import os
import sys
import re
import csv
import json
import time
import math
import shutil
import socket
import hashlib
import asyncio
import textwrap
import subprocess
import threading
from pathlib import Path
from functools import partial
from http.server import ThreadingHTTPServer, SimpleHTTPRequestHandler
from importlib.metadata import version, PackageNotFoundError
SETUP_SENTINEL = "/content/.crawlee_python_tutorial_setup_done_v2"
def sh(command, check=True, quiet=False):
   print(f"\n$ {command}")
   result = subprocess.run(
       command,
       shell=True,
       text=True,
       stdout=subprocess.PIPE,
       stderr=subprocess.STDOUT,
   )
   if not quiet and result.stdout:
       print(result.stdout[-5000:])
   if check and result.returncode != 0:
       raise RuntimeError(f"Command failed with exit code {result.returncode}: {command}")
   return result.returncode == 0
def package_version(package_name):
   try:
       return version(package_name)
   except PackageNotFoundError:
       return None
def is_good_pydantic_version(v):
   if not v:
       return False
   m = re.match(r"^(\d+)\.(\d+)", v)
   if not m:
       return False
   major, minor = int(m.group(1)), int(m.group(2))
   return major == 2 and minor == 11
current_crawlee = package_version("crawlee")
current_pydantic = package_version("pydantic")
needs_setup = (
   not os.path.exists(SETUP_SENTINEL)
   or current_crawlee is None
   or not is_good_pydantic_version(current_pydantic)
)
if needs_setup:
   print("PHASE 1: Installing compatible Crawlee + Pydantic + Playwright dependencies.")
   print("After this finishes, Colab will restart automatically. Then run this same cell again.")
   sh(f'{sys.executable} -m pip uninstall -y crawlee pydantic pydantic-core', check=False)
   sh(
       f'{sys.executable} -m pip install -q -U '
       f'"pydantic>=2.11,<2.12" '
       f'"crawlee[all]" '
       f'pandas matplotlib networkx nest_asyncio beautifulsoup4 parsel'
   )
   sh(f'{sys.executable} -m playwright install --with-deps chromium', check=False)
   Path(SETUP_SENTINEL).write_text("done", encoding="utf-8")
   print("\nInstalled versions:")
   sh(f'{sys.executable} -m pip show crawlee pydantic pydantic-core', check=False)
   try:
       import google.colab
       print("\nRestarting Colab runtime now. After it reconnects, run this same cell again.")
       os.kill(os.getpid(), 9)
   except Exception:
       raise SystemExit("Setup complete. Restart the runtime/kernel manually, then run this cell again.")
print("PHASE 2: Dependencies are ready. Running the Crawlee tutorial.")
import pandas as pd
import matplotlib.pyplot as plt
import networkx as nx
import nest_asyncio
nest_asyncio.apply()
TUTORIAL_ROOT = Path("/content/crawlee_python_advanced_tutorial")
SITE_DIR = TUTORIAL_ROOT / "demo_site"
OUTPUT_DIR = TUTORIAL_ROOT / "outputs"
STORAGE_DIR = TUTORIAL_ROOT / "crawlee_storage"
SCREENSHOT_DIR = OUTPUT_DIR / "screenshots"
for path in [SITE_DIR, OUTPUT_DIR, STORAGE_DIR]:
   if path.exists():
       shutil.rmtree(path)
for path in [SITE_DIR, OUTPUT_DIR, STORAGE_DIR, SCREENSHOT_DIR]:
   path.mkdir(parents=True, exist_ok=True)
os.environ["CRAWLEE_STORAGE_DIR"] = str(STORAGE_DIR)
os.environ["CRAWLEE_LOG_LEVEL"] = "INFO"
os.environ["CRAWLEE_PURGE_ON_START"] = "true"
from crawlee import Glob, ConcurrencySettings
from crawlee.crawlers import (
   BeautifulSoupCrawler,
   BeautifulSoupCrawlingContext,
   ParselCrawler,
   ParselCrawlingContext,
   PlaywrightCrawler,
   PlaywrightCrawlingContext,
)
try:
   import crawlee
   print("Crawlee version:", crawlee.__version__)
except Exception:
   print("Crawlee imported successfully.")
print("Pydantic version:", package_version("pydantic"))
def safe_slug(value):
   value = re.sub(r"[^a-zA-Z0-9]+", "-", str(value)).strip("-").lower()
   return value or "item"
def money_to_float(value):
   if value is None:
       return None
   cleaned = re.sub(r"[^0-9.]", "", str(value))
   return float(cleaned) if cleaned else None
def normalize_text(value, max_len=None):
   value = re.sub(r"\s+", " ", value or "").strip()
   return value[:max_len] if max_len else value
def write_file(path, content):
   path = Path(path)
   path.parent.mkdir(parents=True, exist_ok=True)
   path.write_text(textwrap.dedent(content).strip() + "\n", encoding="utf-8")

PRODUCTS = [
   {
       "sku": "CRW-101",
       "name": "Crawler Reliability Kit",
       "category": "automation",
       "price": 149.0,
       "rating": 4.8,
       "stock": 18,
       "features": ["retry policy", "queue replay", "structured logs"],
       "related": ["CRW-202", "CRW-303"],
   },
   {
       "sku": "CRW-202",
       "name": "Playwright Rendering Pack",
       "category": "browser",
       "price": 249.0,
       "rating": 4.7,
       "stock": 9,
       "features": ["headless chromium", "screenshots", "dynamic DOM extraction"],
       "related": ["CRW-101", "CRW-404"],
   },
   {
       "sku": "CRW-303",
       "name": "RAG Extraction Bundle",
       "category": "ai-data",
       "price": 199.0,
       "rating": 4.9,
       "stock": 13,
       "features": ["clean text chunks", "metadata capture", "JSONL export"],
       "related": ["CRW-101", "CRW-505"],
   },
   {
       "sku": "CRW-404",
       "name": "Anti-Fragile Session Toolkit",
       "category": "resilience",
       "price": 299.0,
       "rating": 4.6,
       "stock": 5,
       "features": ["session rotation", "state recovery", "graceful failures"],
       "related": ["CRW-202", "CRW-505"],
   },
   {
       "sku": "CRW-505",
       "name": "Data Export Control Plane",
       "category": "storage",
       "price": 179.0,
       "rating": 4.5,
       "stock": 21,
       "features": ["datasets", "key-value store", "CSV and JSON export"],
       "related": ["CRW-303", "CRW-404"],
   },
]
def layout(title, body, extra_head="", extra_script=""):
   css = """
   <style>
     body {
       font-family: Inter, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
       margin: 0;
       background: #f7f7fb;
       color: #1f2430;
     }
     header {
       background: #202638;
       color: white;
       padding: 28px 40px;
     }
     nav a {
       color: #dbe7ff;
       margin-right: 18px;
       text-decoration: none;
       font-weight: 600;
     }
     main {
       max-width: 1050px;
       margin: 0 auto;
       padding: 32px;
     }
     .grid {
       display: grid;
       grid-template-columns: repeat(auto-fit, minmax(230px, 1fr));
       gap: 18px;
     }
     .card, article, .panel {
       background: white;
       border: 1px solid #e5e7ef;
       border-radius: 16px;
       padding: 20px;
       box-shadow: 0 8px 25px rgba(20, 30, 60, 0.05);
     }
     .price {
       font-size: 1.3rem;
       font-weight: 800;
     }
     .tag {
       display: inline-block;
       background: #edf2ff;
       border: 1px solid #d6e0ff;
       border-radius: 999px;
       padding: 4px 10px;
       margin: 3px;
       font-size: 0.82rem;
     }
     .stock-low {
       color: #b42318;
       font-weight: 700;
     }
     .stock-ok {
       color: #067647;
       font-weight: 700;
     }
     code, pre {
       background: #111827;
       color: #d1fae5;
       border-radius: 10px;
     }
     pre {
       padding: 16px;
       overflow-x: auto;
     }
     footer {
       padding: 30px 40px;
       color: #606779;
     }
   </style>
   """
   return f"""
   <!doctype html>
   <html lang="en">
     <head>
       <meta charset="utf-8">
       <meta name="viewport" content="width=device-width, initial-scale=1">
       <meta name="description" content="{title} page for a Crawlee Python tutorial demo website.">
       <title>{title}</title>
       {css}
       {extra_head}
     </head>
     <body>
       <header>
         <h1>{title}</h1>
         <nav>
           <a href="/index.html">Home</a>
           <a href="/products/product-crw-101.html">Products</a>
           <a href="/docs/getting-started.html">Docs</a>
           <a href="/blog/crawling-at-scale.html">Blog</a>
           <a href="/dynamic.html">Dynamic JS Page</a>
           <a href="/admin/hidden.html">Admin</a>
         </nav>
       </header>
       <main>{body}</main>
       <footer>Local demo website generated for Crawlee Python advanced tutorial.</footer>
       {extra_script}
     </body>
   </html>
   """
def build_demo_site():
   write_file(
       SITE_DIR / "robots.txt",
       """
       User-agent: *
       Disallow: /admin/
       Allow: /
       """,
   )
   product_cards = []
   for product in PRODUCTS:
       product_cards.append(
           f"""
           <div class="card product-teaser" data-sku="{product['sku']}" data-category="{product['category']}">
             <h2><a href="/products/product-{safe_slug(product['sku'])}.html">{product['name']}</a></h2>
             <p>{product['category']} crawler module with rating {product['rating']}.</p>
             <p class="price" data-price="{product['price']}">${product['price']:.2f}</p>
             <p class="{'stock-low' if product['stock'] < 10 else 'stock-ok'}">Stock: {product['stock']}</p>
           </div>
           """
       )
   write_file(
       SITE_DIR / "index.html",
       layout(
           "Crawlee Demo Commerce + Docs Hub",
           f"""
           <section class="panel">
             <h2>Why this site exists</h2>
             <p>
               This local website gives us predictable pages for testing Crawlee without scraping a third-party website.
               We include static HTML pages, documentation pages, product detail pages, a blog article, robots.txt,
               and a JavaScript-rendered page.
             </p>
           </section>
           <h2>Featured crawler modules</h2>
           <section class="grid">
             {''.join(product_cards)}
           </section>
           <section class="panel">
             <h2>Internal links for recursive crawling</h2>
             <ul>
               <li><a href="/docs/getting-started.html">Getting started guide</a></li>
               <li><a href="/docs/advanced-routing.html">Advanced routing guide</a></li>
               <li><a href="/blog/crawling-at-scale.html">Crawling at scale article</a></li>
               <li><a href="/dynamic.html">JavaScript-rendered catalog</a></li>
               <li><a href="/admin/hidden.html">Admin page blocked by robots and crawler filters</a></li>
             </ul>
           </section>
           """,
       ),
   )
   for product in PRODUCTS:
       related_links = "\n".join(
           f'<li><a class="related-link" href="/products/product-{safe_slug(sku)}.html">{sku}</a></li>'
           for sku in product["related"]
       )
       feature_list = "\n".join(f"<li>{feature}</li>" for feature in product["features"])
       json_ld = json.dumps(
           {
               "@context": "https://schema.org",
               "@type": "Product",
               "sku": product["sku"],
               "name": product["name"],
               "category": product["category"],
               "offers": {
                   "@type": "Offer",
                   "price": product["price"],
                   "priceCurrency": "USD",
               },
               "aggregateRating": {
                   "@type": "AggregateRating",
                   "ratingValue": product["rating"],
               },
           },
           indent=2,
       )
       write_file(
           SITE_DIR / "products" / f"product-{safe_slug(product['sku'])}.html",
           layout(
               f"{product['name']} | Product Detail",
               f"""
               <article class="product"
                        data-sku="{product['sku']}"
                        data-category="{product['category']}"
                        data-rating="{product['rating']}"
                        data-stock="{product['stock']}">
                 <h2 class="product-title">{product['name']}</h2>
                 <p class="sku">SKU: <strong>{product['sku']}</strong></p>
                 <p class="category">Category: <strong>{product['category']}</strong></p>
                 <p class="price" data-price="{product['price']}">${product['price']:.2f}</p>
                 <p class="rating">Rating: {product['rating']} / 5</p>
                 <p class="{'stock-low' if product['stock'] < 10 else 'stock-ok'}">Stock: {product['stock']}</p>
                 <h3>Features</h3>
                 <ul class="features">{feature_list}</ul>
                 <h3>Related modules</h3>
                 <ul>{related_links}</ul>
               </article>
               <script type="application/ld+json">{json_ld}</script>
               """,
           ),
       )

   write_file(
       SITE_DIR / "docs" / "getting-started.html",
       layout(
           "Getting Started with Reliable Crawlers",
           """
           <article class="doc" data-doc-id="getting-started">
             <h2>HTTP-first crawling strategy</h2>
             <p>
               We start with HTTP crawlers because they are lightweight and efficient.
               Browser crawling is reserved for pages that need JavaScript rendering.
             </p>
             <h2>Core extraction fields</h2>
             <p>
               Each crawler extracts URL, title, page type, text summary, outgoing links, and page-specific metadata.
             </p>
             <pre><code>crawler = BeautifulSoupCrawler(max_requests_per_crawl=20)</code></pre>
             <p><a href="/docs/advanced-routing.html">Next: advanced routing</a></p>
           </article>
           """,
       ),
   )
   write_file(
       SITE_DIR / "docs" / "advanced-routing.html",
       layout(
           "Advanced Routing and Storage",
           """
           <article class="doc" data-doc-id="advanced-routing">
             <h2>Queue filtering</h2>
             <p>
               We filter links to keep the crawl focused on the same local domain and skip admin pages.
             </p>
             <h2>Storage design</h2>
             <p>
               Structured rows go to datasets. Binary screenshots and snapshots go to a key-value store.
             </p>
             <pre><code>await context.enqueue_links(include=[Glob("https://example.com/**")])</code></pre>
             <p><a href="/blog/crawling-at-scale.html">Read the scaling article</a></p>
           </article>
           """,
       ),
   )
   write_file(
       SITE_DIR / "blog" / "crawling-at-scale.html",
       layout(
           "Crawling at Scale",
           """
           <article class="blog-post" data-author="demo-team" data-reading-time="7">
             <h2>Scaling crawler jobs without losing reliability</h2>
             <p>
               Production crawlers need controlled concurrency, retry behavior, stable request queues,
               structured exports, and monitoring-ready output.
             </p>
             <p>
               For AI data workflows, we also normalize text, preserve source URLs, create chunks,
               and record extraction provenance.
             </p>
             <span class="tag">queues</span>
             <span class="tag">datasets</span>
             <span class="tag">rag</span>
             <span class="tag">playwright</span>
           </article>
           """,
       ),
   )
   dynamic_items = json.dumps(
       [
           {
               "sku": "JS-900",
               "name": "Dynamic Inventory Scanner",
               "price": 329.0,
               "stock": 4,
               "desc": "Rendered only after JavaScript executes.",
           },
           {
               "sku": "JS-901",
               "name": "Client-Side Review Miner",
               "price": 279.0,
               "stock": 11,
               "desc": "Created by browser-side DOM manipulation.",
           },
           {
               "sku": "JS-902",
               "name": "Async Catalog Watcher",
               "price": 389.0,
               "stock": 7,
               "desc": "Useful for testing PlaywrightCrawler extraction.",
           },
       ],
       indent=2,
   )
   dynamic_script = f"""
   <script>
     const dynamicItems = {dynamic_items};
     function renderItems() {{
       const root = document.querySelector("#dynamic-products");
       root.innerHTML = "";
       for (const item of dynamicItems) {{
         const card = document.createElement("div");
         card.className = "card js-card";
         card.dataset.sku = item.sku;
         card.dataset.price = item.price;
         card.dataset.stock = item.stock;
         card.innerHTML = `
           <h3>${{item.name}}</h3>
           <p class="desc">${{item.desc}}</p>
           <p class="price">$${{item.price.toFixed(2)}}</p>
           <p class="${{item.stock < 8 ? "stock-low" : "stock-ok"}}">Stock: ${{item.stock}}</p>
         `;
         root.appendChild(card);
       }}
       document.querySelector("#render-status").textContent =
         "Rendered " + dynamicItems.length + " JavaScript items.";
     }}
     setTimeout(renderItems, 600);
   </script>
   """
   write_file(
       SITE_DIR / "dynamic.html",
       layout(
           "JavaScript Rendered Catalog",
           """
           <section class="panel">
             <h2>Dynamic content test</h2>
             <p>
               A plain HTTP crawler can download this page, but it will not see the cards below until JavaScript runs.
               PlaywrightCrawler opens a real browser and extracts the rendered DOM.
             </p>
             <p id="render-status">Waiting for JavaScript rendering...</p>
           </section>
           <section id="dynamic-products" class="grid"></section>
           """,
           extra_script=dynamic_script,
       ),
   )
   write_file(
       SITE_DIR / "admin" / "hidden.html",
       layout(
           "Hidden Admin Page",
           """
           <article class="panel">
             <h2>This page should be skipped</h2>
             <p>
               The crawler excludes this admin path to demonstrate control over the rawl scope 
             </p>
           </article>
           """,
       ),
   )
build_demo_site()
print(f"Demo site generated at: {SITE_DIR}")
class QuietHandler(SimpleHTTPRequestHandler):
   def log_message(self, format, *args):
       pass
def start_local_server(directory):
   probe = socket.socket()
   probe.bind(("127.0.0.1", 0))
   port = probe.getsockname()[1]
   probe.close()
   handler = partial(QuietHandler, directory=str(directory))
   httpd = ThreadingHTTPServer(("127.0.0.1", port), handler)
   thread = threading.Thread(target=httpd.serve_forever, daemon=True)
   thread.start()
   base_url = f"http://127.0.0.1:{port}"
   time.sleep(0.5)
   return httpd, base_url
def extract_json_ld(soup):
   blocks = []
   for script in soup.select('script[type="application/ld+json"]'):
       raw = script.string or script.get_text()
       if not raw:
           continue
       try:
           blocks.append(json.loads(raw))
       except Exception:
           blocks.append({"raw": raw})
   return blocks
def write_json(path, rows):
   path = Path(path)
   path.write_text(json.dumps(rows, ensure_ascii=False, indent=2), encoding="utf-8")
def write_csv(path, rows):
   path = Path(path)
   if not rows:
       path.write_text("", encoding="utf-8")
       return
   flattened = []
   for row in rows:
       flat = {}
       for key, value in row.items():
           if isinstance(value, (list, dict)):
               flat[key] = json.dumps(value, ensure_ascii=False)
           else:
               flat[key] = value
       flattened.append(flat)
   fieldnames = sorted({key for row in flattened for key in row.keys()})
   with path.open("w", newline="", encoding="utf-8") as f:
       writer = csv.DictWriter(f, fieldnames=fieldnames)
       writer.writeheader()
       writer.writerows(flattened)

async def run_beautifulsoup_crawl(base_url):
   print("\n=== 1) BeautifulSoupCrawler: fast recursive HTTP crawl ===")
   rows = []
   crawler = BeautifulSoupCrawler(
       parser="html.parser",
       max_requests_per_crawl=30,
       max_request_retries=1,
       respect_robots_txt_file=True,
       concurrency_settings=ConcurrencySettings(
           desired_concurrency=4,
           max_concurrency=6,
       ),
   )
   @crawler.router.default_handler
   async def request_handler(context: BeautifulSoupCrawlingContext) -> None:
       soup = context.soup
       url = context.request.url
       title = normalize_text(soup.title.get_text(" ", strip=True) if soup.title else "")
       meta_description = ""
       meta_tag = soup.find("meta", attrs={"name": "description"})
       if meta_tag:
           meta_description = normalize_text(meta_tag.get("content", ""))
       out_links = []
       for a in soup.select("a[href]"):
           href = a.get("href")
           label = normalize_text(a.get_text(" ", strip=True), 120)
           out_links.append({"href": href, "label": label})
       page_text = normalize_text(soup.get_text(" ", strip=True), 1000)
       if "/products/" in url:
           page_type = "product"
       elif "/docs/" in url:
           page_type = "documentation"
       elif "/blog/" in url:
           page_type = "blog"
       elif "/dynamic" in url:
           page_type = "dynamic-shell"
       else:
           page_type = "index"
       row = {
           "source": "beautifulsoup-http",
           "url": url,
           "title": title,
           "page_type": page_type,
           "meta_description": meta_description,
           "text_preview": page_text,
           "out_links": out_links,
           "json_ld": extract_json_ld(soup),
           "extracted_at_unix": time.time(),
       }
       if page_type == "product":
           article = soup.select_one("article.product")
           if article:
               price_node = soup.select_one(".price")
               row["product"] = {
                   "sku": article.get("data-sku"),
                   "category": article.get("data-category"),
                   "name": normalize_text(
                       soup.select_one(".product-title").get_text(" ", strip=True)
                       if soup.select_one(".product-title")
                       else ""
                   ),
                   "price": money_to_float(price_node.get("data-price") if price_node else None),
                   "rating": float(article.get("data-rating")) if article.get("data-rating") else None,
                   "stock": int(article.get("data-stock")) if article.get("data-stock") else None,
                   "features": [
                       normalize_text(li.get_text(" ", strip=True))
                       for li in soup.select(".features li")
                   ],
               }
       if page_type == "documentation":
           row["doc"] = {
               "headings": [
                   normalize_text(h.get_text(" ", strip=True))
                   for h in soup.select("h2, h3")
               ],
               "code_blocks": [
                   normalize_text(code.get_text(" ", strip=True))
                   for code in soup.select("pre code")
               ],
           }
       if page_type == "blog":
           row["blog"] = {
               "author": soup.select_one(".blog-post").get("data-author") if soup.select_one(".blog-post") else None,
               "reading_time": soup.select_one(".blog-post").get("data-reading-time") if soup.select_one(".blog-post") else None,
               "tags": [
                   normalize_text(tag.get_text(" ", strip=True))
                   for tag in soup.select(".tag")
               ],
           }
       rows.append(row)
       await context.push_data(row)
       await context.enqueue_links(
           include=[Glob(f"{base_url}/**")],
           exclude=[
               Glob(f"{base_url}/admin/**"),
               Glob(f"{base_url}/dynamic.html"),
           ],
       )
   await crawler.run([f"{base_url}/index.html"])
   write_json(OUTPUT_DIR / "beautifulsoup_crawl.json", rows)
   write_csv(OUTPUT_DIR / "beautifulsoup_crawl.csv", rows)
   print(f"BeautifulSoup rows extracted: {len(rows)}")
   return rows
async def run_parsel_precision_crawl(base_url):
   print("\n=== 2) ParselCrawler: precise CSS/XPath extraction from product pages ===")
   rows = []
   product_urls = [
       f"{base_url}/products/product-{safe_slug(product['sku'])}.html"
       for product in PRODUCTS
   ]
   crawler = ParselCrawler(
       max_requests_per_crawl=len(product_urls),
       max_request_retries=1,
       concurrency_settings=ConcurrencySettings(
           desired_concurrency=5,
           max_concurrency=8,
       ),
   )
   @crawler.router.default_handler
   async def request_handler(context: ParselCrawlingContext) -> None:
       selector = context.selector
       title = selector.css("title::text").get()
       sku = selector.css("article.product::attr(data-sku)").get()
       category = selector.css("article.product::attr(data-category)").get()
       rating = selector.css("article.product::attr(data-rating)").get()
       stock = selector.css("article.product::attr(data-stock)").get()
       name = selector.css(".product-title::text").get()
       price = selector.css(".price::attr(data-price)").get()
       features = [
           normalize_text(feature)
           for feature in selector.css(".features li::text").getall()
       ]
       row = {
           "source": "parsel-precision",
           "url": context.request.url,
           "title": normalize_text(title),
           "sku": sku,
           "name": normalize_text(name),
           "category": category,
           "price": money_to_float(price),
           "rating": float(rating) if rating else None,
           "stock": int(stock) if stock else None,
           "features": features,
           "xpath_title": normalize_text(selector.xpath("//title/text()").get()),
       }
       rows.append(row)
       await context.push_data(row)
   await crawler.run(product_urls)
   write_json(OUTPUT_DIR / "parsel_products.json", rows)
   write_csv(OUTPUT_DIR / "parsel_products.csv", rows)
   print(f"Parsel product rows extracted: {len(rows)}")
   return rows

async def run_playwright_dynamic_crawl(base_url):
   print("\n=== 3) PlaywrightCrawler: browser-rendered JavaScript crawl ===")
   rows = []
   crawler = PlaywrightCrawler(
       max_requests_per_crawl=2,
       max_request_retries=1,
       headless=True,
       browser_type="chromium",
       browser_launch_options={
           "args": ["--no-sandbox", "--disable-dev-shm-usage"],
       },
       goto_options={
           "wait_until": "domcontentloaded",
       },
       concurrency_settings=ConcurrencySettings(
           desired_concurrency=1,
           max_concurrency=2,
       ),
   )
   @crawler.router.default_handler
   async def request_handler(context: PlaywrightCrawlingContext) -> None:
       await context.page.wait_for_selector(".js-card", timeout=10000)
       cards = await context.page.locator(".js-card").evaluate_all(
           """
           (cards) => cards.map((card) => {
             const h3 = card.querySelector("h3");
             const desc = card.querySelector(".desc");
             const price = card.querySelector(".price");
             return {
               sku: card.dataset.sku,
               name: h3 ? h3.textContent.trim() : null,
               description: desc ? desc.textContent.trim() : null,
               price_text: price ? price.textContent.trim() : null,
               price: Number(card.dataset.price),
               stock: Number(card.dataset.stock),
               rendered_text: card.innerText.trim()
             };
           })
           """
       )
       screenshot_bytes = await context.page.screenshot(full_page=True)
       screenshot_path = SCREENSHOT_DIR / "dynamic_catalog_full_page.png"
       screenshot_path.write_bytes(screenshot_bytes)
       try:
           kvs = await context.get_key_value_store()
           await kvs.set_value(
               key="dynamic-catalog-full-page",
               value=screenshot_bytes,
               content_type="image/png",
           )
       except Exception as exc:
           print("Key-value store screenshot save skipped:", repr(exc))
       for card in cards:
           row = {
               **card,
               "source": "playwright-rendered-js",
               "url": context.request.url,
               "screenshot_path": str(screenshot_path),
               "extracted_at_unix": time.time(),
           }
           rows.append(row)
       await context.push_data(rows)
   try:
       await crawler.run([f"{base_url}/dynamic.html"])
   except Exception as exc:
       print("Playwright section failed gracefully.")
       print("Reason:", repr(exc))
   write_json(OUTPUT_DIR / "playwright_dynamic.json", rows)
   write_csv(OUTPUT_DIR / "playwright_dynamic.csv", rows)
   print(f"Playwright dynamic rows extracted: {len(rows)}")
   return rows
def flatten_products(rows):
   products = []
   for row in rows:
       if row.get("page_type") == "product" and isinstance(row.get("product"), dict):
           product = row["product"]
           products.append(
               {
                   "source": row.get("source"),
                   "url": row.get("url"),
                   "sku": product.get("sku"),
                   "name": product.get("name"),
                   "category": product.get("category"),
                   "price": product.get("price"),
                   "rating": product.get("rating"),
                   "stock": product.get("stock"),
                   "features": "; ".join(product.get("features", [])),
               }
           )
       elif row.get("source") == "parsel-precision":
           products.append(
               {
                   "source": row.get("source"),
                   "url": row.get("url"),
                   "sku": row.get("sku"),
                   "name": row.get("name"),
                   "category": row.get("category"),
                   "price": row.get("price"),
                   "rating": row.get("rating"),
                   "stock": row.get("stock"),
                   "features": "; ".join(row.get("features", [])),
               }
           )
       elif row.get("source") == "playwright-rendered-js":
           products.append(
               {
                   "source": row.get("source"),
                   "url": row.get("url"),
                   "sku": row.get("sku"),
                   "name": row.get("name"),
                   "category": "dynamic-js",
                   "price": row.get("price") or money_to_float(row.get("price_text")),
                   "rating": None,
                   "stock": row.get("stock"),
                   "features": row.get("description"),
               }
           )
   return products
def absolute_url(base_url, href):
   if not href:
       return None
   if href.startswith("http://") or href.startswith("https://"):
       return href
   if href.startswith("/"):
       return base_url + href
   return base_url + "/" + href
def build_link_graph(base_url, rows):
   graph = nx.DiGraph()
   for row in rows:
       src = row.get("url")
       if not src:
           continue
       graph.add_node(
           src,
           title=row.get("title", ""),
           page_type=row.get("page_type", ""),
       )
       for link in row.get("out_links", []) or []:
           dst = absolute_url(base_url, link.get("href"))
           if not dst:
               continue
           if "/admin/" in dst:
               continue
           graph.add_node(dst)
           graph.add_edge(src, dst, label=link.get("label", ""))
   return graph

def make_rag_chunks(rows, max_chars=700):
   chunks = []
   for row in rows:
       text = (
           row.get("text_preview")
           or row.get("rendered_text")
           or row.get("description")
           or ""
       )
       text = normalize_text(text)
       if not text:
           continue
       sentences = re.split(r"(?<=[.!?])\s+", text)
       current = ""
       for sentence in sentences:
           if len(current) + len(sentence) + 1 <= max_chars:
               current = (current + " " + sentence).strip()
           else:
               if current:
                   chunks.append(
                       {
                           "chunk_id": hashlib.sha1(
                               (row.get("url", "") + current).encode()
                           ).hexdigest()[:12],
                           "url": row.get("url"),
                           "source": row.get("source"),
                           "page_type": row.get("page_type"),
                           "title": row.get("title") or row.get("name"),
                           "text": current,
                       }
                   )
               current = sentence
       if current:
           chunks.append(
               {
                   "chunk_id": hashlib.sha1(
                       (row.get("url", "") + current).encode()
                   ).hexdigest()[:12],
                   "url": row.get("url"),
                   "source": row.get("source"),
                   "page_type": row.get("page_type"),
                   "title": row.get("title") or row.get("name"),
                   "text": current,
               }
           )
   return chunks
def analyze_outputs(base_url, bs4_rows, parsel_rows, playwright_rows):
   all_rows = bs4_rows + parsel_rows + playwright_rows
   products = flatten_products(all_rows)
   crawl_df = pd.DataFrame(all_rows)
   product_df = pd.DataFrame(products)
   if not product_df.empty:
       product_df["price"] = pd.to_numeric(product_df["price"], errors="coerce")
       product_df["stock"] = pd.to_numeric(product_df["stock"], errors="coerce")
       product_df["rating"] = pd.to_numeric(product_df["rating"], errors="coerce")
       product_df["inventory_value"] = product_df["price"] * product_df["stock"]
   graph = build_link_graph(base_url, bs4_rows)
   graph_path = OUTPUT_DIR / "site_link_graph.graphml"
   if graph.number_of_nodes() > 0:
       nx.write_graphml(graph, graph_path)
   chunks = make_rag_chunks(all_rows)
   rag_path = OUTPUT_DIR / "rag_chunks.jsonl"
   with rag_path.open("w", encoding="utf-8") as f:
       for chunk in chunks:
           f.write(json.dumps(chunk, ensure_ascii=False) + "\n")
   crawl_json_path = OUTPUT_DIR / "combined_crawl_results.json"
   crawl_json_path.write_text(
       json.dumps(all_rows, ensure_ascii=False, indent=2),
       encoding="utf-8",
   )
   product_csv_path = OUTPUT_DIR / "normalized_product_catalog.csv"
   if not product_df.empty:
       product_df.to_csv(product_csv_path, index=False)
   price_plot_path = OUTPUT_DIR / "product_price_chart.png"
   if not product_df.empty and product_df["price"].notna().any():
       plot_df = product_df.dropna(subset=["price"]).copy()
       plot_df["label"] = plot_df["sku"].fillna("unknown") + "\n" + plot_df["source"].fillna("")
       ax = plot_df.plot(
           kind="bar",
           x="label",
           y="price",
           legend=False,
           figsize=(11, 5),
           title="Extracted Product Prices by Source",
       )
       ax.set_xlabel("Product / extraction source")
       ax.set_ylabel("Price")
       plt.xticks(rotation=35, ha="right")
       plt.tight_layout()
       plt.savefig(price_plot_path, dpi=160)
       plt.show()
   graph_stats = {
       "nodes": graph.number_of_nodes(),
       "edges": graph.number_of_edges(),
       "weakly_connected_components": (
           nx.number_weakly_connected_components(graph)
           if graph.number_of_nodes()
           else 0
       ),
   }
   if graph.number_of_nodes() > 0:
       in_degrees = dict(graph.in_degree())
       out_degrees = dict(graph.out_degree())
       graph_stats["top_in_degree"] = sorted(
           in_degrees.items(),
           key=lambda x: x[1],
           reverse=True,
       )[:5]
       graph_stats["top_out_degree"] = sorted(
           out_degrees.items(),
           key=lambda x: x[1],
           reverse=True,
       )[:5]
   summary = {
       "base_url": base_url,
       "rows_total": len(all_rows),
       "beautifulsoup_rows": len(bs4_rows),
       "parsel_rows": len(parsel_rows),
       "playwright_rows": len(playwright_rows),
       "products_total": len(product_df),
       "rag_chunks_total": len(chunks),
       "graph": graph_stats,
       "outputs": {
           "beautifulsoup_json": str(OUTPUT_DIR / "beautifulsoup_crawl.json"),
           "beautifulsoup_csv": str(OUTPUT_DIR / "beautifulsoup_crawl.csv"),
           "parsel_json": str(OUTPUT_DIR / "parsel_products.json"),
           "parsel_csv": str(OUTPUT_DIR / "parsel_products.csv"),
           "playwright_json": str(OUTPUT_DIR / "playwright_dynamic.json"),
           "playwright_csv": str(OUTPUT_DIR / "playwright_dynamic.csv"),
           "combined_json": str(crawl_json_path),
           "product_csv": str(product_csv_path) if product_csv_path.exists() else None,
           "rag_jsonl": str(rag_path),
           "graphml": str(graph_path) if graph_path.exists() else None,
           "price_plot": str(price_plot_path) if price_plot_path.exists() else None,
           "screenshots_dir": str(SCREENSHOT_DIR),
       },
   }
   summary_path = OUTPUT_DIR / "run_summary.md"
   summary_path.write_text(
       "# Crawlee Python Advanced Tutorial Run Summary\n\n"
       f"- Local demo site: `{base_url}`\n"
       f"- Total extracted rows: `{summary['rows_total']}`\n"
       f"- BeautifulSoup rows: `{summary['beautifulsoup_rows']}`\n"
       f"- Parsel rows: `{summary['parsel_rows']}`\n"
       f"- Playwright rows: `{summary['playwright_rows']}`\n"
       f"- Normalized products: `{summary['products_total']}`\n"
       f"- RAG chunks: `{summary['rag_chunks_total']}`\n"
       f"- Link graph nodes: `{graph_stats['nodes']}`\n"
       f"- Link graph edges: `{graph_stats['edges']}`\n\n"
       "## Output files\n\n"
       + "\n".join(f"- `{k}`: `{v}`" for k, v in summary["outputs"].items())
       + "\n",
       encoding="utf-8",
   )
   print("\n=== 4) Analysis summary ===")
   print(json.dumps(summary, indent=2, ensure_ascii=False))
   try:
       from IPython.display import display, Markdown, Image as IPImage
       display(Markdown("## Crawlee crawl preview"))
       if not crawl_df.empty:
           preview_cols = [
               col for col in ["source", "page_type", "title", "url"]
               if col in crawl_df.columns
           ]
           display(crawl_df[preview_cols].head(12))
       display(Markdown("## Normalized product catalog"))
       if not product_df.empty:
           display(product_df.head(20))
       if price_plot_path.exists():
           display(Markdown("## Product price chart"))
           display(IPImage(filename=str(price_plot_path)))
       screenshot_path = SCREENSHOT_DIR / "dynamic_catalog_full_page.png"
       if screenshot_path.exists():
           display(Markdown("## Playwright screenshot of JavaScript-rendered page"))
           display(IPImage(filename=str(screenshot_path)))
       display(Markdown(f"## Output directory\n`{OUTPUT_DIR}`"))
   except Exception as exc:
       print("Notebook display skipped:", repr(exc))
   return summary
async def main():
   httpd, base_url = start_local_server(SITE_DIR)
   print(f"\nLocal demo website is running at: {base_url}/index.html")
   try:
       bs4_rows = await run_beautifulsoup_crawl(base_url)
       parsel_rows = await run_parsel_precision_crawl(base_url)
       playwright_rows = await run_playwright_dynamic_crawl(base_url)
       summary = analyze_outputs(base_url, bs4_rows, parsel_rows, playwright_rows)
       return summary
   finally:
       httpd.shutdown()
       print("\nLocal demo server shut down.")
loop = asyncio.get_event_loop()
summary = loop.run_until_complete(main())
print("\nTutorial complete.")
print(f"All outputs are in: {OUTPUT_DIR}")
print("Key files:")
for file_path in sorted(OUTPUT_DIR.rglob("*")):
   if file_path.is_file():
       print(" -", file_path)