import requests
import json
import datetime as dt
import time
from collections import defaultdict, deque


class RateLimiter:
    """
    Simple rate limiter to prevent excessive API calls.
    Tracks requests per domain and enforces delays.
    """
    
    def __init__(self):
        self.request_times = defaultdict(deque)  # domain -> deque of timestamps
        self.domain_limits = {
            'reddit.com': {'requests': 60, 'window': 60},  # 60 requests per minute
            'api.stackexchange.com': {'requests': 300, 'window': 86400},  # 300 per day
            'hacker-news.firebaseio.com': {'requests': 300, 'window': 60},  # 300 per minute
            'lobste.rs': {'requests': 30, 'window': 60},  # 30 per minute
            'default': {'requests': 60, 'window': 60}  # Default rate limit
        }
    
    def wait_if_needed(self, url: str):
        """
        Check rate limit and wait if necessary before making request.
        
        Args:
            url: The URL being requested
        """
        from urllib.parse import urlparse
        
        domain = urlparse(url).netloc
        current_time = time.time()
        
        # Get rate limit for this domain
        limit_config = self.domain_limits.get(domain, self.domain_limits['default'])
        max_requests = limit_config['requests']
        time_window = limit_config['window']
        
        # Get request times for this domain
        times = self.request_times[domain]
        
        # Remove requests outside the time window
        cutoff_time = current_time - time_window
        while times and times[0] < cutoff_time:
            times.popleft()
        
        # Check if we're at the rate limit
        if len(times) >= max_requests:
            # Calculate how long to wait
            oldest_request = times[0]
            wait_time = time_window - (current_time - oldest_request)
            
            if wait_time > 0:
                import logging
                logger = logging.getLogger(__name__)
                logger.info(f"Rate limit reached for {domain}. Waiting {wait_time:.1f}s")
                time.sleep(wait_time)
        
        # Record this request
        times.append(current_time)


# Global rate limiter instance
_rate_limiter = RateLimiter()


#a collection of static methods to grab reddit and reddit like data from various sources
class data_methods():
    @staticmethod
    def getData(platform, start_date, end_date, community, max_posts):
        if platform == "reddit":
            return data_methods.fetchers.getRedditData(start_date, end_date, community, max_posts)
        elif  platform == "pushshift":
            return data_methods.fetchers.getPushshiftData(start_date, end_date, community, max_posts)
        elif platform == "hackernews":
            return data_methods.fetchers.getHackerNewsData(start_date, end_date, community, max_posts)
        elif platform == "lobsters":
            return data_methods.fetchers.getLobstersData(start_date, end_date, community, max_posts)
        elif platform == "stackexchange":
            return data_methods.fetchers.getStackExchangeData(start_date, end_date, community, max_posts)
        elif platform == "rss":
            return data_methods.fetchers.getRSSData(start_date, end_date, community, max_posts)
        else:
            print("dataGrab.getData: platform not recognized")
            return None

    # ===== ATOMIC UTILITY FUNCTIONS =====
    class utils():
        """Generic utility functions used across all fetchers"""

        @staticmethod
        def http_get_json(url, headers=None, params=None, timeout=30, max_retries=3):
            """
            Generic HTTP GET request that returns JSON with comprehensive error handling.
            
            Args:
                url: Target URL
                headers: HTTP headers
                params: Query parameters
                timeout: Request timeout in seconds
                max_retries: Maximum number of retry attempts
                
            Returns:
                JSON response data
                
            Raises:
                requests.RequestException: On persistent failure after retries
            """
            import time
            import logging
            
            logger = logging.getLogger(__name__)
            
            for attempt in range(max_retries + 1):
                try:
                    # Add retry delay for subsequent attempts
                    if attempt > 0:
                        delay = min(2 ** attempt, 30)  # Exponential backoff, max 30s
                        logger.info(f"Retrying request to {url} in {delay}s (attempt {attempt + 1}/{max_retries + 1})")
                        time.sleep(delay)
                    
                    # Apply rate limiting before making the request
                    _rate_limiter.wait_if_needed(url)
                    
                    response = requests.get(url, headers=headers, params=params, timeout=timeout)
                    
                    # Handle different HTTP status codes
                    if response.status_code == 429:  # Rate limited
                        retry_after = int(response.headers.get('Retry-After', 60))
                        if attempt < max_retries:
                            logger.warning(f"Rate limited. Waiting {retry_after}s before retry")
                            time.sleep(retry_after)
                            continue
                    
                    response.raise_for_status()
                    
                    # Validate JSON response
                    try:
                        json_data = response.json()
                        return json_data
                    except ValueError as e:
                        logger.error(f"Invalid JSON response from {url}: {e}")
                        if attempt < max_retries:
                            continue
                        raise requests.RequestException(f"Invalid JSON response: {e}")
                        
                except requests.exceptions.Timeout:
                    logger.warning(f"Request timeout for {url} (attempt {attempt + 1})")
                    if attempt == max_retries:
                        raise
                        
                except requests.exceptions.ConnectionError:
                    logger.warning(f"Connection error for {url} (attempt {attempt + 1})")
                    if attempt == max_retries:
                        raise
                        
                except requests.exceptions.HTTPError as e:
                    # Don't retry on client errors (4xx) except rate limiting
                    if 400 <= e.response.status_code < 500 and e.response.status_code != 429:
                        logger.error(f"Client error {e.response.status_code} for {url}: {e}")
                        raise
                    logger.warning(f"HTTP error {e.response.status_code} for {url} (attempt {attempt + 1})")
                    if attempt == max_retries:
                        raise
                        
                except Exception as e:
                    logger.error(f"Unexpected error for {url}: {e}")
                    if attempt == max_retries:
                        raise
            
            raise requests.RequestException(f"Failed to fetch {url} after {max_retries + 1} attempts")

        @staticmethod
        def filter_by_date_range(posts, start_date, end_date):
            """Filter posts by timestamp range"""
            start_ts = int(dt.datetime.fromisoformat(start_date).timestamp())
            end_ts = int(dt.datetime.fromisoformat(end_date).timestamp())
            return [p for p in posts if p and start_ts <= p['timestamp'] <= end_ts]

        @staticmethod
        def convert_iso_to_timestamp(iso_string):
            """Convert ISO format datetime string to Unix timestamp"""
            return int(dt.datetime.fromisoformat(iso_string.replace('Z', '+00:00')).timestamp())

    # ===== URL AND PARAMETER BUILDERS =====
    class builders():
        """Functions to build URLs, headers, and parameters"""

        @staticmethod
        def build_reddit_url(subreddit):
            return f"https://www.reddit.com/r/{subreddit}/new.json"

        @staticmethod
        def build_reddit_headers():
            return {'User-Agent': 'Mozilla/5.0 (compatible; DataCollector/1.0)'}

        @staticmethod
        def build_reddit_params(limit):
            return {'limit': limit}

        @staticmethod
        def build_reddit_search_url(subreddit, start_date, end_date):
            """Build Reddit search URL for time-based queries"""
            return f"https://www.reddit.com/r/{subreddit}/search.json"

        @staticmethod
        def build_reddit_search_params(limit, start_date, end_date):
            """Build search parameters for Reddit API with time constraints"""
            import datetime
            
            # Convert date strings to timestamps for Reddit API
            try:
                start_ts = int(datetime.datetime.fromisoformat(start_date.replace('Z', '+00:00')).timestamp())
                end_ts = int(datetime.datetime.fromisoformat(end_date.replace('Z', '+00:00')).timestamp())
                
                # Use Reddit's search syntax for time-based queries
                # Reddit search uses 'after:' and 'before:' with timestamps
                query = f"after:{start_ts} before:{end_ts}"
                
                return {
                    'q': query,
                    'sort': 'new',
                    'restrict_sr': 'true',  # Restrict to subreddit
                    'limit': limit,
                    't': 'all'  # Time period: all
                }
            except (ValueError, TypeError):
                # Fallback to simple search without time constraints
                return {
                    'q': '*',  # Match all posts
                    'sort': 'new',
                    'restrict_sr': 'true',
                    'limit': limit,
                    't': 'week'  # Default to past week
                }

        @staticmethod
        def build_hackernews_top_stories_url():
            return "https://hacker-news.firebaseio.com/v0/topstories.json"

        @staticmethod
        def build_hackernews_story_url(story_id):
            return f"https://hacker-news.firebaseio.com/v0/item/{story_id}.json"

        @staticmethod
        def build_lobsters_url():
            return "https://lobste.rs/hottest.json"

        @staticmethod
        def build_stackexchange_url():
            return f"https://api.stackexchange.com/2.3/questions"

        @staticmethod
        def build_stackexchange_params(site, limit, start_date, end_date):
            start_ts = int(dt.datetime.fromisoformat(start_date).timestamp())
            end_ts = int(dt.datetime.fromisoformat(end_date).timestamp())
            return {
                'site': site,
                'pagesize': limit,
                'fromdate': start_ts,
                'todate': end_ts,
                'sort': 'votes',
                'order': 'desc'
            }

        @staticmethod
        def build_rss_url(feed_url):
            """RSS feeds use the URL directly as provided in config"""
            return feed_url

    # ===== SCHEMA CONVERTERS =====
    class converters():
        """Functions to convert platform-specific data to unified schema"""

        @staticmethod
        def reddit_to_schema(child):
            post = child['data']
            return {
                'platform': 'reddit',
                'id': post.get('id'),
                'title': post.get('title'),
                'author': post.get('author'),
                'timestamp': int(post.get('created_utc', 0)),
                'score': post.get('score', 0),
                'replies': post.get('num_comments', 0),
                'url': post.get('url'),
                'content': post.get('selftext', ''),
                'source': post.get('subreddit'),
                'tags': [post.get('link_flair_text', '')],
                'meta': {'is_self': post.get('is_self', False)}
            }

        # In data_methods.converters.hackernews_to_schema()

        @staticmethod
        def hackernews_to_schema(raw, community='front_page'): # Add community parameter
            if not raw or raw.get('type') != 'story':
                return None
            return {
                'platform': 'hackernews',
                'id': f"hn_{raw.get('id')}",
                'title': raw.get('title'),
                'author': raw.get('by', 'unknown'),
                'timestamp': int(raw.get('time', 0)),
                'score': raw.get('score', 0),
                'replies': raw.get('descendants', 0),
                'url': raw.get('url', f"https://news.ycombinator.com/item?id={raw.get('id')}"),
                'content': raw.get('text', ''),
                # ====================================================================
                # FIX: Use the community parameter for the source
                # ====================================================================
                'source': community,
                # ====================================================================
                'tags': ['hackernews'],
                'meta': {}
            }

        @staticmethod
        def lobsters_to_schema(raw):
            submitter = raw.get('submitter_user', 'unknown')
            author = submitter.get('username', 'unknown') if isinstance(submitter, dict) else submitter
            return {
                'platform': 'lobsters',
                'id': f"lob_{raw.get('short_id')}",
                'title': raw.get('title'),
                'author': author,
                'timestamp': data_methods.utils.convert_iso_to_timestamp(raw.get('created_at')),
                'score': raw.get('score', 0),
                'replies': raw.get('comment_count', 0),
                'url': raw.get('url', raw.get('comments_url')),
                'content': raw.get('description', ''),
                'source': 'lobsters',
                'tags': raw.get('tags', []),
                'meta': {}
            }

        @staticmethod
        def stackexchange_to_schema(raw, community):
            return {
                'platform': 'stackexchange',
                'id': f"se_{raw.get('question_id')}",
                'title': raw.get('title'),
                'author': raw.get('owner', {}).get('display_name', 'unknown'),
                'timestamp': int(raw.get('creation_date', 0)),
                'score': raw.get('score', 0),
                'replies': raw.get('answer_count', 0),
                'url': raw.get('link'),
                'content': '',
                'source': community,
                'tags': raw.get('tags', []),
                'meta': {'view_count': raw.get('view_count', 0)}
            }

        @staticmethod
        def rss_to_schema(entry, feed_url):
            """
            Convert RSS/Atom feed entry to unified schema.
            Supports both RSS 2.0 and Atom formats via feedparser.
            """
            import hashlib
            from html import unescape

            # Extract link (RSS uses 'link', Atom may use 'links')
            link = entry.get('link', '')
            if not link and 'links' in entry and len(entry['links']) > 0:
                link = entry['links'][0].get('href', '')

            # Generate ID from link or guid
            entry_id = entry.get('id', entry.get('guid', link))
            if not entry_id:
                # Fallback: hash of title + link
                entry_id = hashlib.md5(f"{entry.get('title', '')}{link}".encode()).hexdigest()

            # Clean up ID to make it URL-safe
            safe_id = entry_id.replace('/', '_').replace(':', '_')[:100]

            # Extract timestamp
            timestamp = 0
            if 'published_parsed' in entry and entry['published_parsed']:
                import time
                timestamp = int(time.mktime(entry['published_parsed']))
            elif 'updated_parsed' in entry and entry['updated_parsed']:
                import time
                timestamp = int(time.mktime(entry['updated_parsed']))

            # Extract author
            author = 'unknown'
            if 'author' in entry:
                author = entry['author']
            elif 'author_detail' in entry:
                author = entry['author_detail'].get('name', 'unknown')

            # Extract content (try summary, then description, then content)
            content = ''
            if 'summary' in entry:
                content = unescape(entry['summary'])
            elif 'description' in entry:
                content = unescape(entry['description'])
            elif 'content' in entry and len(entry['content']) > 0:
                content = unescape(entry['content'][0].get('value', ''))

            # Strip HTML tags for cleaner content
            import re
            content = re.sub(r'<[^>]+>', '', content)

            # Extract tags/categories
            tags = []
            if 'tags' in entry:
                tags = [tag.get('term', '') for tag in entry['tags']]

            return {
                'platform': 'rss',
                'id': f"rss_{safe_id}",
                'title': unescape(entry.get('title', 'Untitled')),
                'author': author,
                'timestamp': timestamp,
                'score': 0,  # RSS doesn't have scores
                'replies': 0,  # RSS doesn't track comments
                'url': link,
                'content': content[:1000],  # Limit content length
                'source': feed_url,
                'tags': tags,
                'meta': {
                    'feed_url': feed_url,
                    'guid': entry.get('guid', '')
                }
            }

    # ===== COMMENT FETCHERS =====
    class comment_fetchers():
        """Functions to fetch comments for posts from various platforms"""

        @staticmethod
        def fetch_reddit_comments(post_id, subreddit, max_comments=50):
            """
            Fetch comments for a Reddit post.
            Note: Reddit JSON API has limited comment support without auth.
            Returns list of comment dicts with parent relationships.
            """
            # Reddit comment API: /r/{subreddit}/comments/{post_id}.json
            url = f"https://www.reddit.com/r/{subreddit}/comments/{post_id}.json"
            headers = {'User-Agent': 'Mozilla/5.0 (compatible; DataCollector/1.0)'}

            try:
                raw = data_methods.utils.http_get_json(url, headers=headers)

                # Reddit returns [post_data, comments_data]
                if len(raw) < 2:
                    return []

                comments_data = raw[1]['data']['children']
                comments = []

                def extract_comment(comment_obj, parent_id=None, depth=0):
                    if comment_obj['kind'] != 't1':  # t1 = comment
                        return

                    data = comment_obj['data']
                    comments.append({
                        'id': data.get('id'),
                        'parent_comment_id': parent_id,
                        'author': data.get('author', '[deleted]'),
                        'content': data.get('body', ''),
                        'timestamp': int(data.get('created_utc', 0)),
                        'score': data.get('score', 0),
                        'depth': depth
                    })

                    # Process replies
                    if 'replies' in data and isinstance(data['replies'], dict):
                        for reply in data['replies']['data']['children']:
                            extract_comment(reply, data.get('id'), depth + 1)

                # Extract all comments
                for comment_obj in comments_data:
                    extract_comment(comment_obj, None, 0)

                return comments[:max_comments]

            except Exception as e:
                print(f"Error fetching Reddit comments: {e}")
                return []

        @staticmethod
        def fetch_hackernews_comments(story_id, max_comments=50):
            """
            Fetch comments for a HackerNews story.
            HN provides comment IDs in the 'kids' field.
            """
            comments = []

            def fetch_comment_recursive(comment_id, parent_id=None, depth=0):
                if len(comments) >= max_comments:
                    return

                url = f"https://hacker-news.firebaseio.com/v0/item/{comment_id}.json"
                try:
                    raw = data_methods.utils.http_get_json(url)

                    if not raw or raw.get('deleted') or raw.get('dead'):
                        return

                    comments.append({
                        'id': str(raw.get('id')),
                        'parent_comment_id': parent_id,
                        'author': raw.get('by', 'unknown'),
                        'content': raw.get('text', ''),
                        'timestamp': int(raw.get('time', 0)),
                        'score': 0,  # HN doesn't provide comment scores via API
                        'depth': depth
                    })

                    # Fetch child comments
                    if 'kids' in raw:
                        for kid_id in raw['kids'][:5]:  # Limit children
                            fetch_comment_recursive(kid_id, str(raw.get('id')), depth + 1)

                except Exception as e:
                    print(f"Error fetching HN comment {comment_id}: {e}")

            # Start with top-level comment IDs from story
            try:
                story_url = f"https://hacker-news.firebaseio.com/v0/item/{story_id}.json"
                story = data_methods.utils.http_get_json(story_url)

                if 'kids' in story:
                    for kid_id in story['kids'][:10]:  # Limit top-level comments
                        fetch_comment_recursive(kid_id, None, 0)

            except Exception as e:
                print(f"Error fetching HN story for comments: {e}")

            return comments

        @staticmethod
        def fetch_lobsters_comments(story_id):
            """
            Lobsters provides comments in the story JSON.
            """
            # Lobsters API doesn't easily provide comment trees
            # Would need to parse HTML or use authenticated API
            return []

        @staticmethod
        def fetch_stackexchange_comments(question_id, site='stackoverflow'):
            """
            Fetch comments for a StackExchange question and its answers.
            Uses the public StackExchange API v2.3.
            """
            import datetime
            comments = []
            
            try:
                # First, get question comments
                question_comments_url = f"https://api.stackexchange.com/2.3/questions/{question_id}/comments"
                params = {
                    'site': site,
                    'filter': 'default',  # Includes basic comment data
                    'page': 1,
                    'pagesize': 100
                }
                
                response = data_methods.utils.http_get_json(question_comments_url, params=params)
                if response and 'items' in response:
                    for comment in response['items']:
                        comments.append({
                            'uuid': f"se_{site}_{comment['comment_id']}",
                            'platform': 'stackexchange',
                            'source': site,
                            'content': comment.get('body', ''),
                            'author': comment.get('owner', {}).get('display_name', 'Anonymous'),
                            'timestamp': datetime.datetime.fromtimestamp(
                                comment.get('creation_date', 0)
                            ).isoformat() + 'Z',
                            'score': comment.get('score', 0),
                            'parent_post_id': str(question_id),
                            'parent_comment_uuid': None,  # Top-level comment
                            'depth': 0,
                            'se_comment_id': comment['comment_id'],
                            'se_post_id': comment.get('post_id'),
                            'se_post_type': comment.get('post_type', 'question')
                        })

                # Then get answer IDs for this question
                answers_url = f"https://api.stackexchange.com/2.3/questions/{question_id}/answers"
                answers_params = {
                    'site': site,
                    'filter': 'default',
                    'page': 1,
                    'pagesize': 50
                }
                
                answers_response = data_methods.utils.http_get_json(answers_url, params=answers_params)
                if answers_response and 'items' in answers_response:
                    # Get comments for each answer
                    for answer in answers_response['items']:
                        answer_id = answer['answer_id']
                        answer_comments_url = f"https://api.stackexchange.com/2.3/answers/{answer_id}/comments"
                        
                        answer_comments_response = data_methods.utils.http_get_json(answer_comments_url, params=params)
                        if answer_comments_response and 'items' in answer_comments_response:
                            for comment in answer_comments_response['items']:
                                comments.append({
                                    'uuid': f"se_{site}_{comment['comment_id']}",
                                    'platform': 'stackexchange',
                                    'source': site,
                                    'content': comment.get('body', ''),
                                    'author': comment.get('owner', {}).get('display_name', 'Anonymous'),
                                    'timestamp': datetime.datetime.fromtimestamp(
                                        comment.get('creation_date', 0)
                                    ).isoformat() + 'Z',
                                    'score': comment.get('score', 0),
                                    'parent_post_id': str(answer_id),
                                    'parent_comment_uuid': None,  # SE comments are flat
                                    'depth': 0,
                                    'se_comment_id': comment['comment_id'],
                                    'se_post_id': comment.get('post_id'),
                                    'se_post_type': comment.get('post_type', 'answer')
                                })

                return comments[:100]  # Limit total comments
                
            except Exception as e:
                print(f"Error fetching StackExchange comments for {question_id} on {site}: {e}")
                return []

    # ===== PLATFORM FETCHERS (ORCHESTRATION) =====
    class fetchers():
        """Orchestration functions that compose atomic functions"""

        @staticmethod
        def getRedditData(start_date, end_date, community, max_posts):
            # Build request components
            url = data_methods.builders.build_reddit_url(community)
            headers = data_methods.builders.build_reddit_headers()
            params = data_methods.builders.build_reddit_params(max_posts)

            # Fetch and extract
            raw = data_methods.utils.http_get_json(url, headers, params)
            children = raw['data']['children']

            # Convert and filter
            posts = [data_methods.converters.reddit_to_schema(c) for c in children]
            return data_methods.utils.filter_by_date_range(posts, start_date, end_date)

        @staticmethod
        def getPushshiftData(start_date, end_date, community, max_posts):
            """
            Alternative Reddit data collection using official Reddit API.
            Since Pushshift is deprecated, we use Reddit's native search/listing endpoints.
            """
            try:
                # Use Reddit's native search for historical posts within date range
                # Build search URL for the specific subreddit and time range
                url = data_methods.builders.build_reddit_search_url(community, start_date, end_date)
                headers = data_methods.builders.build_reddit_headers()
                params = data_methods.builders.build_reddit_search_params(max_posts, start_date, end_date)
                
                # Fetch data from Reddit search
                raw = data_methods.utils.http_get_json(url, headers, params)
                
                if not raw or 'data' not in raw or 'children' not in raw['data']:
                    return []
                
                children = raw['data']['children']
                
                # Convert and filter by date range
                posts = [data_methods.converters.reddit_to_schema(c) for c in children]
                return data_methods.utils.filter_by_date_range(posts, start_date, end_date)
                
            except Exception as e:
                print(f"Error fetching Reddit search data: {e}")
                return []

        @staticmethod
        def getHackerNewsData(start_date, end_date, community, max_posts):
            # Fetch story IDs
            ids_url = data_methods.builders.build_hackernews_top_stories_url()
            ids = data_methods.utils.http_get_json(ids_url)[:max_posts]

            # Fetch individual stories
            stories = []
            for story_id in ids:
                story_url = data_methods.builders.build_hackernews_story_url(story_id)
                stories.append(data_methods.utils.http_get_json(story_url))

            # Convert and filter
            posts = [data_methods.converters.hackernews_to_schema(s, community) for s in stories]
            return data_methods.utils.filter_by_date_range(posts, start_date, end_date)

        @staticmethod
        def getLobstersData(start_date, end_date, community, max_posts):
            # Fetch posts
            url = data_methods.builders.build_lobsters_url()
            raw = data_methods.utils.http_get_json(url)[:max_posts]

            # Convert and filter
            posts = [data_methods.converters.lobsters_to_schema(r) for r in raw]
            return data_methods.utils.filter_by_date_range(posts, start_date, end_date)

        @staticmethod
        def getStackExchangeData(start_date, end_date, community, max_posts):
            # Build request components
            url = data_methods.builders.build_stackexchange_url()
            params = data_methods.builders.build_stackexchange_params(community, max_posts, start_date, end_date)

            # Fetch and convert
            raw = data_methods.utils.http_get_json(url, params=params)
            return [data_methods.converters.stackexchange_to_schema(q, community) for q in raw.get('items', [])]

        @staticmethod
        def getRSSData(start_date, end_date, feed_url, max_posts):
            """
            Fetch and parse RSS/Atom feeds.
            Requires feedparser library: pip install feedparser
            """
            try:
                import feedparser
            except ImportError:
                print("Error: feedparser not installed. Run: pip install feedparser")
                return []

            try:
                # Fetch and parse RSS feed
                feed = feedparser.parse(feed_url)

                # Check for errors
                if hasattr(feed, 'bozo') and feed.bozo:
                    print(f"Warning: RSS feed may have issues: {feed.get('bozo_exception', 'Unknown error')}")

                # Extract entries
                entries = feed.get('entries', [])[:max_posts]

                if not entries:
                    print(f"No entries found in RSS feed: {feed_url}")
                    return []

                # Convert entries to unified schema
                posts = [data_methods.converters.rss_to_schema(entry, feed_url) for entry in entries]

                # Filter by date range
                return data_methods.utils.filter_by_date_range(posts, start_date, end_date)

            except Exception as e:
                print(f"Error fetching RSS feed {feed_url}: {e}")
                import traceback
                traceback.print_exc()
                return []