Recovered state: integrated TrendSurferAgent, restored frontend/backend files, and cleaned up recovery scripts
This commit is contained in:
ajaysi
@@ -16,7 +16,7 @@ from services.llm_providers.main_text_generation import llm_text_gen
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from services.llm_providers.gemini_provider import gemini_structured_json_response
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# Import services
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from services.ai_service_manager import AIServiceManager
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from services.ai_service_manager import AIServiceManager, AIServiceType
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# Import existing modules (will be updated to use FastAPI services)
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from services.database import get_db_session
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@@ -40,12 +40,13 @@ class AIEngineService:
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logger.debug("AIEngineService initialized")
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self._initialized = True
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async def analyze_content_gaps(self, analysis_summary: Dict[str, Any]) -> Dict[str, Any]:
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async def analyze_content_gaps(self, analysis_summary: Dict[str, Any], user_id: str) -> Dict[str, Any]:
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"""
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Analyze content gaps using AI insights.
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Args:
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analysis_summary: Summary of content analysis
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user_id: User ID for subscription checking
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Returns:
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AI-powered content gap insights
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@@ -54,7 +55,7 @@ class AIEngineService:
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logger.info("🤖 Generating AI-powered content gap insights using centralized AI service")
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# Use the centralized AI service manager for strategic analysis
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result = await self.ai_service_manager.generate_content_gap_analysis(analysis_summary)
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result = await self.ai_service_manager.generate_content_gap_analysis(analysis_summary, user_id=user_id)
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logger.info("✅ Advanced AI content gap analysis completed")
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return result
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@@ -97,12 +98,13 @@ class AIEngineService:
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}
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}
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async def analyze_market_position(self, market_data: Dict[str, Any]) -> Dict[str, Any]:
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async def analyze_market_position(self, market_data: Dict[str, Any], user_id: str) -> Dict[str, Any]:
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"""
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Analyze market position using AI insights.
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Args:
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market_data: Market analysis data
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user_id: User ID for subscription checking
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Returns:
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AI-powered market position analysis
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@@ -111,7 +113,7 @@ class AIEngineService:
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logger.info("🤖 Generating AI-powered market position analysis using centralized AI service")
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# Use the centralized AI service manager for market position analysis
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result = await self.ai_service_manager.generate_market_position_analysis(market_data)
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result = await self.ai_service_manager.generate_market_position_analysis(market_data, user_id=user_id)
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logger.info("✅ Advanced AI market position analysis completed")
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return result
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@@ -165,12 +167,13 @@ class AIEngineService:
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]
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}
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async def generate_content_recommendations(self, analysis_data: Dict[str, Any]) -> List[Dict[str, Any]]:
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async def generate_content_recommendations(self, analysis_data: Dict[str, Any], user_id: str) -> List[Dict[str, Any]]:
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"""
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Generate AI-powered content recommendations.
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Args:
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analysis_data: Content analysis data
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user_id: User ID for subscription checking
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Returns:
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List of AI-generated content recommendations
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@@ -196,35 +199,38 @@ class AIEngineService:
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"""
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# Use structured JSON response for better parsing
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response = gemini_structured_json_response(
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prompt=prompt,
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schema={
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"type": "object",
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"properties": {
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"recommendations": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"type": {"type": "string"},
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"title": {"type": "string"},
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"description": {"type": "string"},
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"priority": {"type": "string"},
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"estimated_impact": {"type": "string"},
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"implementation_time": {"type": "string"},
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"ai_confidence": {"type": "number"},
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"content_suggestions": {
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"type": "array",
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"items": {"type": "string"}
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}
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schema = {
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"type": "object",
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"properties": {
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"recommendations": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"type": {"type": "string"},
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"title": {"type": "string"},
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"description": {"type": "string"},
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"priority": {"type": "string"},
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"estimated_impact": {"type": "string"},
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"implementation_time": {"type": "string"},
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"ai_confidence": {"type": "number"},
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"content_suggestions": {
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"type": "array",
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"items": {"type": "string"}
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}
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}
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}
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}
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}
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}
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response = llm_text_gen(
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prompt=prompt,
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json_struct=schema,
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user_id=user_id
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)
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# Handle response - gemini_structured_json_response returns dict directly
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# Handle response - llm_text_gen returns structured dict when json_struct is provided
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if isinstance(response, dict):
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result = response
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elif isinstance(response, str):
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@@ -292,12 +298,13 @@ class AIEngineService:
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}
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]
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async def predict_content_performance(self, content_data: Dict[str, Any]) -> Dict[str, Any]:
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async def predict_content_performance(self, content_data: Dict[str, Any], user_id: str) -> Dict[str, Any]:
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"""
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Predict content performance using AI.
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Args:
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content_data: Content analysis data
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user_id: User ID for subscription checking
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Returns:
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AI-powered performance predictions
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@@ -323,61 +330,64 @@ class AIEngineService:
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"""
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# Use structured JSON response for better parsing
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response = gemini_structured_json_response(
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prompt=prompt,
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schema={
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"type": "object",
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"properties": {
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"traffic_predictions": {
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"type": "object",
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"properties": {
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"estimated_monthly_traffic": {"type": "string"},
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"traffic_growth_rate": {"type": "string"},
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"peak_traffic_month": {"type": "string"},
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"confidence_level": {"type": "string"}
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}
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},
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"engagement_predictions": {
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"type": "object",
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"properties": {
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"estimated_time_on_page": {"type": "string"},
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"estimated_bounce_rate": {"type": "string"},
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"estimated_social_shares": {"type": "string"},
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"estimated_comments": {"type": "string"},
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"confidence_level": {"type": "string"}
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}
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},
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"ranking_predictions": {
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"type": "object",
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"properties": {
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"estimated_ranking_position": {"type": "string"},
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"estimated_ranking_time": {"type": "string"},
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"ranking_confidence": {"type": "string"},
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"competition_level": {"type": "string"}
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}
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},
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"conversion_predictions": {
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"type": "object",
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"properties": {
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"estimated_conversion_rate": {"type": "string"},
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"estimated_lead_generation": {"type": "string"},
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"estimated_revenue_impact": {"type": "string"},
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"confidence_level": {"type": "string"}
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}
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},
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"risk_factors": {
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"type": "array",
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"items": {"type": "string"}
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},
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"success_factors": {
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"type": "array",
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"items": {"type": "string"}
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schema = {
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"type": "object",
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"properties": {
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"traffic_predictions": {
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"type": "object",
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"properties": {
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"estimated_monthly_traffic": {"type": "string"},
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"traffic_growth_rate": {"type": "string"},
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"peak_traffic_month": {"type": "string"},
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"confidence_level": {"type": "string"}
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}
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},
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"engagement_predictions": {
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"type": "object",
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"properties": {
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"estimated_time_on_page": {"type": "string"},
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"estimated_bounce_rate": {"type": "string"},
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"estimated_social_shares": {"type": "string"},
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"estimated_comments": {"type": "string"},
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"confidence_level": {"type": "string"}
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}
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},
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"ranking_predictions": {
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"type": "object",
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"properties": {
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"estimated_ranking_position": {"type": "string"},
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"estimated_ranking_time": {"type": "string"},
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"ranking_confidence": {"type": "string"},
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"competition_level": {"type": "string"}
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}
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},
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"conversion_predictions": {
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"type": "object",
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"properties": {
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"estimated_conversion_rate": {"type": "string"},
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"estimated_lead_generation": {"type": "string"},
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"estimated_revenue_impact": {"type": "string"},
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"confidence_level": {"type": "string"}
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}
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},
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"risk_factors": {
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"type": "array",
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"items": {"type": "string"}
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},
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"success_factors": {
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"type": "array",
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"items": {"type": "string"}
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}
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}
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}
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response = llm_text_gen(
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prompt=prompt,
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json_struct=schema,
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user_id=user_id
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)
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# Handle response - gemini_structured_json_response returns dict directly
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# Handle response - llm_text_gen returns structured dict when json_struct is provided
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if isinstance(response, dict):
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predictions = response
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elif isinstance(response, str):
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@@ -437,12 +447,13 @@ class AIEngineService:
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]
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}
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async def analyze_competitive_intelligence(self, competitor_data: Dict[str, Any]) -> Dict[str, Any]:
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async def analyze_competitive_intelligence(self, competitor_data: Dict[str, Any], user_id: str) -> Dict[str, Any]:
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"""
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Analyze competitive intelligence using AI.
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Args:
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competitor_data: Competitor analysis data
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user_id: User ID for subscription checking
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Returns:
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AI-powered competitive intelligence
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@@ -467,82 +478,71 @@ class AIEngineService:
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"""
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# Use structured JSON response for better parsing
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response = gemini_structured_json_response(
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prompt=prompt,
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schema={
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"type": "object",
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"properties": {
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"market_analysis": {
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schema = {
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"type": "object",
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"properties": {
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"market_analysis": {
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"type": "object",
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"properties": {
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"market_leader": {"type": "string"},
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"market_share_estimate": {"type": "string"},
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"market_trends": {"type": "array", "items": {"type": "string"}}
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}
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},
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"content_strategy_insights": {
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"type": "object",
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"properties": {
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"content_focus": {"type": "string"},
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"content_frequency": {"type": "string"},
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"content_channels": {"type": "array", "items": {"type": "string"}}
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}
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},
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"competitive_advantages": {
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"type": "array",
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"items": {"type": "string"}
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},
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"threat_analysis": {
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"type": "object",
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"properties": {
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"direct_threats": {"type": "array", "items": {"type": "string"}},
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"indirect_threats": {"type": "array", "items": {"type": "string"}}
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}
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},
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"opportunity_analysis": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"market_leader": {"type": "string"},
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"content_leader": {"type": "string"},
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"innovation_leader": {"type": "string"},
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"market_gaps": {
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"type": "array",
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"items": {"type": "string"}
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}
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}
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},
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"content_strategy_insights": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"insight": {"type": "string"},
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"opportunity": {"type": "string"},
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"priority": {"type": "string"},
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"estimated_impact": {"type": "string"}
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}
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}
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},
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"competitive_advantages": {
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"type": "array",
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"items": {"type": "string"}
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},
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"threat_analysis": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"threat": {"type": "string"},
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"risk_level": {"type": "string"},
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"mitigation": {"type": "string"}
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}
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}
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},
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"opportunity_analysis": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"opportunity": {"type": "string"},
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"market_gap": {"type": "string"},
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"estimated_impact": {"type": "string"},
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"implementation_time": {"type": "string"}
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}
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"opportunity": {"type": "string"},
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"potential_impact": {"type": "string"},
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"implementation_difficulty": {"type": "string"}
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}
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}
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}
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}
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}
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response = llm_text_gen(
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prompt=prompt,
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json_struct=schema,
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user_id=user_id
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)
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# Parse and return the AI response
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# Handle response - gemini_structured_json_response returns dict directly
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# Handle response - llm_text_gen returns structured dict when json_struct is provided
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if isinstance(response, dict):
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competitive_intelligence = response
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intelligence = response
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elif isinstance(response, str):
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# If it's a string, try to parse as JSON
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try:
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competitive_intelligence = json.loads(response)
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intelligence = json.loads(response)
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except json.JSONDecodeError as e:
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logger.error(f"Failed to parse AI response as JSON: {e}")
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raise Exception(f"Invalid AI response format: {str(e)}")
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else:
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logger.error(f"Unexpected response type from AI service: {type(response)}")
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raise Exception(f"Unexpected response type from AI service: {type(response)}")
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logger.info("✅ AI competitive intelligence completed")
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return competitive_intelligence
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logger.info("✅ AI competitive intelligence analysis completed")
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return intelligence
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except Exception as e:
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logger.error(f"Error in AI competitive intelligence: {str(e)}")
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@@ -833,14 +833,9 @@ class AIEngineService:
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try:
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logger.info("Performing health check for AIEngineService")
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# Test AI functionality with a simple prompt
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test_prompt = "Hello, this is a health check test."
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try:
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test_response = llm_text_gen(test_prompt)
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ai_status = "operational" if test_response else "degraded"
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except Exception as e:
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ai_status = "error"
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logger.warning(f"AI health check failed: {str(e)}")
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# Check if AIServiceManager is healthy
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ai_manager_health = await self.ai_service_manager.health_check()
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ai_status = ai_manager_health.get('capabilities', {}).get('ai_integration', 'unknown')
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health_status = {
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'service': 'AIEngineService',
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@@ -37,7 +37,7 @@ class ContentGapAnalyzer:
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logger.info("ContentGapAnalyzer initialized")
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async def analyze_comprehensive_gap(self, target_url: str, competitor_urls: List[str],
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target_keywords: List[str], industry: str = "general") -> Dict[str, Any]:
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target_keywords: List[str], user_id: str, industry: str = "general") -> Dict[str, Any]:
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"""
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Perform comprehensive content gap analysis.
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@@ -45,6 +45,7 @@ class ContentGapAnalyzer:
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target_url: Your website URL
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competitor_urls: List of competitor URLs (max 5 for performance)
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target_keywords: List of primary keywords to analyze
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user_id: User ID for subscription checking
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industry: Industry category for context
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Returns:
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@@ -95,7 +96,7 @@ class ContentGapAnalyzer:
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# Phase 5: AI-Powered Insights
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logger.info("🤖 Generating AI-powered insights")
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ai_insights = await self._generate_ai_insights(results)
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ai_insights = await self._generate_ai_insights(results, user_id=user_id)
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results['ai_insights'] = ai_insights
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logger.info("✅ Generated comprehensive AI insights")
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@@ -496,12 +497,13 @@ class ContentGapAnalyzer:
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logger.error(f"Error in content theme analysis: {str(e)}")
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return {}
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async def _generate_ai_insights(self, analysis_results: Dict[str, Any]) -> Dict[str, Any]:
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async def _generate_ai_insights(self, analysis_results: Dict[str, Any], user_id: str) -> Dict[str, Any]:
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"""
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Generate AI-powered insights using advanced AI analysis.
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Args:
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analysis_results: Complete analysis results
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user_id: User ID for subscription checking
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Returns:
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AI-generated insights
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@@ -520,7 +522,7 @@ class ContentGapAnalyzer:
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}
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# Generate comprehensive AI insights using AI engine
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ai_insights = await self.ai_engine.analyze_content_gaps(analysis_summary)
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ai_insights = await self.ai_engine.analyze_content_gaps(analysis_summary, user_id=user_id)
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if ai_insights:
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logger.info("✅ Generated comprehensive AI insights")
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