startswith("#") or href.startswith("javascript:") or href.startswith("mailto:"):
            continue
        # Make absolute
        absolute = urljoin(base_url, href)
        # Deduplicate by absolute URL + link text combo
        key = (absolute, text)
        if key in seen:
            continue
        seen.add(key)
        domain = urlparse(absolute).netloc
        results.append({"Text": text or "(no text)", "URL": absolute, "Domain": domain})
    return results


# ====================================
# Special-case: Hacker News front page
# ====================================

def is_hn_front(url: str) -> bool:
    """
    Checks if the URL is the Hacker News front page (news.ycombinator.com).
    We'll special-handle it for a great experience listing posts.
    """
    p = urlparse(url)
    if p.netloc != "news.ycombinator.com":
        return False
    # Treat /, /news, or /front as "front page" style
    return p.path in ("", "/", "/news", "/front")


def parse_hn_front(html: str, base_url: str) -> Tuple[str, List[Dict]]:
    """
    Parses the Hacker News front page HTML to extract ranked items with points and comments.
    Returns a Markdown overview and a list-of-dicts suitable for a table.
    """
    soup = BeautifulSoup(html, "lxml")
    items = []

    # Each story is a <tr class="athing">; subtext is in the immediate next <tr>
    for story in soup.select("tr.athing"):
        # Rank (e.g., "1.") is usually in a sibling cell, but sometimes inside
        rank_tag = story.select_one("span.rank")
        rank = (rank_tag.get_text(strip=True).replace(".", "") if rank_tag else "")

        # Title + URL (HN changed markup: 'span.titleline a' is current)
        title_a = story.select_one("span.titleline > a") or story.select_one("a.titlelink") or story.select_one("a.storylink")
        title = title_a.get_text(strip=True) if title_a else "(no title)"
        url = urljoin(base_url, title_a["href"]) if (title_a and title_a.get("href")) else base_url

        # Source domain (e.g., (github.com))
        site = story.select_one("span.sitestr")
        source = site.get_text(strip=True) if site else urlparse(url).netloc

        # Subtext row comes right after the 'athing' row
        subtext_row = story.find_next_sibling("tr")
        points, comments, age, by = "", "", "", ""
        if subtext_row:
            # Points like "123 points"
            score = subtext_row.select_one("span.score")
            points = score.get_text(strip=True) if score else ""
            # Byline: "by username"
            user_a = subtext_row.select_one("a.hnuser")
            by = user_a.get_text(strip=True) if user_a else ""
            # Age: "5 hours ago"
            age_tag = subtext_row.select_one("span.age")
            age = age_tag.get_text(strip=True) if age_tag else ""
            # Comments link: last <a> typically ends with "comments" or "discuss"
            comment_a = None
            links = subtext_row.select("a")
            if links:
                comment_a = links[-1]
            comments = (comment_a.get_text(strip=True) if comment_a else "").lower()

        items.append({
            "Rank": rank,
            "Title": title,
            "URL": url,
            "Source": source,
            "Points": points,
            "By": by,
            "Age": age,
            "Comments": comments,
        })

    # Build a tight Markdown digest so you can "use" HN inside the tool
    md_lines = ["# Hacker News — Front Page",
                "",
                "Here are the current front-page posts (click to open):",
                ""]
    for it in items:
        rank = it["Rank"] or "•"
        title = it["Title"]
        url = it["URL"]
        pts = it["Points"] or ""
        cmt = it["Comments"] or ""
        age = it["Age"] or ""
        src = it["Source"] or ""
        # Example line: "1. [Cool Project](url) — 345 points • 123 comments • 5 hours ago (github.com)"
        extras = " — ".join(filter(None, [
            " ".join(filter(None, [pts, cmt])),
            age,
            f"({src})"
        ]))
        md_lines.append(f"{rank}. [{title}]({url}){(' — ' + extras) if extras else ''}")
    md = "\n".join(md_lines) if items else "# Hacker News — No items found"

    return md, items


# ===========================
# Public function for Gradio
# ===========================

def extract_page(url: str, full_text: bool, max_links: int, content_links_only: bool) -> Tuple[str, List[Dict]]:
    """
    Main function wired to the UI.
    - Fetches the page
    - If it's Hacker News front page, parse posts specially
    - Otherwise: extract metadata, main text (optional), and links
    - Returns Markdown (summary) + a table of links
    """
    try:
        html = fetch_html(url)
    except requests.exceptions.RequestException as e:
        # Friendly error message for the UI textbox
        return f"## Error\nUnable to fetch the page.\n\n**Details:** {e}", []

    # Hacker News special handling for top-notch usability
    if is_hn_front(url):
        md, items = parse_hn_front(html, url)
        return md, items  # For HN, the table is the rich story list

    # Generic page pipeline
    soup_full = BeautifulSoup(html, "lxml")                # Full page soup for metadata and optional link fallback
    metadata = extract_metadata(soup_full, url)            # Title, canonical, description, etc.
    main_text, summary_soup = extract_main_text(html)      # Readability content (may be empty on index pages)

    # Choose where we harvest links from
    links = collect_links(summary_soup, url, content_links_only, html)
    if max_links and max_links > 0:
        links = links[:max_links]

    # Build a readable Markdown summary
    md_lines = []

    # Title line (prefer metadata title)
    title_to_show = metadata.title or "(Untitled)"
    md_lines.append(f"# {title_to_show}")

    # Canonical + URL info
    if metadata.canonical_url and metadata.canonical_url != url:
        md_lines.append(f"- **Canonical:** {metadata.canonical_url}")
    md_lines.append(f"- **URL:** {url}")

    # Optional metadata lines
    if metadata.site_name:
        md_lines.append(f"- **Site:** {metadata.site_name}")
    if metadata.description:
        md_lines.append(f"- **Description:** {metadata.description}")
    if metadata.published_time:
        md_lines.append(f"- **Published:** {metadata.published_time}")
    if metadata.og_type:
        md_lines.append(f"- **OG Type:** {metadata.og_type}")

    # Spacer
    md_lines.append("\n---\n")

    # Main content (optional, controlled by checkbox)
    if full_text and main_text:
        md_lines.append("## Main Content")
        # Keep things readable; long pages can be huge—Readability already helps keep it topical
        md_lines.append(main_text)
        md_lines.append("\n---\n")

    # Links brief (we also return a structured table below)
    md_lines.append("## Links Found")
    md_lines.append(
        f"Showing {'content-only' if content_links_only else 'all-page'} links (up to {max_links}). "
        "Click any to open in a new tab."
    )

    md = "\n".join(md_lines)
    return md, links


# ===========
# Gradio UI
# ===========

# Build a Blocks UI so we can have multiple outputs (Markdown + DataFrame) nicely arranged
with gr.Blocks(theme="Nymbo/Nymbo_Theme", title="Fetch MCP — Clean Text & Links") as demo:
    # --- Header area: title + quick helper buttons
    gr.Markdown("# Fetch MCP — Clean Text & Links\n"
                "Extract clean **title**, **metadata**, **main text**, and **hyperlinks** from any page.\n\n"
                "- Special handling for **Hacker News** front page (rank, points, comments).\n"
                "- Toggle **Full Text** if you also want the extracted article content.")

    with gr.Row():
        url_in = gr.Textbox(
            label="URL",
            placeholder="https://news.ycombinator.com/  •  https://example.com/article",
            value="https://news.ycombinator.com/",
            scale=4
        )
        fetch_btn = gr.Button("Fetch / Extract", variant="primary", scale=1)

    with gr.Row():
        full_text_chk = gr.Checkbox(
            label="Include main content text (Readability extract)?",
            value=False
        )
        content_only_chk = gr.Checkbox(
            label="Links from main content only (fallback: full page)?",
            value=True
        )
        max_links_sld = gr.Slider(
            label="Max links to return",
            minimum=10, maximum=500, value=100, step=10
        )

    # Outputs: Markdown summary + a table of links (or HN posts table)
    summary_md = gr.Markdown(label="Summary")
    links_tbl = gr.Dataframe(
        headers=["Rank/—", "Title/Text", "URL", "Source/Domain", "Points", "By", "Age", "Comments"],
        # We won't pre-enforce headers strictly; DataFrame will adapt to dict keys provided.
        interactive=False,
        wrap=True,
        row_count=(0, "dynamic"),
        col_count=(0, "dynamic")
    )

    # Wire up the action: clicking the button runs extract_page and shows results
    fetch_btn.click(
        fn=extract_page,
        inputs=[url_in, full_text_chk, max_links_sld, content_only_chk],
        outputs=[summary_md, links_tbl]
    )

# Keep MCP server behavior enabled for your setup
if __name__ == "__main__":
    demo.launch(mcp_server=True)