"""
Content Strategy Analysis Test
Comprehensive analysis of content strategy data flow, AI prompts, and generated data points.
"""

import asyncio
import json
import time
from typing import Dict, Any, List
from datetime import datetime
from loguru import logger

# Import test utilities - using absolute import
try:
    from test_data import TestData
except ImportError:
    # Fallback for when running as standalone script
    class TestData:
        def __init__(self):
            pass

class ContentStrategyAnalysis:
    """Comprehensive analysis of content strategy functionality."""
    
    def __init__(self):
        self.test_data = TestData()
        self.analysis_results = {}
    
    async def analyze_content_strategy_flow(self) -> Dict[str, Any]:
        """Analyze the complete content strategy data flow."""
        logger.info("🔍 Starting Content Strategy Analysis")
        
        analysis = {
            "timestamp": datetime.utcnow().isoformat(),
            "phase": "content_strategy",
            "analysis": {}
        }
        
        # 1. Input Analysis
        analysis["analysis"]["inputs"] = await self._analyze_inputs()
        
        # 2. AI Prompt Analysis
        analysis["analysis"]["ai_prompts"] = await self._analyze_ai_prompts()
        
        # 3. Data Points Analysis
        analysis["analysis"]["data_points"] = await self._analyze_data_points()
        
        # 4. Frontend Mapping Analysis
        analysis["analysis"]["frontend_mapping"] = await self._analyze_frontend_mapping()
        
        # 5. Test Results
        analysis["analysis"]["test_results"] = await self._run_comprehensive_tests()
        
        logger.info("✅ Content Strategy Analysis Completed")
        return analysis
    
    async def _analyze_inputs(self) -> Dict[str, Any]:
        """Analyze the inputs required for content strategy generation."""
        logger.info("📊 Analyzing Content Strategy Inputs")
        
        inputs_analysis = {
            "required_inputs": {
                "user_id": {
                    "type": "integer",
                    "description": "User identifier for personalization",
                    "required": True,
                    "example": 1
                },
                "name": {
                    "type": "string",
                    "description": "Strategy name for identification",
                    "required": True,
                    "example": "Digital Marketing Strategy"
                },
                "industry": {
                    "type": "string",
                    "description": "Business industry for context",
                    "required": True,
                    "example": "technology"
                },
                "target_audience": {
                    "type": "object",
                    "description": "Target audience demographics and preferences",
                    "required": True,
                    "example": {
                        "demographics": ["professionals", "business_owners"],
                        "interests": ["digital_marketing", "content_creation"],
                        "age_range": "25-45",
                        "location": "global"
                    }
                },
                "content_pillars": {
                    "type": "array",
                    "description": "Content pillars and themes",
                    "required": False,
                    "example": [
                        {
                            "name": "Educational Content",
                            "description": "How-to guides and tutorials",
                            "content_types": ["blog", "video", "webinar"]
                        }
                    ]
                }
            },
            "optional_inputs": {
                "ai_recommendations": {
                    "type": "object",
                    "description": "AI-generated recommendations",
                    "required": False
                },
                "strategy_id": {
                    "type": "integer",
                    "description": "Existing strategy ID for updates",
                    "required": False
                }
            },
            "data_sources": [
                "User onboarding data",
                "Industry benchmarks",
                "Competitor analysis",
                "Historical performance data",
                "Market trends"
            ]
        }
        
        logger.info(f"📋 Input Analysis: {len(inputs_analysis['required_inputs'])} required inputs identified")
        return inputs_analysis
    
    async def _analyze_ai_prompts(self) -> Dict[str, Any]:
        """Analyze the AI prompts used in content strategy generation."""
        logger.info("🤖 Analyzing AI Prompts for Content Strategy")
        
        prompts_analysis = {
            "strategic_intelligence_prompt": {
                "purpose": "Generate strategic intelligence for content planning",
                "components": [
                    "Strategy data analysis",
                    "Market positioning assessment",
                    "Competitive advantage identification",
                    "Strategic score calculation",
                    "Risk assessment",
                    "Opportunity analysis"
                ],
                "input_data": [
                    "strategy_id",
                    "market_data (optional)",
                    "historical performance",
                    "competitor analysis",
                    "industry trends"
                ],
                "output_structure": {
                    "strategy_id": "integer",
                    "market_positioning": "object",
                    "competitive_advantages": "array",
                    "strategic_scores": "object",
                    "risk_assessment": "array",
                    "opportunity_analysis": "array",
                    "analysis_date": "datetime"
                }
            },
            "performance_trends_prompt": {
                "purpose": "Analyze performance trends for content strategy",
                "components": [
                    "Metric trend analysis",
                    "Predictive insights generation",
                    "Performance score calculation",
                    "Recommendation generation"
                ],
                "metrics_analyzed": [
                    "engagement_rate",
                    "reach",
                    "conversion_rate",
                    "click_through_rate"
                ]
            },
            "content_evolution_prompt": {
                "purpose": "Analyze content evolution over time",
                "components": [
                    "Content type evolution analysis",
                    "Engagement pattern analysis",
                    "Performance trend analysis",
                    "Evolution recommendation generation"
                ]
            }
        }
        
        logger.info(f"🤖 AI Prompt Analysis: {len(prompts_analysis)} prompt types identified")
        return prompts_analysis
    
    async def _analyze_data_points(self) -> Dict[str, Any]:
        """Analyze the data points generated by content strategy."""
        logger.info("📊 Analyzing Generated Data Points")
        
        data_points_analysis = {
            "strategic_insights": {
                "description": "AI-generated strategic insights for content planning",
                "structure": [
                    {
                        "id": "string",
                        "type": "string",
                        "title": "string",
                        "description": "string",
                        "priority": "string",
                        "estimated_impact": "string",
                        "created_at": "datetime"
                    }
                ],
                "example": {
                    "id": "market_position_1",
                    "type": "warning",
                    "title": "Market Positioning Needs Improvement",
                    "description": "Your market positioning score is 4/10. Consider strategic adjustments.",
                    "priority": "high",
                    "estimated_impact": "significant",
                    "created_at": "2024-08-01T10:00:00Z"
                }
            },
            "market_positioning": {
                "description": "Market positioning analysis and scores",
                "structure": {
                    "industry_position": "string",
                    "competitive_advantage": "string",
                    "market_share": "string",
                    "positioning_score": "integer"
                },
                "example": {
                    "industry_position": "emerging",
                    "competitive_advantage": "AI-powered content",
                    "market_share": "2.5%",
                    "positioning_score": 4
                }
            },
            "strategic_scores": {
                "description": "Strategic performance scores",
                "structure": {
                    "overall_score": "float",
                    "content_quality_score": "float",
                    "engagement_score": "float",
                    "conversion_score": "float",
                    "innovation_score": "float"
                },
                "example": {
                    "overall_score": 7.2,
                    "content_quality_score": 8.1,
                    "engagement_score": 6.8,
                    "conversion_score": 7.5,
                    "innovation_score": 8.3
                }
            },
            "risk_assessment": {
                "description": "Strategic risk assessment",
                "structure": [
                    {
                        "type": "string",
                        "severity": "string",
                        "description": "string",
                        "mitigation_strategy": "string"
                    }
                ],
                "example": [
                    {
                        "type": "market_competition",
                        "severity": "medium",
                        "description": "Increasing competition in AI content space",
                        "mitigation_strategy": "Focus on unique value propositions"
                    }
                ]
            },
            "opportunity_analysis": {
                "description": "Strategic opportunity analysis",
                "structure": [
                    {
                        "title": "string",
                        "description": "string",
                        "estimated_impact": "string",
                        "implementation_difficulty": "string",
                        "timeline": "string"
                    }
                ],
                "example": [
                    {
                        "title": "Video Content Expansion",
                        "description": "Expand into video content to capture growing demand",
                        "estimated_impact": "high",
                        "implementation_difficulty": "medium",
                        "timeline": "3-6 months"
                    }
                ]
            },
            "recommendations": {
                "description": "AI-generated strategic recommendations",
                "structure": [
                    {
                        "id": "string",
                        "type": "string",
                        "title": "string",
                        "description": "string",
                        "priority": "string",
                        "estimated_impact": "string",
                        "action_items": "array"
                    }
                ],
                "example": [
                    {
                        "id": "rec_001",
                        "type": "content_strategy",
                        "title": "Implement AI-Powered Content Personalization",
                        "description": "Use AI to personalize content for different audience segments",
                        "priority": "high",
                        "estimated_impact": "significant",
                        "action_items": [
                            "Implement AI content recommendation engine",
                            "Create audience segmentation strategy",
                            "Develop personalized content templates"
                        ]
                    }
                ]
            }
        }
        
        logger.info(f"📊 Data Points Analysis: {len(data_points_analysis)} data point types identified")
        return data_points_analysis
    
    async def _analyze_frontend_mapping(self) -> Dict[str, Any]:
        """Analyze how backend data maps to frontend components."""
        logger.info("🖥️ Analyzing Frontend-Backend Data Mapping")
        
        frontend_mapping = {
            "dashboard_components": {
                "strategy_overview": {
                    "backend_data": "strategic_scores",
                    "frontend_component": "StrategyOverviewCard",
                    "data_mapping": {
                        "overall_score": "score",
                        "content_quality_score": "qualityScore",
                        "engagement_score": "engagementScore",
                        "conversion_score": "conversionScore"
                    }
                },
                "strategic_insights": {
                    "backend_data": "strategic_insights",
                    "frontend_component": "InsightsList",
                    "data_mapping": {
                        "title": "title",
                        "description": "description",
                        "priority": "priority",
                        "type": "type"
                    }
                },
                "market_positioning": {
                    "backend_data": "market_positioning",
                    "frontend_component": "MarketPositioningChart",
                    "data_mapping": {
                        "positioning_score": "score",
                        "industry_position": "position",
                        "competitive_advantage": "advantage"
                    }
                },
                "risk_assessment": {
                    "backend_data": "risk_assessment",
                    "frontend_component": "RiskAssessmentPanel",
                    "data_mapping": {
                        "type": "riskType",
                        "severity": "severity",
                        "description": "description",
                        "mitigation_strategy": "mitigation"
                    }
                },
                "opportunities": {
                    "backend_data": "opportunity_analysis",
                    "frontend_component": "OpportunitiesList",
                    "data_mapping": {
                        "title": "title",
                        "description": "description",
                        "estimated_impact": "impact",
                        "implementation_difficulty": "difficulty"
                    }
                },
                "recommendations": {
                    "backend_data": "recommendations",
                    "frontend_component": "RecommendationsPanel",
                    "data_mapping": {
                        "title": "title",
                        "description": "description",
                        "priority": "priority",
                        "action_items": "actions"
                    }
                }
            },
            "data_flow": {
                "api_endpoints": {
                    "get_strategies": "/api/content-planning/strategies/",
                    "get_strategy_by_id": "/api/content-planning/strategies/{id}",
                    "create_strategy": "/api/content-planning/strategies/",
                    "update_strategy": "/api/content-planning/strategies/{id}",
                    "delete_strategy": "/api/content-planning/strategies/{id}"
                },
                "response_structure": {
                    "status": "success/error",
                    "data": "strategy_data",
                    "message": "user_message",
                    "timestamp": "iso_datetime"
                }
            }
        }
        
        logger.info(f"🖥️ Frontend Mapping Analysis: {len(frontend_mapping['dashboard_components'])} components mapped")
        return frontend_mapping
    
    async def _run_comprehensive_tests(self) -> Dict[str, Any]:
        """Run comprehensive tests for content strategy functionality."""
        logger.info("🧪 Running Comprehensive Content Strategy Tests")
        
        test_results = {
            "test_cases": [],
            "summary": {
                "total_tests": 0,
                "passed": 0,
                "failed": 0,
                "success_rate": 0.0
            }
        }
        
        # Test Case 1: Strategy Creation
        test_case_1 = await self._test_strategy_creation()
        test_results["test_cases"].append(test_case_1)
        
        # Test Case 2: Strategy Retrieval
        test_case_2 = await self._test_strategy_retrieval()
        test_results["test_cases"].append(test_case_2)
        
        # Test Case 3: Strategic Intelligence Generation
        test_case_3 = await self._test_strategic_intelligence()
        test_results["test_cases"].append(test_case_3)
        
        # Test Case 4: Data Structure Validation
        test_case_4 = await self._test_data_structure_validation()
        test_results["test_cases"].append(test_case_4)
        
        # Calculate summary
        total_tests = len(test_results["test_cases"])
        passed_tests = sum(1 for test in test_results["test_cases"] if test["status"] == "passed")
        
        test_results["summary"] = {
            "total_tests": total_tests,
            "passed": passed_tests,
            "failed": total_tests - passed_tests,
            "success_rate": (passed_tests / total_tests * 100) if total_tests > 0 else 0.0
        }
        
        logger.info(f"🧪 Test Results: {passed_tests}/{total_tests} tests passed ({test_results['summary']['success_rate']:.1f}%)")
        return test_results
    
    async def _test_strategy_creation(self) -> Dict[str, Any]:
        """Test strategy creation functionality."""
        try:
            logger.info("Testing strategy creation...")
            
            # Simulate strategy creation
            strategy_data = {
                "user_id": 1,
                "name": "Test Digital Marketing Strategy",
                "industry": "technology",
                "target_audience": {
                    "demographics": ["professionals"],
                    "interests": ["digital_marketing"]
                },
                "content_pillars": [
                    {
                        "name": "Educational Content",
                        "description": "How-to guides and tutorials"
                    }
                ]
            }
            
            # Validate required fields
            required_fields = ["user_id", "name", "industry", "target_audience"]
            missing_fields = [field for field in required_fields if field not in strategy_data]
            
            if missing_fields:
                return {
                    "name": "Strategy Creation - Required Fields",
                    "status": "failed",
                    "error": f"Missing required fields: {missing_fields}"
                }
            
            return {
                "name": "Strategy Creation - Required Fields",
                "status": "passed",
                "message": "All required fields present"
            }
            
        except Exception as e:
            return {
                "name": "Strategy Creation",
                "status": "failed",
                "error": str(e)
            }
    
    async def _test_strategy_retrieval(self) -> Dict[str, Any]:
        """Test strategy retrieval functionality."""
        try:
            logger.info("Testing strategy retrieval...")
            
            # Simulate strategy retrieval
            user_id = 1
            strategy_id = 1
            
            # Validate query parameters
            if not isinstance(user_id, int) or user_id <= 0:
                return {
                    "name": "Strategy Retrieval - User ID Validation",
                    "status": "failed",
                    "error": "Invalid user_id"
                }
            
            return {
                "name": "Strategy Retrieval - User ID Validation",
                "status": "passed",
                "message": "User ID validation passed"
            }
            
        except Exception as e:
            return {
                "name": "Strategy Retrieval",
                "status": "failed",
                "error": str(e)
            }
    
    async def _test_strategic_intelligence(self) -> Dict[str, Any]:
        """Test strategic intelligence generation."""
        try:
            logger.info("Testing strategic intelligence generation...")
            
            # Expected strategic intelligence structure
            expected_structure = {
                "strategy_id": "integer",
                "market_positioning": "object",
                "competitive_advantages": "array",
                "strategic_scores": "object",
                "risk_assessment": "array",
                "opportunity_analysis": "array"
            }
            
            # Validate structure
            required_keys = list(expected_structure.keys())
            
            return {
                "name": "Strategic Intelligence - Structure Validation",
                "status": "passed",
                "message": f"Expected structure contains {len(required_keys)} required keys"
            }
            
        except Exception as e:
            return {
                "name": "Strategic Intelligence",
                "status": "failed",
                "error": str(e)
            }
    
    async def _test_data_structure_validation(self) -> Dict[str, Any]:
        """Test data structure validation."""
        try:
            logger.info("Testing data structure validation...")
            
            # Test strategic insights structure
            strategic_insight_structure = {
                "id": "string",
                "type": "string",
                "title": "string",
                "description": "string",
                "priority": "string",
                "created_at": "datetime"
            }
            
            # Test market positioning structure
            market_positioning_structure = {
                "industry_position": "string",
                "competitive_advantage": "string",
                "positioning_score": "integer"
            }
            
            # Validate both structures
            insight_keys = list(strategic_insight_structure.keys())
            positioning_keys = list(market_positioning_structure.keys())
            
            if len(insight_keys) >= 5 and len(positioning_keys) >= 3:
                return {
                    "name": "Data Structure Validation",
                    "status": "passed",
                    "message": "Data structures properly defined"
                }
            else:
                return {
                    "name": "Data Structure Validation",
                    "status": "failed",
                    "error": "Insufficient data structure definition"
                }
            
        except Exception as e:
            return {
                "name": "Data Structure Validation",
                "status": "failed",
                "error": str(e)
            }

async def main():
    """Main function to run content strategy analysis."""
    logger.info("🚀 Starting Content Strategy Analysis")
    
    analyzer = ContentStrategyAnalysis()
    results = await analyzer.analyze_content_strategy_flow()
    
    # Save results to file
    with open("content_strategy_analysis_results.json", "w") as f:
        json.dump(results, f, indent=2, default=str)
    
    logger.info("✅ Content Strategy Analysis completed and saved to content_strategy_analysis_results.json")
    
    # Print summary
    print("\n" + "="*60)
    print("📊 CONTENT STRATEGY ANALYSIS SUMMARY")
    print("="*60)
    
    test_results = results["analysis"]["test_results"]["summary"]
    print(f"🧪 Test Results: {test_results['passed']}/{test_results['total_tests']} passed ({test_results['success_rate']:.1f}%)")
    
    inputs_count = len(results["analysis"]["inputs"]["required_inputs"])
    data_points_count = len(results["analysis"]["data_points"])
    components_count = len(results["analysis"]["frontend_mapping"]["dashboard_components"])
    
    print(f"📋 Inputs Analyzed: {inputs_count} required inputs")
    print(f"📊 Data Points: {data_points_count} data point types")
    print(f"🖥️ Frontend Components: {components_count} components mapped")
    
    print("\n" + "="*60)
    print("✅ Content Strategy Phase Analysis Complete!")
    print("="*60)

if __name__ == "__main__":
    asyncio.run(main()) 