feat: Add scheduled item tracking service with dedicated web scraping and data extraction capabilities.

app/services/scheduler_service.py

@@ -107,22 +107,33 @@ async def process_item_check(item_id: int):
         # Use independent ScraperService for tracking
         from app.services.tracking_scraper_service import ScraperService
         
-        screenshot_path, html_content, final_url = await ScraperService.scrape_item(
+        screenshot_path, html_content, final_url, page_title = await ScraperService.scrape_item(
             url=item_data["url"],
             selector=item_data["selector"],
             item_id=item_id,
             return_html=True
         )
 
-        # Detect Amazon login wall/redirection
-        if "amazon" in item_data["url"] and ("signin" in final_url or "captcha" in final_url):
-            logger.warning(f"Amazon Bot Detection triggered for item {item_id}. URL redirected to: {final_url}")
-            await loop.run_in_executor(None, _update_db_error, item_id, "Amazon Bot Detection: Redirected to login/captcha")
-            return
-
         if not screenshot_path:
             raise Exception("Failed to capture screenshot")
 
+        # Detect Amazon login wall/redirection/block
+        is_amazon = "amazon" in item_data["url"]
+        is_blocked = False
+        
+        if is_amazon:
+            login_terms = ["signin", "captcha", "s'identifier", "log in", "login"]
+            title_lower = page_title.lower()
+            if any(term in final_url.lower() for term in login_terms) or \
+               any(term in title_lower for term in login_terms) or \
+               "amazon.fr: s'identifier" in title_lower:
+                is_blocked = True
+        
+        if is_blocked:
+            logger.warning(f"Amazon Bot Detection triggered for item {item_id}. Title: {page_title}, URL: {final_url}")
+            await loop.run_in_executor(None, _update_db_error, item_id, f"Amazon Bot Detection: {page_title}")
+            return
+
         # HYBRID EXTRACTION STRATEGY (Aligned with ImprovedSearchService)
         # 1. Try specialized parser (if available) or JSON-LD
         price = None
@@ -193,8 +204,10 @@ async def process_item_check(item_id: int):
             )
         else:
             # Fallback to Vision AI if text extraction failed
-            # Use limited text context for Vision prompt to avoid token bloat
+            # Clean text properly before sending to AI
-            if not (ai_result := await AIService.analyze_image(screenshot_path, page_text=html_content[:5000])):
+            from app.utils.text import clean_text
+            cleaned_html = clean_text(html_content)
+            if not (ai_result := await AIService.analyze_image(screenshot_path, page_text=cleaned_html[:10000])):
                 raise Exception("AI analysis (Vision) failed")
             extraction, metadata = ai_result
 

app/services/tracking_scraper_service.py

@@ -130,10 +130,10 @@ class ScraperService:
         item_id: int | None = None,
         config: ScrapeConfig | None = None,
         return_html: bool = False,
-    ) -> tuple[str | None, str, str]:
+    ) -> tuple[str | None, str, str, str]:
         """
         Scrapes the given URL using Browserless and Playwright.
-        Returns a tuple: (screenshot_path, page_text_or_html, final_url)
+        Returns a tuple: (screenshot_path, page_text_or_html, final_url, page_title)
         """
         if config is None:
             config = ScrapeConfig()
@@ -145,16 +145,27 @@ class ScraperService:
         # Ensure browser is connected and healthy
         if not await ScraperService._ensure_browser_connected():
             logger.error("Failed to establish browser connection")
-            return None, "", url
+            return None, "", url, ""
 
         try:
             context = await ScraperService._create_context(ScraperService._browser, url)
             page = await context.new_page()
 
             try:
+                # Random delay to simulate human lead-in
+                import random
+                await asyncio.sleep(random.uniform(0.5, 2.0))
+                
                 await ScraperService._navigate_and_wait(page, url, timeout)
                 final_url = page.url
+                page_title = await page.title()
                 
+                # Humanize: scroll a bit and back
+                await page.mouse.move(random.randint(100, 500), random.randint(100, 500))
+                await page.evaluate("window.scrollBy(0, 100)")
+                await asyncio.sleep(0.5)
+                await page.evaluate("window.scrollBy(0, -100)")
+
                 await ScraperService._handle_popups(page)
 
                 if selector:
@@ -172,19 +183,20 @@ class ScraperService:
                 
                 screenshot_path = await ScraperService._take_screenshot(page, url, item_id)
 
-                return screenshot_path, content_data, final_url
+                return screenshot_path, content_data, final_url, page_title
 
             finally:
                 await context.close()
 
         except Exception as e:
             logger.error(f"Error scraping {url}: {e}")
-            return None, "", url
+            return None, "", url, ""
 
     @staticmethod
     async def _connect_browser(p) -> Browser:
         logger.info(f"Connecting to Browserless at {BROWSERLESS_URL}")
-        return await p.chromium.connect_over_cdp(BROWSERLESS_URL)
+        # Note: Added timeout for connection
+        return await p.chromium.connect_over_cdp(BROWSERLESS_URL, timeout=30000)
 
     @staticmethod
     async def _create_context(browser: Browser, url: str) -> BrowserContext:

app/utils/text.py

@@ -8,21 +8,24 @@ SNIPPET_CONTEXT_WINDOW = 100
 
 def clean_text(text: str) -> str:
     """
-    Cleans the text by removing code blocks, HTML tags, and excessive whitespace.
+    Cleans the text by removing script/style content, code blocks, HTML tags, and excessive whitespace.
     """
     if not text:
         return ""
 
-    # Remove code blocks (```...```)
+    # 1. Remove script and style elements entirely (including content)
+    text = re.sub(r'<(script|style|header|footer|nav)[\s\S]*?>[\s\S]*?<\/\1>', '', text, flags=re.IGNORECASE)
+
+    # 2. Remove other HTML tags (basic) but keep content
+    text = re.sub(r"<[^>]+>", " ", text)
+
+    # 3. Remove code blocks (```...```)
     text = re.sub(r"```.*?```", "", text, flags=re.DOTALL)
 
-    # Remove HTML tags (basic)
+    # 4. Remove non-printable characters (keep newlines and tabs)
-    text = re.sub(r"<[^>]+>", "", text)
-
-    # Remove non-printable characters (keep newlines and tabs)
     text = re.sub(r"[^\x20-\x7E\n\t]", "", text)
 
-    # Collapse excessive whitespace
+    # 5. Collapse excessive whitespace
     text = re.sub(r"\s+", " ", text).strip()
 
     return text