feat(scraper): add LLM-powered job data refinement and new scraping logic

- Implement LLMJobRefiner class for processing job data with Gemini API
- Add new job_scraper2.py with enhanced scraping capabilities
- Remove search_keywords parameter from scraping engine
- Add environment variable loading in config.py
- Update main script to use new scraper and target field

config.py

@@ -2,6 +2,16 @@
 import os
 import json
 
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+# LLM Agent Configuration
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+if not GEMINI_API_KEY:
+    raise ValueError("GEMINI_API_KEY environment variable not set in .env file")
+
 
 def load_spoof_config():
     """Load spoof data from JSON config file. Falls back to defaults if missing."""

job_scraper.py

@@ -5,7 +5,7 @@ import random
 import sqlite3
 import os
 from datetime import datetime
-from typing import Optional, Dict
+from typing import Optional, Dict, List
 from playwright.async_api import async_playwright
 from browserforge.injectors.playwright import AsyncNewContext
 
@@ -200,7 +200,7 @@ class LinkedInJobScraper:
 
     async def scrape_jobs(
         self,
-        search_keywords: str,
+        search_keywords: Optional[str],
         max_pages: int = 1,
         credentials: Optional[Dict] = None
     ):
@@ -214,7 +214,7 @@ class LinkedInJobScraper:
 
         async with async_playwright() as pw:
             browser = await pw.chromium.launch(
-                headless=False,
+                headless= False,
                 args=['--disable-blink-features=AutomationControlled']
             )
             context = await AsyncNewContext(browser, fingerprint=profile)
@@ -269,7 +269,7 @@ class LinkedInJobScraper:
                     return
 
             print(f"🔍 Searching for: {search_keywords}")
-            await page.goto(search_url, wait_until='load', timeout=60000)
+            await page.goto(search_url, wait_until='networkidle', timeout=60000)
             await asyncio.sleep(random.uniform(4.0, 6.0) * self.human_speed)
 
             if await self.engine._detect_cloudflare(page):

job_scraper2.py

@@ -0,0 +1,510 @@
+
+import asyncio
+import random
+import sqlite3
+import os
+from datetime import datetime
+from typing import Optional, Dict, List
+from playwright.async_api import async_playwright
+from browserforge.injectors.playwright import AsyncNewContext
+from llm_agent import LLMJobRefiner
+import re
+
+
+class LinkedInJobScraper:
+    def __init__(
+        self,
+        engine,
+        db_path: str = "linkedin_jobs.db",
+        human_speed: float = 1.0,
+        target_field: str = "all"
+    ):
+        self.engine = engine
+        self.db_path = db_path
+        self.human_speed = human_speed
+        self.target_field = target_field
+        self._init_db()
+        self.llm_agent = LLMJobRefiner()
+
+    def _init_db(self):
+        os.makedirs(os.path.dirname(self.db_path) if os.path.dirname(self.db_path) else ".", exist_ok=True)
+        with sqlite3.connect(self.db_path) as conn:
+            cursor = conn.cursor()
+            cursor.execute('''
+                CREATE TABLE IF NOT EXISTS jobs (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    title TEXT,
+                    company_name TEXT,
+                    location TEXT,
+                    description TEXT,
+                    requirements TEXT,
+                    qualifications TEXT,
+                    salary_range TEXT,
+                    nature_of_work TEXT,
+                    job_id TEXT,
+                    url TEXT UNIQUE
+                )
+            ''')
+            conn.commit()
+
+    async def _human_click(self, page, element, wait_after: bool = True):
+        if not element:
+            return False
+        await element.scroll_into_view_if_needed()
+        await asyncio.sleep(random.uniform(0.3, 0.8) * self.human_speed)
+        try:
+            await element.click()
+            if wait_after:
+                await asyncio.sleep(random.uniform(2, 4) * self.human_speed)
+            return True
+        except:
+            return False
+
+    async def _login(self, page, credentials: Dict) -> bool:
+        """Human-realistic LinkedIn login"""
+        print("🔐 Navigating to LinkedIn login page...")
+        await page.goto("https://www.linkedin.com/login", timeout=60000)
+        await asyncio.sleep(random.uniform(2.0, 3.5) * self.human_speed)
+
+        email_field = await page.query_selector('input[name="session_key"]')
+        if not email_field:
+            print("❌ Email field not found.")
+            return False
+        
+        print("✍️ Typing username...")
+        await email_field.click()
+        await asyncio.sleep(random.uniform(0.4, 0.9) * self.human_speed)
+        for char in credentials["email"]:
+            await page.keyboard.type(char)
+            await asyncio.sleep(random.uniform(0.06, 0.14) * self.human_speed)
+        await asyncio.sleep(random.uniform(1.0, 1.8) * self.human_speed)
+
+        password_field = await page.query_selector('input[name="session_password"]')
+        if not password_field:
+            print("❌ Password field not found.")
+            return False
+
+        print("🔒 Typing password...")
+        await password_field.click()
+        await asyncio.sleep(random.uniform(0.3, 0.7) * self.human_speed)
+        for char in credentials["password"]:
+            await page.keyboard.type(char)
+            await asyncio.sleep(random.uniform(0.08, 0.16) * self.human_speed)
+        await asyncio.sleep(random.uniform(0.8, 1.5) * self.human_speed)
+
+        print("✅ Submitting login form...")
+        await page.keyboard.press("Enter")
+        
+        for _ in range(15):
+            current_url = page.url
+            if "/feed" in current_url or "/jobs" in current_url:
+                if "login" not in current_url:
+                    print("✅ Login successful!")
+                    await asyncio.sleep(random.uniform(2.0, 3.0) * self.human_speed)
+                    return True
+            await asyncio.sleep(1)
+        print("❌ Login may have failed.")
+        return False
+
+    async def _extract_all_page_content(self, page) -> str:
+        """Extract all content from the job page"""
+        await asyncio.sleep(2 * self.human_speed)
+        
+        # Human-like scrolling to load all content
+        await self.engine._human_like_scroll(page)
+        await asyncio.sleep(2 * self.human_speed)
+        
+        # Get the full page content
+        page_content = await page.content()
+        return page_content
+
+    def _calculate_keyword_match(self, title: str, keywords: str) -> float:
+        """Calculate percentage of keywords matched in title"""
+        if not title or not keywords:
+            return 0.0
+        
+        title_lower = title.lower()
+        keyword_list = [kw.strip().lower() for kw in keywords.split()]
+        
+        matches = 0
+        for keyword in keyword_list:
+            if keyword in title_lower:
+                matches += 1
+        
+        return matches / len(keyword_list) if keyword_list else 0.0
+
+    def _extract_location_from_keywords(self, search_keywords: str) -> str:
+        """Extract location from search keywords if present"""
+        location_match = re.search(r'location:\s*([^,]+)', search_keywords, re.IGNORECASE)
+        if location_match:
+            return location_match.group(1).strip().lower()
+        return ""
+
+    async def _scrape_jobs_from_current_page(self, page, search_keywords: str, seen_job_ids, all_job_links):
+        """Scrape job links from the current page that match keywords and location"""
+        current_links = await page.query_selector_all("a[href*='/jobs/view/']")
+        new_jobs = 0
+        
+        # Extract location from search keywords
+        location_from_keywords = self._extract_location_from_keywords(search_keywords)
+        
+        for link in current_links:
+            href = await link.get_attribute("href")
+            if href:
+                full_url = href if href.startswith("http") else f"https://www.linkedin.com{href}"
+                job_id = href.split("/view/")[-1].split("/")[0] if "/view/" in href else href
+                
+                if job_id and job_id not in seen_job_ids:
+                    # Check if job title matches keywords (at least 70% match)
+                    title_element = await link.query_selector("span.job-title, h3, .job-card-title")
+                    if title_element:
+                        title = await title_element.inner_text()
+                        match_percentage = self._calculate_keyword_match(title, search_keywords)
+                        
+                        # Check if location matches (if specified in keywords)
+                        location_match = True
+                        if location_from_keywords:
+                            # Try to get location from the job card
+                            location_element = await link.query_selector("span.job-location, .job-card-location, .location")
+                            if location_element:
+                                location_text = await location_element.inner_text()
+                                location_match = location_from_keywords in location_text.lower()
+                        
+                        if match_percentage >= 0.7 and location_match:  # At least 70% match and location matches
+                            seen_job_ids.add(job_id)
+                            all_job_links.append((href, title))
+                            new_jobs += 1
+                        elif match_percentage < 0.7:
+                            print(f"   ⚠️ Skipping job due to low keyword match: {title[:50]}... (match: {match_percentage:.2%})")
+                        elif not location_match:
+                            print(f"   ⚠️ Skipping job due to location mismatch: {title[:50]}... (expected: {location_from_keywords})")
+                    else:
+                        # If no title element, still add to check later
+                        seen_job_ids.add(job_id)
+                        all_job_links.append((href, "Unknown Title"))
+                        new_jobs += 1
+        return new_jobs
+
+    async def _handle_pagination(self, page, search_keywords: str, seen_job_ids, all_job_links):
+        """Handle pagination by going through pages"""
+        current_page = 1
+        while True:
+            print(f"📄 Processing page {current_page}")
+            
+            # Collect job links on current page
+            new_jobs = await self._scrape_jobs_from_current_page(page, search_keywords, seen_job_ids, all_job_links)
+            print(f"   ➕ Found {new_jobs} new job(s) on page {current_page} (total: {len(all_job_links)})")
+
+            # Try to go to next page
+            next_btn = await page.query_selector("button[aria-label='Next']")
+            if next_btn and await next_btn.is_enabled():
+                await self._human_click(page, next_btn)
+                await asyncio.sleep(random.uniform(4.0, 6.0) * self.human_speed)
+                # Wait for URL to change or new content
+                try:
+                    await page.wait_for_function("() => window.location.href.includes('start=')", timeout=30000)
+                except:
+                    pass
+                current_page += 1
+            else:
+                print("🔚 'Next' button not available — stopping pagination.")
+                break
+
+    async def _handle_infinite_scroll(self, page, search_keywords: str, seen_job_ids, all_job_links):
+        """Handle infinite scroll to load more jobs"""
+        last_height = await page.evaluate("document.body.scrollHeight")
+        no_new_jobs_count = 0
+        max_no_new = 3
+
+        while no_new_jobs_count < max_no_new:
+            await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
+            await asyncio.sleep(random.uniform(3.0, 5.0) * self.human_speed)
+
+            new_jobs_found = await self._scrape_jobs_from_current_page(page, search_keywords, seen_job_ids, all_job_links)
+
+            print(f"   ➕ Found {new_jobs_found} new job(s) (total: {len(all_job_links)})")
+
+            new_height = await page.evaluate("document.body.scrollHeight")
+            if new_height == last_height:
+                no_new_jobs_count += 1
+            else:
+                no_new_jobs_count = 0
+                last_height = new_height
+
+            if new_jobs_found == 0 and no_new_jobs_count >= 1:
+                print("🔚 No new jobs loaded. Stopping scroll.")
+                break
+
+    async def scrape_jobs(
+        self,
+        search_keywords: Optional[str],
+        max_pages: int = 1,
+        credentials: Optional[Dict] = None
+    ):
+        # Parse location from keywords if present
+        location_match = re.search(r'location:\s*([^,]+)', search_keywords, re.IGNORECASE)
+        location = location_match.group(1).strip() if location_match else ""
+        
+        # Remove location part from keywords for search
+        clean_keywords = re.sub(r'location:\s*[^,]+', '', search_keywords, flags=re.IGNORECASE).strip()
+        encoded_keywords = clean_keywords.replace(" ", "%20")
+        
+        # Build search URL with location if specified
+        search_url = f"https://www.linkedin.com/jobs/search/?keywords={encoded_keywords}"
+        if location:
+            search_url += f"&location={location.replace(' ', '%20')}"
+        
+        profile = self.engine._select_profile()
+        renderer = random.choice(self.engine.common_renderers[self.engine.os])
+        vendor = random.choice(self.engine.common_vendors)
+        spoof_script = self.engine._get_spoof_script(renderer, vendor)
+
+        async with async_playwright() as pw:
+            browser = await pw.chromium.launch(
+                headless= False,
+                args=['--disable-blink-features=AutomationControlled']
+            )
+            context = await AsyncNewContext(browser, fingerprint=profile)
+            
+            await context.add_init_script(f"""
+                Object.defineProperty(navigator, 'hardwareConcurrency', {{ get: () => {profile.navigator.hardwareConcurrency} }});
+                Object.defineProperty(navigator, 'deviceMemory', {{ get: () => {profile.navigator.deviceMemory} }});
+                Object.defineProperty(navigator, 'platform', {{ get: () => '{profile.navigator.platform}' }});
+            """)
+            await context.add_init_script(spoof_script)
+
+            page = await context.new_page()
+
+            session_loaded = await self.engine.load_session(context)
+            login_successful = False
+
+            if session_loaded:
+                print("🔁 Using saved session — verifying login...")
+                await page.goto("https://www.linkedin.com/feed/", timeout=60000)
+                if "feed" in page.url and "login" not in page.url:
+                    print("✅ Session still valid.")
+                    login_successful = True
+                else:
+                    print("⚠️ Saved session expired — re-authenticating.")
+                    session_loaded = False
+
+            if not session_loaded and credentials:
+                print("🔐 Performing fresh login...")
+                login_successful = await self._login(page, credentials)
+                if login_successful:
+                    await self.engine.save_session(context)
+                else:
+                    print("❌ Login failed. Exiting.")
+                    await browser.close()
+                    self.engine.report_outcome("block")
+                    return
+            elif not credentials:
+                print("ℹ️ No credentials — proceeding as guest.")
+                login_successful = True
+            else:
+                pass
+
+            await page.wait_for_load_state("load", timeout=60000)
+            print("✅ Post-login page fully loaded. Starting search...")
+
+            if await self.engine._detect_cloudflare(page):
+                print("☁️ Cloudflare detected on initial load.")
+                if not await self.engine._handle_cloudflare(page):
+                    print("❌ Cloudflare could not be resolved.")
+                    await browser.close()
+                    self.engine.report_outcome("cloudflare")
+                    return
+
+            print(f"🔍 Searching for: {search_keywords}")
+            await page.goto(search_url, wait_until='load', timeout=60000)
+            await asyncio.sleep(random.uniform(4.0, 6.0) * self.human_speed)
+
+            if await self.engine._detect_cloudflare(page):
+                print("☁️ Cloudflare detected on search page.")
+                if not await self.engine._handle_cloudflare(page):
+                    await browser.close()
+                    self.engine.report_outcome("cloudflare")
+                    return
+
+            all_job_links = []
+            seen_job_ids = set()
+
+            # First, scrape the initial page
+            print("🔄 Collecting initial job links...")
+            initial_jobs = await self._scrape_jobs_from_current_page(page, search_keywords, seen_job_ids, all_job_links)
+            print(f"   ➕ Found {initial_jobs} initial job(s) (total: {len(all_job_links)})")
+
+            # Loop until no new jobs are found
+            iteration = 1
+            while True:
+                print(f"🔄 Iteration {iteration}: Checking for new jobs...")
+                
+                # First try infinite scroll
+                prev_job_count = len(all_job_links)
+                await self._handle_infinite_scroll(page, search_keywords, seen_job_ids, all_job_links)
+                new_jobs_count = len(all_job_links) - prev_job_count
+                
+                if new_jobs_count > 0:
+                    print(f"   ➕ Found {new_jobs_count} new jobs via infinite scroll")
+                    iteration += 1
+                    continue  # Continue with infinite scroll if new jobs found
+                
+                # If no new jobs via scroll, check for pagination
+                pagination_exists = await page.query_selector("button[aria-label='Next']")
+                
+                if pagination_exists:
+                    print("⏭️ Pagination detected. Processing pages...")
+                    await self._handle_pagination(page, search_keywords, seen_job_ids, all_job_links)
+                    iteration += 1
+                    continue  # Continue with pagination if new jobs found
+                else:
+                    # If no pagination and no new jobs from scroll, check by refreshing
+                    print("🔄 Refreshing page to check for new results...")
+                    await page.reload(wait_until='networkidle')
+                    await asyncio.sleep(random.uniform(3.0, 5.0) * self.human_speed)
+                    
+                    # Check for new jobs after refresh
+                    new_jobs_after_refresh = await self._scrape_jobs_from_current_page(page, search_keywords, seen_job_ids, all_job_links)
+                    if new_jobs_after_refresh > 0:
+                        print(f"   ➕ Found {new_jobs_after_refresh} new job(s) after refresh")
+                        iteration += 1
+                        continue  # Continue if new jobs found after refresh
+                    else:
+                        print("🔚 No new jobs found after refresh. Stopping.")
+                        break
+                
+                # Limit iterations to prevent infinite loops
+                if iteration > 10:
+                    print("🔄 Maximum iterations reached. Stopping.")
+                    break
+
+            print(f"✅ Collected {len(all_job_links)} unique job links.")
+
+            # Process all collected job links
+            scraped_count = 0
+            for idx, (href, title) in enumerate(all_job_links):
+                try:
+                    full_url = href if href.startswith("http") else f"https://www.linkedin.com{href}"
+                    print(f"  → Opening job {idx+1}/{len(all_job_links)}: {full_url}")
+                    await page.goto(full_url, wait_until='load', timeout=60000)
+                    await asyncio.sleep(3 * self.human_speed)
+
+                    is_cloudflare = await self.engine._detect_cloudflare(page)
+                    page_content = await page.content()
+                    has_captcha_text = "captcha" in page_content.lower()
+                    captcha_present = is_cloudflare or has_captcha_text
+
+                    title_element = await page.query_selector("h1.t-24")
+                    job_data_accessible = title_element is not None
+
+                    if captcha_present:
+                        if job_data_accessible:
+                            print("  ⚠️ CAPTCHA detected, but job data is accessible. Proceeding in stealth mode...")
+                            await self.engine._avoid_captcha(page)
+                        else:
+                            print("  ⚠️ CAPTCHA detected and job data blocked. Attempting recovery...")
+                            if not await self.engine._solve_captcha_fallback(page):
+                                print("  ❌ CAPTCHA recovery failed. Skipping job.")
+                                continue
+                            title_element = await page.query_selector("h1.t-24")
+                            if not title_element:
+                                print("  ❌ Job data still unavailable after CAPTCHA handling. Skipping.")
+                                continue
+
+                    if not captcha_present:
+                        await self.engine._avoid_captcha(page)
+
+                    apply_btn = None
+                    apply_selectors = [
+                        "button[aria-label*='Apply']",
+                        "button:has-text('Apply')",
+                        "a:has-text('Apply')",
+                        "button:has-text('Easy Apply')"
+                    ]
+                    for selector in apply_selectors:
+                        apply_btn = await page.query_selector(selector)
+                        if apply_btn:
+                            break
+
+                    page_data = None
+                    final_url = full_url
+
+                    if apply_btn:
+                        print("  → Clicking 'Apply' / 'Easy Apply' button...")
+                        
+                        page_waiter = asyncio.create_task(context.wait_for_event("page"))
+                        await self._human_click(page, apply_btn, wait_after=False)
+                        
+                        external_page = None
+                        try:
+                            external_page = await asyncio.wait_for(page_waiter, timeout=5.0)
+                            print("  🌐 External job site opened in new tab.")
+                            await external_page.wait_for_load_state("load", timeout=30000)
+                            await asyncio.sleep(2 * self.human_speed)
+                            await self.engine._human_like_scroll(external_page)
+                            await asyncio.sleep(2 * self.human_speed)
+                            
+                            page_data = await self._extract_all_page_content(external_page)
+                            final_url = external_page.url
+                            
+                            if not external_page.is_closed():
+                                await external_page.close()
+                                
+                        except asyncio.TimeoutError:
+                            print("  🖥️ No external tab — scraping LinkedIn job page.")
+                            await page.wait_for_timeout(2000)
+                            try:
+                                await page.wait_for_selector("div.jobs-apply-button--fixed, div.jobs-easy-apply-modal", timeout=8000)
+                            except:
+                                pass
+                            await self.engine._human_like_scroll(page)
+                            await asyncio.sleep(2 * self.human_speed)
+                            page_data = await self._extract_all_page_content(page)
+                            final_url = page.url
+                    else:
+                        print("    ⚠️ No 'Apply' button found — scraping job details directly.")
+                        await self.engine._human_like_scroll(page)
+                        await asyncio.sleep(2 * self.human_speed)
+                        page_data = await self._extract_all_page_content(page)
+                        final_url = page.url
+
+                    # Extract job ID from URL
+                    job_id = final_url.split("/")[-2] if "/jobs/view/" in final_url else "unknown"
+                    
+                    # Prepare raw data for LLM processing
+                    raw_data = {
+                        "page_content": page_data,
+                        "url": final_url,
+                        "job_id": job_id
+                    }
+                    
+                    # Send raw data to LLM agent for refinement
+                    refined_data = await self.llm_agent.refine_job_data(raw_data, search_keywords)
+                    
+                    # Only save if LLM successfully extracted meaningful data
+                    if refined_data and refined_data.get("title", "N/A") != "N/A":
+                        # Save refined data to markdown and database through LLM agent
+                        await self.llm_agent.save_job_data(refined_data, search_keywords)
+                        
+                        scraped_count += 1
+                        print(f"  ✅ Scraped and refined: {refined_data['title'][:50]}...")
+                    else:
+                        print(f"  🟡 Could not extract meaningful data from: {final_url}")
+
+                except Exception as e:
+                    print(f"    ⚠️ Failed on job {idx+1}: {str(e)[:100]}")
+                    continue
+                
+                finally:
+                    print("  ↩️ Returning to LinkedIn search results...")
+                    await page.goto(search_url, timeout=60000)
+                    await asyncio.sleep(4 * self.human_speed)
+
+            await browser.close()
+            
+            if scraped_count > 0:
+                self.engine.report_outcome("success")
+                print(f"✅ Completed! Processed {scraped_count} jobs for '{search_keywords}'.")
+            else:
+                self.engine.report_outcome("captcha")
+                print("⚠️ No jobs processed successfully.")

linkedin_main.py

@@ -1,23 +1,26 @@
+
 from scraping_engine import FingerprintScrapingEngine
-from job_scraper import LinkedInJobScraper
+from job_scraper2 import LinkedInJobScraper
 import os
+from dotenv import load_dotenv
 import asyncio
 
+# Load environment variables
+load_dotenv()
 
 async def main():
     engine = FingerprintScrapingEngine(
         seed="job_scraping_engine",
         target_os="windows",
         db_path="job_listings.db",
-        markdown_path="job_listings.md",
+        markdown_path="job_listings.md"
-        search_keywords="Data Anaylst" 
     )
 
-    scraper = LinkedInJobScraper(engine, human_speed=1.6)
+    # Initialize scraper with target field
+    scraper = LinkedInJobScraper(engine, human_speed=1.6, target_field="Web designer")
 
     await scraper.scrape_jobs(
-        search_keywords="Data Anaylst",  # ← Your search terms
+        search_keywords="Web Designer location:New York",
-        max_pages=3,
         credentials={
             "email": os.getenv("SCRAPING_USERNAME"),
             "password": os.getenv("SCRAPING_PASSWORD")

llm_agent.py

@@ -0,0 +1,166 @@
+import google.generativeai as genai
+from typing import Dict, Any
+import asyncio
+import sqlite3
+import os
+from datetime import datetime
+from config import GEMINI_API_KEY
+
+class LLMJobRefiner:
+    def __init__(self):
+        genai.configure(api_key=GEMINI_API_KEY)
+        self.model = genai.GenerativeModel('gemini-pro')
+    
+    async def refine_job_data(self, raw_data: Dict[str, Any], target_field: str) -> Dict[str, Any]:
+        """
+        Refine raw job data using Gemini LLM based on target field
+        """
+        prompt = f"""
+        You are a job data extraction assistant. Extract the following fields from the job posting:
+        - title
+        - company_name
+        - location
+        - description
+        - requirements
+        - qualifications
+        - salary_range
+        - nature_of_work (remote, onsite, or hybrid)
+        - job_id
+
+        Target Field: {target_field}
+        Raw Page Content:
+        {raw_data.get('page_content', '')[:3000]}  # Limit content size
+
+        Instructions:
+        1. Extract only the information relevant to the target field: {target_field}
+        2. Clean up any formatting issues in the description
+        3. Standardize location format (city, state/country)
+        4. Extract salary range if mentioned in description
+        5. Determine nature of work (remote, onsite, or hybrid) from work arrangements
+        6. Ensure all fields are properly formatted
+        7. If a field cannot be found, use "N/A"
+        8. Return the refined data in JSON format
+
+        Response format (only return the JSON):
+        {{
+            "title": "...",
+            "company_name": "...",
+            "location": "...",
+            "description": "...",
+            "requirements": "...",
+            "qualifications": "...",
+            "salary_range": "...",
+            "nature_of_work": "...",
+            "job_id": "{raw_data.get('job_id', 'unknown')}",
+            "url": "{raw_data.get('url', 'N/A')}"
+        }}
+        """
+        
+        try:
+            response = await asyncio.get_event_loop().run_in_executor(
+                None, 
+                lambda: self.model.generate_content(prompt)
+            )
+            
+            # Parse the response and return refined data
+            refined_data = self._parse_llm_response(response.text)
+            
+            # If parsing fails, return None
+            if not refined_data:
+                return None
+            
+            return refined_data
+            
+        except Exception as e:
+            print(f"LLM refinement failed: {str(e)}")
+            return None
+    
+    def _parse_llm_response(self, response_text: str) -> Dict[str, Any]:
+        """
+        Parse the LLM response to extract refined job data
+        """
+        import json
+        import re
+        
+        # Extract JSON from response (handle markdown code blocks)
+        json_match = re.search(r'```(?:json)?\s*({.*?})\s*```', response_text, re.DOTALL)
+        if json_match:
+            json_str = json_match.group(1)
+        else:
+            # If no code block, try to find JSON directly
+            json_match = re.search(r'\{.*\}', response_text, re.DOTALL)
+            if json_match:
+                json_str = json_match.group(0)
+            else:
+                return None
+        
+        try:
+            return json.loads(json_str)
+        except json.JSONDecodeError:
+            return None
+    
+    async def save_job_data(self, job_data: Dict[str, Any], keyword: str):
+        """
+        Save job data to both markdown and database
+        """
+        # Save to database
+        await self._save_to_db(job_data)
+        
+        # Save to markdown
+        await self._save_to_markdown(job_data, keyword)
+    
+    async def _save_to_db(self, job_data: Dict[str, Any]):
+        """
+        Save job data to database
+        """
+        db_path = "linkedin_jobs.db"
+        os.makedirs(os.path.dirname(db_path) if os.path.dirname(db_path) else ".", exist_ok=True)
+        
+        with sqlite3.connect(db_path) as conn:
+            cursor = conn.cursor()
+            cursor.execute('''
+                INSERT OR IGNORE INTO jobs 
+                (title, company_name, location, description, requirements, 
+                 qualifications, salary_range, nature_of_work, job_id, url)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+            ''', (
+                job_data.get("title", "N/A"),
+                job_data.get("company_name", "N/A"),
+                job_data.get("location", "N/A"),
+                job_data.get("description", "N/A"),
+                job_data.get("requirements", "N/A"),
+                job_data.get("qualifications", "N/A"),
+                job_data.get("salary_range", "N/A"),
+                job_data.get("nature_of_work", "N/A"),
+                job_data.get("job_id", "N/A"),
+                job_data.get("url", "N/A")
+            ))
+            conn.commit()
+    
+    async def _save_to_markdown(self, job_data: Dict[str, Any], keyword: str):
+        """
+        Save job data to markdown file
+        """
+        os.makedirs("linkedin_jobs", exist_ok=True)
+        
+        # Create a single markdown file for all jobs
+        filename = "linkedin_jobs_scraped.md"
+        filepath = os.path.join("linkedin_jobs", filename)
+        
+        with open(filepath, "a", encoding="utf-8") as f:
+            # Only write header if file is empty
+            if os.path.getsize(filepath) == 0:
+                f.write(f"# LinkedIn Jobs - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n")
+            
+            f.write(f"## Job: {job_data.get('title', 'N/A')}\n\n")
+            f.write(f"- **Keyword**: {keyword}\n")
+            f.write(f"- **Company**: {job_data.get('company_name', 'N/A')}\n")
+            f.write(f"- **Location**: {job_data.get('location', 'N/A')}\n")
+            f.write(f"- **Nature of Work**: {job_data.get('nature_of_work', 'N/A')}\n")
+            f.write(f"- **Salary Range**: {job_data.get('salary_range', 'N/A')}\n")
+            f.write(f"- **Job ID**: {job_data.get('job_id', 'N/A')}\n")
+            f.write(f"- **URL**: <{job_data.get('url', 'N/A')}>\n\n")
+            f.write(f"### Description\n\n{job_data.get('description', 'N/A')}\n\n")
+            f.write(f"### Requirements\n\n{job_data.get('requirements', 'N/A')}\n\n")
+            f.write(f"### Qualifications\n\n{job_data.get('qualifications', 'N/A')}\n\n")
+            f.write("---\n\n")

scraping_engine.py

@@ -24,8 +24,7 @@ class FingerprintScrapingEngine:
         db_path: str = "jobs.db",
         markdown_path: str = "scraped_jobs.md",
         proxies: List[str] = None,
-        login_credentials: Optional[Dict[str, str]] = None,
+        login_credentials: Optional[Dict[str, str]] = None
-        search_keywords: Optional[str] = None
     ):
         if target_os not in ['windows', 'macos']:
             raise ValueError("operating_system must be 'windows' or 'macos'")
@@ -42,7 +41,6 @@ class FingerprintScrapingEngine:
         self.markdown_path = markdown_path
         self.proxies = proxies or []
         self.login_credentials = login_credentials
-        self.search_keywords = search_keywords
         self.fingerprint_generator = FingerprintGenerator(
             browser=('chrome',),
             os=(self.os,)