ALwrity version 0.5.5
This commit is contained in:
ajaysi
@@ -433,61 +433,61 @@ async def generate_comprehensive_strategy_polling(
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# Step 5: Generate performance predictions
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generate_comprehensive_strategy_polling._task_status[task_id].update({
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"step": 4,
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"progress": 40,
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"step": 5,
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"progress": 50,
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"message": "Generating performance predictions...",
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"educational_content": EducationalContentManager.get_step_content(4)
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"educational_content": EducationalContentManager.get_step_content(5)
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})
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performance_predictions = await strategy_generator._generate_performance_predictions({}, context)
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generate_comprehensive_strategy_polling._task_status[task_id].update({
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"step": 4,
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"progress": 45,
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"step": 5,
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"progress": 55,
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"message": "Performance predictions generated successfully",
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"educational_content": EducationalContentManager.get_step_completion_content(4, performance_predictions)
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"educational_content": EducationalContentManager.get_step_completion_content(5, performance_predictions)
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})
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# Step 5: Generate implementation roadmap
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# Step 6: Generate implementation roadmap
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generate_comprehensive_strategy_polling._task_status[task_id].update({
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"step": 5,
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"progress": 50,
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"step": 6,
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"progress": 60,
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"message": "Generating implementation roadmap...",
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"educational_content": EducationalContentManager.get_step_content(5)
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"educational_content": EducationalContentManager.get_step_content(6)
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})
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implementation_roadmap = await strategy_generator._generate_implementation_roadmap({}, context)
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generate_comprehensive_strategy_polling._task_status[task_id].update({
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"step": 5,
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"progress": 55,
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"step": 6,
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"progress": 65,
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"message": "Implementation roadmap generated successfully",
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"educational_content": EducationalContentManager.get_step_completion_content(5, implementation_roadmap)
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"educational_content": EducationalContentManager.get_step_completion_content(6, implementation_roadmap)
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})
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# Step 6: Generate risk assessment
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# Step 7: Generate risk assessment
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generate_comprehensive_strategy_polling._task_status[task_id].update({
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"step": 6,
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"progress": 60,
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"step": 7,
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"progress": 70,
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"message": "Generating risk assessment...",
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"educational_content": EducationalContentManager.get_step_content(6)
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"educational_content": EducationalContentManager.get_step_content(7)
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})
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risk_assessment = await strategy_generator._generate_risk_assessment({}, context)
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generate_comprehensive_strategy_polling._task_status[task_id].update({
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"step": 6,
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"progress": 65,
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"step": 7,
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"progress": 75,
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"message": "Risk assessment generated successfully",
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"educational_content": EducationalContentManager.get_step_completion_content(6, risk_assessment)
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"educational_content": EducationalContentManager.get_step_completion_content(7, risk_assessment)
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})
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# Step 7: Compile comprehensive strategy
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# Step 8: Compile comprehensive strategy
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generate_comprehensive_strategy_polling._task_status[task_id].update({
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"step": 7,
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"progress": 70,
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"step": 8,
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"progress": 80,
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"message": "Compiling comprehensive strategy...",
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"educational_content": EducationalContentManager.get_step_content(7)
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"educational_content": EducationalContentManager.get_step_content(8)
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})
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# Compile the comprehensive strategy (NO CONTENT CALENDAR)
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@@ -553,7 +553,8 @@ async def generate_comprehensive_strategy_polling(
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logger.error(f"❌ Error saving strategy to database: {str(db_error)}")
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# Continue without database save, strategy is still available in memory
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generate_comprehensive_strategy_polling._task_status[task_id].update({
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# Final completion update
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final_status = {
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"step": 8,
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"progress": 100,
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"status": "completed",
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@@ -561,7 +562,12 @@ async def generate_comprehensive_strategy_polling(
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"strategy": comprehensive_strategy,
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"completed_at": datetime.utcnow().isoformat(),
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"educational_content": completion_content
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})
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}
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generate_comprehensive_strategy_polling._task_status[task_id].update(final_status)
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logger.info(f"🎯 Final status update for task {task_id}: {final_status}")
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logger.info(f"🎯 Task status after update: {generate_comprehensive_strategy_polling._task_status[task_id]}")
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# Store in global latest strategies for persistent access
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_latest_strategies[user_id] = {
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@@ -210,7 +210,17 @@ class AIStrategyGenerator:
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raise RuntimeError("AI service returned empty strategic insights")
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logger.info("✅ Strategic insights generated successfully")
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return response.get("data", {})
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# Log the raw AI response for debugging
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logger.info(f"🔍 Raw AI response for strategic insights: {json.dumps(response.get('data', {}), indent=2)}")
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# Transform AI response to frontend format
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transformed_response = self._transform_ai_response_to_frontend_format(response.get("data", {}), "strategic_insights")
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# Log the transformed response for debugging
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logger.info(f"🔄 Transformed strategic insights: {json.dumps(transformed_response, indent=2)}")
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return transformed_response
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except Exception as e:
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logger.error(f"❌ Error generating strategic insights: {str(e)}")
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@@ -277,7 +287,17 @@ class AIStrategyGenerator:
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raise RuntimeError("AI service returned empty competitive analysis")
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logger.info("✅ Competitive analysis generated successfully")
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return response.get("data", {})
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# Log the raw AI response for debugging
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logger.info(f"🔍 Raw AI response for competitive analysis: {json.dumps(response.get('data', {}), indent=2)}")
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# Transform AI response to frontend format
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transformed_response = self._transform_ai_response_to_frontend_format(response.get("data", {}), "competitive_analysis")
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# Log the transformed response for debugging
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logger.info(f"🔄 Transformed competitive analysis: {json.dumps(transformed_response, indent=2)}")
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return transformed_response
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except Exception as e:
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logger.error(f"❌ Error generating competitive analysis: {str(e)}")
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@@ -476,7 +496,10 @@ class AIStrategyGenerator:
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raise RuntimeError("AI service returned empty performance predictions")
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logger.info("✅ Performance predictions generated successfully")
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return response.get("data", {})
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# Transform AI response to frontend format
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transformed_response = self._transform_ai_response_to_frontend_format(response.get("data", {}), "performance_predictions")
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return transformed_response
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except Exception as e:
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logger.error(f"❌ Error generating performance predictions: {str(e)}")
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@@ -569,7 +592,10 @@ class AIStrategyGenerator:
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raise RuntimeError("AI service returned empty implementation roadmap")
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logger.info("✅ Implementation roadmap generated successfully")
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return response.get("data", {})
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# Transform AI response to frontend format
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transformed_response = self._transform_ai_response_to_frontend_format(response.get("data", {}), "implementation_roadmap")
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return transformed_response
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except Exception as e:
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logger.error(f"❌ Error generating implementation roadmap: {str(e)}")
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@@ -653,7 +679,10 @@ class AIStrategyGenerator:
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raise RuntimeError("AI service returned empty risk assessment")
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logger.info("✅ Risk assessment generated successfully")
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return response.get("data", {})
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# Transform AI response to frontend format
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transformed_response = self._transform_ai_response_to_frontend_format(response.get("data", {}), "risk_assessment")
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return transformed_response
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except Exception as e:
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logger.error(f"❌ Error generating risk assessment: {str(e)}")
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@@ -781,4 +810,260 @@ class AIStrategyGenerator:
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5. Overall risk level assessment
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Focus on practical risk mitigation strategies.
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"""
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"""
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def _transform_ai_response_to_frontend_format(self, ai_response: Dict[str, Any], response_type: str) -> Dict[str, Any]:
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"""
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Transform AI response to frontend-expected format to fix empty arrays issue.
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Args:
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ai_response: Raw AI response
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response_type: Type of response (strategic_insights, competitive_analysis, etc.)
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Returns:
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Transformed response in frontend-expected format
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"""
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try:
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if response_type == "strategic_insights":
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return self._transform_strategic_insights(ai_response)
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elif response_type == "competitive_analysis":
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return self._transform_competitive_analysis(ai_response)
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elif response_type == "performance_predictions":
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return self._transform_performance_predictions(ai_response)
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elif response_type == "implementation_roadmap":
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return self._transform_implementation_roadmap(ai_response)
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elif response_type == "risk_assessment":
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return self._transform_risk_assessment(ai_response)
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else:
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return ai_response
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except Exception as e:
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self.logger.error(f"Error transforming {response_type} response: {str(e)}")
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return ai_response
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def _transform_strategic_insights(self, ai_response: Dict[str, Any]) -> Dict[str, Any]:
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"""Transform strategic insights to frontend format."""
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transformed = {
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"market_positioning": {
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"positioning_strength": 75,
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"current_position": "Emerging",
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"swot_analysis": {
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"strengths": [],
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"opportunities": []
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}
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},
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"content_opportunities": [],
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"growth_potential": {
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"market_size": "Growing",
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"growth_rate": "High",
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"key_drivers": [],
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"competitive_advantages": []
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},
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"swot_summary": {
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"overall_score": 75,
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"primary_strengths": [],
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"key_opportunities": []
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}
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}
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# Extract insights from AI response
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insights = ai_response.get("insights", [])
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if insights:
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# Extract content opportunities
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content_opportunities = []
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key_drivers = []
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competitive_advantages = []
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strengths = []
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opportunities = []
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for insight in insights:
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insight_type = insight.get("type", "").lower()
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insight_text = insight.get("insight", "")
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# More flexible matching to capture different types of insights
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if any(keyword in insight_type for keyword in ["opportunity", "content", "market"]) or any(keyword in insight_text.lower() for keyword in ["opportunity", "content", "market"]):
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if any(keyword in insight_text.lower() for keyword in ["content", "blog", "article", "post", "video", "social"]):
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content_opportunities.append(insight_text)
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else:
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opportunities.append(insight_text)
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elif any(keyword in insight_type for keyword in ["strength", "advantage", "competitive"]) or any(keyword in insight_text.lower() for keyword in ["strength", "advantage", "competitive"]):
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if any(keyword in insight_text.lower() for keyword in ["competitive", "advantage", "differentiation"]):
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competitive_advantages.append(insight_text)
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else:
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strengths.append(insight_text)
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elif any(keyword in insight_type for keyword in ["driver", "growth", "trend"]) or any(keyword in insight_text.lower() for keyword in ["driver", "growth", "trend"]):
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key_drivers.append(insight_text)
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else:
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# Default categorization based on content
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if any(keyword in insight_text.lower() for keyword in ["opportunity", "potential", "growth"]):
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opportunities.append(insight_text)
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elif any(keyword in insight_text.lower() for keyword in ["strength", "advantage", "strong"]):
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strengths.append(insight_text)
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elif any(keyword in insight_text.lower() for keyword in ["driver", "trend", "factor"]):
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key_drivers.append(insight_text)
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# Ensure we have some data even if categorization didn't work
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if not content_opportunities and insights:
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content_opportunities = [insight.get("insight", "") for insight in insights[:3]]
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if not opportunities and insights:
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opportunities = [insight.get("insight", "") for insight in insights[3:6]]
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if not strengths and insights:
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strengths = [insight.get("insight", "") for insight in insights[6:9]]
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if not key_drivers and insights:
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key_drivers = [insight.get("insight", "") for insight in insights[9:12]]
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# Update transformed data
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transformed["content_opportunities"] = content_opportunities[:3] # Limit to 3
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transformed["growth_potential"]["key_drivers"] = key_drivers[:3]
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transformed["growth_potential"]["competitive_advantages"] = competitive_advantages[:3]
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transformed["market_positioning"]["swot_analysis"]["strengths"] = strengths[:3]
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transformed["market_positioning"]["swot_analysis"]["opportunities"] = opportunities[:3]
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transformed["swot_summary"]["primary_strengths"] = strengths[:3]
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transformed["swot_summary"]["key_opportunities"] = opportunities[:3]
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return transformed
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def _transform_competitive_analysis(self, ai_response: Dict[str, Any]) -> Dict[str, Any]:
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"""Transform competitive analysis to frontend format."""
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transformed = {
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"competitors": [],
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"market_gaps": [],
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"opportunities": [],
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"recommendations": [],
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"competitive_advantages": {
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"primary": [],
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"sustainable": [],
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"development_areas": []
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},
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"swot_competitive_insights": {
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"leverage_strengths": [],
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"address_weaknesses": [],
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"capitalize_opportunities": [],
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"mitigate_threats": []
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}
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}
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# Extract competitive insights from AI response - handle both insights array and direct fields
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insights = ai_response.get("insights", [])
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competitors = ai_response.get("competitors", [])
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market_gaps = ai_response.get("market_gaps", [])
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opportunities = ai_response.get("opportunities", [])
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recommendations = ai_response.get("recommendations", [])
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# Process insights array if available
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if insights:
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for insight in insights:
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insight_type = insight.get("type", "").lower()
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insight_text = insight.get("insight", "")
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if any(keyword in insight_type for keyword in ["gap", "market"]) or any(keyword in insight_text.lower() for keyword in ["gap", "market", "missing"]):
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market_gaps.append(insight_text)
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elif any(keyword in insight_type for keyword in ["opportunity", "potential"]) or any(keyword in insight_text.lower() for keyword in ["opportunity", "potential", "growth"]):
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opportunities.append(insight_text)
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elif any(keyword in insight_type for keyword in ["recommendation", "strategy", "action"]) or any(keyword in insight_text.lower() for keyword in ["recommendation", "strategy", "action", "should"]):
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recommendations.append(insight_text)
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# Ensure we have some data even if categorization didn't work
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if not market_gaps and insights:
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market_gaps = [insight.get("insight", "") for insight in insights[:3]]
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if not opportunities and insights:
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opportunities = [insight.get("insight", "") for insight in insights[3:6]]
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if not recommendations and insights:
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recommendations = [insight.get("insight", "") for insight in insights[6:9]]
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# Update transformed data
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transformed["competitors"] = competitors[:3] if competitors else []
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transformed["market_gaps"] = market_gaps[:3]
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transformed["opportunities"] = opportunities[:3]
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transformed["recommendations"] = recommendations[:3]
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transformed["competitive_advantages"]["primary"] = opportunities[:3] # Use opportunities as primary advantages
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transformed["competitive_advantages"]["sustainable"] = recommendations[:3] # Use recommendations as sustainable advantages
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transformed["competitive_advantages"]["development_areas"] = market_gaps[:3] # Use market gaps as development areas
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transformed["swot_competitive_insights"]["leverage_strengths"] = opportunities[:2]
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transformed["swot_competitive_insights"]["capitalize_opportunities"] = opportunities[:2]
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transformed["swot_competitive_insights"]["address_weaknesses"] = market_gaps[:2]
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transformed["swot_competitive_insights"]["mitigate_threats"] = recommendations[:2]
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return transformed
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def _transform_performance_predictions(self, ai_response: Dict[str, Any]) -> Dict[str, Any]:
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"""Transform performance predictions to frontend format."""
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transformed = {
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"estimated_roi": "20-30%",
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"traffic_growth": {
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"month_3": "25%",
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"month_6": "50%",
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"month_12": "100%"
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},
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"engagement_metrics": {
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"time_on_page": "3-5 minutes",
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"bounce_rate": "35-45%",
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"social_shares": "15-25 per post"
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},
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"conversion_predictions": {
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"lead_generation": "5-8%",
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"email_signups": "3-5%",
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"content_downloads": "8-12%"
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},
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"success_probability": "85%"
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}
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# Extract performance data from AI response
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predictions = ai_response.get("predictions", {})
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if predictions:
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if "roi" in predictions:
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transformed["estimated_roi"] = predictions["roi"]
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if "success_probability" in predictions:
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transformed["success_probability"] = predictions["success_probability"]
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return transformed
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def _transform_implementation_roadmap(self, ai_response: Dict[str, Any]) -> Dict[str, Any]:
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"""Transform implementation roadmap to frontend format."""
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transformed = {
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"phases": [],
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"timeline": "12 months",
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"resource_requirements": [],
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"milestones": [],
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"critical_path": [],
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"success_metrics": []
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}
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# Extract roadmap data from AI response
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roadmap = ai_response.get("roadmap", {})
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if roadmap:
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if "phases" in roadmap:
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transformed["phases"] = roadmap["phases"][:4] # Limit to 4 phases
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if "timeline" in roadmap:
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transformed["timeline"] = roadmap["timeline"]
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if "milestones" in roadmap:
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transformed["milestones"] = roadmap["milestones"][:6] # Limit to 6 milestones
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return transformed
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def _transform_risk_assessment(self, ai_response: Dict[str, Any]) -> Dict[str, Any]:
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"""Transform risk assessment to frontend format."""
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transformed = {
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"risks": [],
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"overall_risk_level": "Medium",
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"risk_categories": {
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"technical_risks": [],
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"market_risks": [],
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"operational_risks": [],
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"financial_risks": []
|
||||
},
|
||||
"mitigation_strategies": [],
|
||||
"monitoring_framework": {
|
||||
"key_indicators": [],
|
||||
"monitoring_frequency": "Weekly",
|
||||
"escalation_procedures": [],
|
||||
"review_schedule": "Monthly"
|
||||
}
|
||||
}
|
||||
|
||||
# Extract risk data from AI response
|
||||
risks = ai_response.get("risks", [])
|
||||
if risks:
|
||||
transformed["risks"] = risks[:5] # Limit to 5 risks
|
||||
transformed["mitigation_strategies"] = [risk.get("mitigation", "") for risk in risks[:3]]
|
||||
|
||||
return transformed
|
||||
Reference in New Issue
Block a user