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import asyncio
from playwright.async_api import async_playwright
from openai import OpenAI
import logging
import random
import time
import os
from prometheus_client import start_http_server, Counter, Histogram
from diskcache import Cache
from dotenv import load_dotenv
load_dotenv()
# Setting up Prometheus metrics
SCRAPE_ATTEMPTS = Counter('scrape_attempts', 'Total scraping attempts')
SCRAPE_DURATION = Histogram(
'scrape_duration', 'Scraping duration distribution')
# Setting up cache
cache = Cache('./scraper_cache')
class ScrapingError(Exception):
pass
class ContentAnalysisError(Exception):
pass
class EnhancedOpenAIScraper:
API_KEY = os.getenv("OPENAI_API_KEY")
BROWSER_EXECUTABLE = os.getenv(
"BROWSER_PATH", "/usr/bin/chromium-browser")
MAX_CONTENT_LENGTH = int(os.getenv("MAX_CONTENT_LENGTH", 30000))
def __init__(self, headless=True):
self.user_agents = [
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
]
self.timeout = 45000 # 45 seconds
self.retry_count = int(os.getenv("RETRY_COUNT", 2))
self.headless = headless
self.mouse_velocity_range = (100, 500) # px/ms
self.interaction_delays = {
'scroll': (int(os.getenv("SCROLL_DELAY_MIN", 500)), int(os.getenv("SCROLL_DELAY_MAX", 2000))),
'click': (int(os.getenv("CLICK_DELAY_MIN", 100)), int(os.getenv("CLICK_DELAY_MAX", 300))),
'movement': (int(os.getenv("MOVEMENT_DELAY_MIN", 50)), int(os.getenv("MOVEMENT_DELAY_MAX", 200)))
}
self.proxy_servers = [server.strip() for server in os.getenv(
"PROXY_SERVERS", "").split(',') if server.strip()]
async def human_interaction(self, page):
"""Advanced simulation of user behavior"""
# Random mouse movement path
for _ in range(random.randint(2, 5)):
x = random.randint(0, 1366)
y = random.randint(0, 768)
await page.mouse.move(x, y, steps=random.randint(5, 20))
await page.wait_for_timeout(random.randint(*self.interaction_delays['movement']))
# Simulating typing
if random.random() < 0.3:
await page.keyboard.press('Tab')
await page.keyboard.type(' ', delay=random.randint(50, 200))
# More realistic scrolling
scroll_distance = random.choice([300, 600, 900])
await page.mouse.wheel(0, scroll_distance)
await page.wait_for_timeout(random.randint(*self.interaction_delays['scroll']))
async def load_page(self, page, url):
"""Smarter page loading with dynamic waiting"""
start_time = time.time()
try:
await page.goto(url, wait_until="domcontentloaded", timeout=self.timeout)
# Smarter content extraction selectors
selectors = [
'main article',
'#main-content',
'section:first-of-type',
'div[class*="content"]',
'body' # Fallback
]
for selector in selectors:
try:
element = await page.query_selector(selector)
if element:
return True
except Exception:
continue
# Fallback if no selector is found within a certain time
if time.time() - start_time < 30: # If we haven't used the full timeout
await page.wait_for_timeout(30000 - int(time.time() - start_time))
return True # Page likely loaded
except Exception as e:
logging.error(f"Error loading page {url}: {e}")
return False
@SCRAPE_DURATION.time()
async def scrape_with_retry(self):
"""Main function with retry mechanism and browser reuse"""
SCRAPE_ATTEMPTS.inc()
last_error = None
browser = None
context = None
page = None
try:
async with async_playwright() as p:
launch_args = {
"headless": self.headless,
"args": [
"--disable-blink-features=AutomationControlled",
"--single-process",
"--no-sandbox",
f"--user-agent={random.choice(self.user_agents)}"
],
"executable_path": self.BROWSER_EXECUTABLE
}
if self.proxy_servers:
proxy_url = random.choice(self.proxy_servers)
proxy_config = {"server": proxy_url}
proxy_username = os.getenv('PROXY_USER')
proxy_password = os.getenv('PROXY_PASS')
if proxy_username and proxy_password:
proxy_config['username'] = proxy_username
proxy_config['password'] = proxy_password
launch_args['proxy'] = proxy_config
browser = await p.chromium.launch(**launch_args)
context = await browser.new_context(
user_agent=random.choice(self.user_agents),
viewport={"width": 1366, "height": 768},
locale=os.getenv("BROWSER_LOCALE", "en-US")
)
await context.route("**/*", lambda route: route.continue_())
page = await context.new_page()
await page.add_init_script("""
Object.defineProperty(navigator, 'webdriver', { get: () => false });
window.navigator.chrome = { runtime: {}, app: { isInstalled: false } };
""")
for attempt in range(self.retry_count):
try:
logging.info(
f"Attempt {attempt + 1}: Loading OpenAI...")
if not await self.load_page(page, "https://openai.com"):
raise ScrapingError(
"Failed to load key content on OpenAI website.")
await self.human_interaction(page)
await page.screenshot(path=f"openai_debug_{attempt}.png")
content = await page.evaluate("""() => {
const selectors = [
'main article',
'#main-content',
'section:first-of-type',
'div[class*="content"]'
];
let content = '';
for (const selector of selectors) {
const element = document.querySelector(selector);
if (element) {
content += element.innerText + '\\n\\n';
}
}
return content.trim() || document.body.innerText;
}""")
if not content.strip():
raise ContentAnalysisError(
"No content extracted from the page.")
return content[:self.MAX_CONTENT_LENGTH]
except (ScrapingError, ContentAnalysisError) as e:
last_error = e
logging.warning(
f"Attempt {attempt + 1} failed: {str(e)}")
if attempt < self.retry_count - 1:
await asyncio.sleep(5)
else:
if browser:
await browser.close()
browser = None
raise
except Exception as e:
last_error = e
logging.exception(f"Unexpected error on attempt {
attempt + 1}: {str(e)}")
if attempt < self.retry_count - 1:
await asyncio.sleep(5)
else:
if browser:
await browser.close()
browser = None
raise
except Exception as e:
last_error = e
finally:
if browser:
await browser.close()
raise last_error if last_error else Exception(
"All scraping attempts failed.")
async def get_cached_content(self):
key = 'openai_content_cache_key'
content = cache.get(key)
if content is None:
content = await self.scrape_with_retry()
cache.set(key, content, expire=int(
os.getenv("CACHE_EXPIRY", 3600)))
return content
async def analyze_content(headless=True):
try:
scraper = EnhancedOpenAIScraper(headless=headless)
content = await scraper.get_cached_content()
client = OpenAI(api_key=EnhancedOpenAIScraper.API_KEY)
if not client.api_key:
raise ContentAnalysisError(
"OpenAI API key not configured (check environment variables).")
prompt_template = """
Analyze the following website content and extract the following information if present:
1. **Overall Summary of the Website:** Provide a concise overview of the website's purpose and the main topics discussed.
2. **Key Individuals or Entities:** Identify and briefly describe any prominent individuals, companies, or organizations mentioned.
3. **Recent Announcements or Updates:** List any recent announcements, news, or updates found on the website, including dates if available.
4. **Main Topics or Themes:** Identify the primary subjects or themes explored on the website.
5. **Any Noteworthy Features or Projects:** Highlight any significant features, projects, or initiatives mentioned.
Format the output clearly under each of these headings. If a particular piece of information is not found, indicate that it is not present.
Content:
{content}
"""
formatted_prompt = prompt_template.format(content=content)
model_name = os.getenv("OPENAI_MODEL", "gpt-4-turbo")
temperature = float(os.getenv("MODEL_TEMPERATURE", 0.3))
max_tokens = int(os.getenv("MAX_TOKENS", 1500))
top_p = float(os.getenv("MODEL_TOP_P", 0.9))
response = client.chat.completions.create(
model=model_name,
messages=[
{"role": "system", "content": "You are a helpful assistant that analyzes website content and extracts key information in a structured format."},
{"role": "user", "content": formatted_prompt}
],
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p
)
if not response.choices:
raise ContentAnalysisError("Empty response from GPT.")
return response.choices[0].message.content
except (ScrapingError, ContentAnalysisError) as e:
logging.error(f"Analysis failed: {str(e)}")
return f"Critical analysis error: {str(e)}"
except Exception as e:
logging.exception("Unexpected error during analysis.")
return f"Unexpected analysis error: {str(e)}"
async def main():
logging.basicConfig(
level=os.getenv("LOG_LEVEL", "INFO").upper(),
format='%(asctime)s - %(levelname)s - %(message)s'
)
# Start Prometheus HTTP server for exposing metrics
try:
prometheus_port = int(os.getenv("PROMETHEUS_PORT", 8000))
start_http_server(prometheus_port)
logging.info(f"Prometheus metrics server started on port {
prometheus_port}")
except Exception as e:
logging.warning(f"Failed to start Prometheus metrics server: {e}")
start_time = time.time()
result = await analyze_content(headless=True)
end_time = time.time()
print(f"\nAnalysis completed in {end_time - start_time:.2f} seconds\n")
print(result)
if __name__ == "__main__":
asyncio.run(main())