feat: implement search query generation and initial scraping structure for backlink opportunities

lib/ai_marketing_tools/ai_backlinking.py

@@ -127,3 +127,82 @@
 #    Database: MongoDB or PostgreSQL to track leads, emails, and responses.
 #
 #This solution will significantly streamline the backlinking process by automating the most tedious tasks, from finding sites to personalizing outreach, enabling marketers to focus on content creation and high-level strategies.
+from lib.ai_web_researcher.firecrawl_web_crawler import scrape_website
+
+def generate_search_queries(keyword):
+    """
+    Generate a list of search queries for finding guest post opportunities.
+    
+    Args:
+        keyword (str): The keyword to base the search queries on.
+    
+    Returns:
+        list: A list of search queries.
+    """
+    search_queries = [
+        f"{keyword} + 'Guest Contributor'",
+        f"{keyword} + 'Add Guest Post'",
+        f"{keyword} + 'Guest Bloggers Wanted'",
+        f"{keyword} + 'Guest Posts Roundup'",
+        f"{keyword} + 'Write for Us'",
+        f"{keyword} + 'Submit Guest Post'",
+        f"{keyword} + 'Submit a Guest Article'",
+        f"{keyword} + 'Guest Bloggers Wanted'",
+        f"{keyword} + 'Submit an article'",
+        f"{keyword} + 'Suggest a guest post'",
+        f"{keyword} + 'Send a guest post'",
+        f"{keyword} + 'Become a Guest Blogger'",
+        f"{keyword} + 'guest post opportunities'",
+        f"{keyword} + 'this is a guest post by'",
+        f"{keyword} + 'This post was written by'",
+        f"{keyword} + 'guest post courtesy of'",
+        f"{keyword} + 'submit article'"
+    ]
+    return search_queries
+
+def find_backlink_opportunities(keyword):
+    """
+    Find backlink opportunities by scraping websites based on search queries.
+    
+    Args:
+        keyword (str): The keyword to search for backlink opportunities.
+    
+    Returns:
+        list: A list of results from the scraped websites.
+    """
+    search_queries = generate_search_queries(keyword)
+    results = []
+    for query in search_queries:
+        # Placeholder for a function to search and get URLs
+        urls = search_for_urls(query)
+        for url in urls:
+            result = scrape_website(url)
+            if result:
+                results.append(result)
+    return results
+
+def search_for_urls(query):
+    """
+    Placeholder function to search for URLs based on a query.
+    
+    Args:
+        query (str): The search query.
+    
+    Returns:
+        list: A list of URLs.
+    """
+    # This function needs to be implemented
+    return []
+
+def extract_contact_info(scraped_data):
+    """
+    Placeholder function to extract contact information from scraped data.
+    
+    Args:
+        scraped_data (dict): The data scraped from a website.
+    
+    Returns:
+        dict: Extracted contact information.
+    """
+    # This function needs to be implemented
+    return {}