Save local changes (GSC/Bing integrations) before merging PR #354
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ajaysi
@@ -20,12 +20,13 @@ class BaseAnalyticsHandler(ABC):
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self.platform_name = platform_type.value
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@abstractmethod
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async def get_analytics(self, user_id: str) -> AnalyticsData:
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async def get_analytics(self, user_id: str, **kwargs) -> AnalyticsData:
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"""
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Get analytics data for the platform
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Args:
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user_id: User ID to get analytics for
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**kwargs: Additional arguments for specific handlers
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Returns:
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AnalyticsData object with platform metrics
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@@ -42,7 +42,7 @@ class BingAnalyticsHandler(BaseAnalyticsHandler):
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db_url = f'sqlite:///{db_path}'
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return BingInsightsService(db_url)
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async def get_analytics(self, user_id: str) -> AnalyticsData:
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async def get_analytics(self, user_id: str, target_url: str = None, **kwargs) -> AnalyticsData:
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"""
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Get Bing Webmaster analytics data using Bing Webmaster API
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"""
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@@ -83,9 +83,32 @@ class BingAnalyticsHandler(BaseAnalyticsHandler):
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if not access_token:
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return self.create_error_response('Bing Webmaster access token not available')
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# Select site: Prefer target_url match, otherwise first site
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selected_site = sites[0] if sites else None
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if not selected_site:
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return self.create_error_response('No Bing sites found')
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if target_url and sites:
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logger.info(f"Attempting to match target URL: {target_url}")
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# Normalize target URL (remove protocol, trailing slash)
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normalized_target = target_url.replace('https://', '').replace('http://', '').rstrip('/')
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for site in sites:
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# Bing uses 'Url' key
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site_url = site.get('Url', '')
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normalized_site = site_url.replace('https://', '').replace('http://', '').rstrip('/')
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if normalized_target in normalized_site or normalized_site in normalized_target:
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selected_site = site
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logger.info(f"Found matching Bing site: {site_url}")
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break
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site_url_for_storage = selected_site.get('Url', '') if selected_site else ''
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logger.info(f"Using Bing site URL: {site_url_for_storage}")
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query_stats = {}
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try:
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site_url_for_storage = sites[0].get('Url', '') if (sites and isinstance(sites[0], dict)) else None
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stored = storage_service.get_analytics_summary(user_id, site_url_for_storage, days=30)
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if stored and isinstance(stored, dict):
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query_stats = {
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@@ -99,7 +122,7 @@ class BingAnalyticsHandler(BaseAnalyticsHandler):
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logger.warning(f"Bing analytics: Failed to read stored analytics summary: {e}")
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# Get enhanced insights
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insights = self._get_enhanced_insights_with_service(insights_service, user_id, sites[0].get('Url', '') if sites else '')
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insights = self._get_enhanced_insights_with_service(insights_service, user_id, site_url_for_storage)
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metrics = {
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'connection_status': 'connected',
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@@ -22,16 +22,22 @@ class GSCAnalyticsHandler(BaseAnalyticsHandler):
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super().__init__(PlatformType.GSC)
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self.gsc_service = GSCService()
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async def get_analytics(self, user_id: str) -> AnalyticsData:
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async def get_analytics(self, user_id: str, target_url: str = None, **kwargs) -> AnalyticsData:
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"""
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Get Google Search Console analytics data with caching
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Args:
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user_id: User ID to get analytics for
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target_url: Optional URL to prefer when selecting GSC site
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Returns comprehensive SEO metrics including clicks, impressions, CTR, and position data.
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"""
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self.log_analytics_request(user_id, "get_analytics")
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# Check cache first - GSC API calls can be expensive
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cached_data = analytics_cache.get('gsc_analytics', user_id)
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# Include target_url in cache key if provided
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cache_key = f"{user_id}_{target_url}" if target_url else user_id
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cached_data = analytics_cache.get('gsc_analytics', cache_key)
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if cached_data:
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logger.info("Using cached GSC analytics for user {user_id}", user_id=user_id)
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return AnalyticsData(**cached_data)
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@@ -45,8 +51,23 @@ class GSCAnalyticsHandler(BaseAnalyticsHandler):
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logger.warning(f"No GSC sites found for user {user_id}")
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return self.create_error_response('No GSC sites found')
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# Get analytics for the first site (or combine all sites)
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site_url = sites[0]['siteUrl']
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# Select site: Prefer target_url match, otherwise first site
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selected_site = sites[0]
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if target_url:
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logger.info(f"Attempting to match target URL: {target_url}")
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# Normalize target URL (remove protocol, trailing slash)
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normalized_target = target_url.replace('https://', '').replace('http://', '').rstrip('/')
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for site in sites:
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site_url = site['siteUrl']
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normalized_site = site_url.replace('https://', '').replace('http://', '').rstrip('/')
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if normalized_target in normalized_site or normalized_site in normalized_target:
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selected_site = site
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logger.info(f"Found matching GSC site: {site_url}")
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break
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site_url = selected_site['siteUrl']
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logger.info(f"Using GSC site URL: {site_url}")
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# Get search analytics for last 30 days
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@@ -71,7 +92,7 @@ class GSCAnalyticsHandler(BaseAnalyticsHandler):
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)
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# Cache the result to avoid expensive API calls
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analytics_cache.set('gsc_analytics', user_id, result.__dict__)
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analytics_cache.set('gsc_analytics', cache_key, result.__dict__)
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logger.info("Cached GSC analytics data for user {user_id}", user_id=user_id)
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return result
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@@ -81,7 +102,7 @@ class GSCAnalyticsHandler(BaseAnalyticsHandler):
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error_result = self.create_error_response(str(e))
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# Cache error result for shorter time to retry sooner
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analytics_cache.set('gsc_analytics', user_id, error_result.__dict__, ttl_override=300) # 5 minutes
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analytics_cache.set('gsc_analytics', cache_key, error_result.__dict__, ttl_override=300) # 5 minutes
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return error_result
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def get_connection_status(self, user_id: str) -> Dict[str, Any]:
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@@ -117,111 +138,93 @@ class GSCAnalyticsHandler(BaseAnalyticsHandler):
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# New structure from updated GSC service
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overall_rows = search_analytics.get('overall_metrics', {}).get('rows', [])
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query_rows = search_analytics.get('query_data', {}).get('rows', [])
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verification_rows = search_analytics.get('verification_data', {}).get('rows', [])
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logger.info(f"GSC Overall metrics rows: {len(overall_rows)}")
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logger.info(f"GSC Query data rows: {len(query_rows)}")
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logger.info(f"GSC Verification rows: {len(verification_rows)}")
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# Calculate totals from overall_rows (most accurate as it includes anonymized queries)
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total_clicks = 0
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total_impressions = 0
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total_position = 0
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valid_position_rows = 0
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if overall_rows:
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logger.info(f"GSC Overall first row: {overall_rows[0]}")
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if query_rows:
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logger.info(f"GSC Query first row: {query_rows[0]}")
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# Use overall_rows for totals if available, otherwise fallback to query_rows
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calc_rows = overall_rows if overall_rows else query_rows
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for row in calc_rows:
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clicks = row.get('clicks', 0)
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impressions = row.get('impressions', 0)
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position = row.get('position', 0)
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total_clicks += clicks
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total_impressions += impressions
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if position and position > 0:
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total_position += position * impressions # Weighted average
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# Calculate weighted average position
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avg_position = total_position / total_impressions if total_impressions > 0 else 0
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avg_ctr = (total_clicks / total_impressions * 100) if total_impressions > 0 else 0
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# Use query_rows for top queries list
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top_queries_source = query_rows
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# Use query_rows for detailed insights, overall_rows for summary
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rows = query_rows if query_rows else overall_rows
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else:
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# Legacy structure
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rows = search_analytics.get('rows', [])
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logger.info(f"GSC Legacy rows count: {len(rows)}")
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if rows:
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logger.info(f"GSC Legacy first row structure: {rows[0]}")
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logger.info(f"GSC Legacy first row keys: {list(rows[0].keys()) if rows[0] else 'No rows'}")
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# Calculate summary metrics - handle different response formats
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total_clicks = 0
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total_impressions = 0
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total_position = 0
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valid_rows = 0
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for row in rows:
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# Handle different possible response formats
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clicks = row.get('clicks', 0)
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impressions = row.get('impressions', 0)
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position = row.get('position', 0)
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# ... existing legacy logic ...
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calc_rows = rows
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top_queries_source = rows
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# If position is 0 or None, skip it from average calculation
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if position and position > 0:
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total_position += position
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valid_rows += 1
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total_clicks = 0
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total_impressions = 0
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total_position = 0
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valid_position_rows = 0
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total_clicks += clicks
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total_impressions += impressions
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avg_ctr = (total_clicks / total_impressions * 100) if total_impressions > 0 else 0
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avg_position = total_position / valid_rows if valid_rows > 0 else 0
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logger.info(f"GSC Calculated metrics - clicks: {total_clicks}, impressions: {total_impressions}, ctr: {avg_ctr}, position: {avg_position}, valid_rows: {valid_rows}")
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# Get top performing queries - handle different data structures
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if rows and 'keys' in rows[0]:
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# New GSC API format with keys array
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top_queries = sorted(rows, key=lambda x: x.get('clicks', 0), reverse=True)[:10]
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# Get top performing pages (if we have page data)
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page_data = {}
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for row in rows:
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# Handle different key structures
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keys = row.get('keys', [])
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if len(keys) > 1 and keys[1]: # Page data available
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page = keys[1].get('keys', ['Unknown'])[0] if isinstance(keys[1], dict) else str(keys[1])
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else:
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page = 'Unknown'
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for row in calc_rows:
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clicks = row.get('clicks', 0)
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impressions = row.get('impressions', 0)
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position = row.get('position', 0)
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if page not in page_data:
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page_data[page] = {'clicks': 0, 'impressions': 0, 'ctr': 0, 'position': 0}
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page_data[page]['clicks'] += row.get('clicks', 0)
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page_data[page]['impressions'] += row.get('impressions', 0)
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else:
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# Legacy format or no keys structure
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top_queries = sorted(rows, key=lambda x: x.get('clicks', 0), reverse=True)[:10]
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page_data = {}
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total_clicks += clicks
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total_impressions += impressions
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if position and position > 0:
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# Simple average for legacy/unknown structure if we can't do weighted
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total_position += position
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valid_position_rows += 1
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avg_ctr = (total_clicks / total_impressions * 100) if total_impressions > 0 else 0
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avg_position = total_position / valid_position_rows if valid_position_rows > 0 else 0
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# Calculate page metrics
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for page in page_data:
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if page_data[page]['impressions'] > 0:
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page_data[page]['ctr'] = page_data[page]['clicks'] / page_data[page]['impressions'] * 100
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top_pages = sorted(page_data.items(), key=lambda x: x[1]['clicks'], reverse=True)[:10]
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return {
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'connection_status': 'connected',
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'connected_sites': 1, # GSC typically has one site per user
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'total_clicks': total_clicks,
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'total_impressions': total_impressions,
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'avg_ctr': round(avg_ctr, 2),
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'avg_position': round(avg_position, 2),
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'total_queries': len(rows),
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'top_queries': [
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{
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# Get top performing queries
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top_queries = []
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if top_queries_source:
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# Sort by clicks
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sorted_queries = sorted(top_queries_source, key=lambda x: x.get('clicks', 0), reverse=True)[:10]
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for row in sorted_queries:
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top_queries.append({
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'query': self._extract_query_from_row(row),
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'clicks': row.get('clicks', 0),
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'impressions': row.get('impressions', 0),
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'ctr': round(row.get('ctr', 0) * 100, 2),
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'position': round(row.get('position', 0), 2)
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}
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for row in top_queries
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],
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'top_pages': [
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{
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'page': page,
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'clicks': data['clicks'],
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'impressions': data['impressions'],
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'ctr': round(data['ctr'], 2)
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}
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for page, data in top_pages
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],
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'note': 'Google Search Console provides search performance data, keyword rankings, and SEO insights'
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})
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# Prepare Top Pages (requires page dimension, but we only requested query dimension in gsc_service step 3)
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# To get top pages, we would need another API call with dimension=['page']
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# For now, we'll return empty top_pages or infer from what we have if possible (we can't from query data)
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top_pages = []
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return {
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'connection_status': 'connected',
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'connected_sites': 1,
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'total_clicks': total_clicks,
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'total_impressions': total_impressions,
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'avg_ctr': round(avg_ctr, 2),
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'avg_position': round(avg_position, 2),
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'total_queries': len(top_queries_source) if top_queries_source else 0,
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'top_queries': top_queries,
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'top_pages': top_pages
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}
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except Exception as e:
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@@ -59,6 +59,32 @@ class PlatformAnalyticsService:
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logger.info(f"Getting comprehensive analytics for user {user_id}, platforms: {platforms}")
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analytics_data = {}
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# Determine target URL from Wix/WP for GSC site selection
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target_url = None
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try:
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status = await self.get_platform_connection_status(user_id)
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# Check Wix
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if status.get('wix', {}).get('connected'):
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sites = status['wix'].get('sites', [])
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if sites:
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# Assuming site object has 'blog_url' or 'url'
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site = sites[0]
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target_url = site.get('blog_url') or site.get('url')
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# Check WordPress if no Wix
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if not target_url and status.get('wordpress', {}).get('connected'):
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sites = status['wordpress'].get('sites', [])
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if sites:
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site = sites[0]
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target_url = site.get('blog_url') or site.get('url')
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if target_url:
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logger.info(f"Identified primary site URL for GSC matching: {target_url}")
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except Exception as e:
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logger.warning(f"Failed to determine target URL for GSC: {e}")
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for platform_name in platforms:
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try:
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# Convert string to PlatformType enum
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@@ -66,7 +92,10 @@ class PlatformAnalyticsService:
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handler = self.handlers.get(platform_type)
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if handler:
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analytics_data[platform_name] = await handler.get_analytics(user_id)
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if platform_type == PlatformType.GSC or platform_type == PlatformType.BING:
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analytics_data[platform_name] = await handler.get_analytics(user_id, target_url=target_url)
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else:
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analytics_data[platform_name] = await handler.get_analytics(user_id)
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else:
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logger.warning(f"Unknown platform: {platform_name}")
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analytics_data[platform_name] = self._create_error_response(platform_name, f"Unknown platform: {platform_name}")
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Block a user