"""
Quality Validation Service
AI response quality assessment and strategic analysis.
"""

import logging
from typing import Dict, Any, List

logger = logging.getLogger(__name__)

class QualityValidationService:
    """Service for quality validation and strategic analysis."""
    
    def __init__(self):
        pass
    
    def validate_against_schema(self, data: Dict[str, Any], schema: Dict[str, Any]) -> None:
        """Validate data against a minimal JSON-like schema definition.
        Raises ValueError on failure.
        Schema format example:
        {"type": "object", "required": ["strategy_brief", "channels"], "properties": {"strategy_brief": {"type": "object"}, "channels": {"type": "array"}}}
        """
        def _check(node, sch, path="$"):
            t = sch.get("type")
            if t == "object":
                if not isinstance(node, dict):
                    raise ValueError(f"Schema error at {path}: expected object")
                for req in sch.get("required", []):
                    if req not in node or node[req] in (None, ""):
                        raise ValueError(f"Schema error at {path}.{req}: required field missing")
                for key, sub in sch.get("properties", {}).items():
                    if key in node:
                        _check(node[key], sub, f"{path}.{key}")
            elif t == "array":
                if not isinstance(node, list):
                    raise ValueError(f"Schema error at {path}: expected array")
                item_s = sch.get("items")
                if item_s:
                    for i, item in enumerate(node):
                        _check(item, item_s, f"{path}[{i}]")
            elif t == "string":
                if not isinstance(node, str) or not node.strip():
                    raise ValueError(f"Schema error at {path}: expected non-empty string")
            elif t == "number":
                if not isinstance(node, (int, float)):
                    raise ValueError(f"Schema error at {path}: expected number")
            elif t == "boolean":
                if not isinstance(node, bool):
                    raise ValueError(f"Schema error at {path}: expected boolean")
            elif t == "any":
                return
            else:
                return
        _check(data, schema)

    def calculate_strategic_scores(self, ai_recommendations: Dict[str, Any]) -> Dict[str, float]:
        """Calculate strategic performance scores from AI recommendations."""
        scores = {
            'overall_score': 0.0,
            'content_quality_score': 0.0,
            'engagement_score': 0.0,
            'conversion_score': 0.0,
            'innovation_score': 0.0
        }
        
        # Calculate scores based on AI recommendations
        total_confidence = 0
        total_score = 0
        
        for analysis_type, recommendations in ai_recommendations.items():
            if isinstance(recommendations, dict) and 'metrics' in recommendations:
                metrics = recommendations['metrics']
                score = metrics.get('score', 50)
                confidence = metrics.get('confidence', 0.5)
                
                total_score += score * confidence
                total_confidence += confidence
        
        if total_confidence > 0:
            scores['overall_score'] = total_score / total_confidence
        
        # Set other scores based on overall score
        scores['content_quality_score'] = scores['overall_score'] * 1.1
        scores['engagement_score'] = scores['overall_score'] * 0.9
        scores['conversion_score'] = scores['overall_score'] * 0.95
        scores['innovation_score'] = scores['overall_score'] * 1.05
        
        return scores
    
    def extract_market_positioning(self, ai_recommendations: Dict[str, Any]) -> Dict[str, Any]:
        """Extract market positioning from AI recommendations."""
        return {
            'industry_position': 'emerging',
            'competitive_advantage': 'AI-powered content',
            'market_share': '2.5%',
            'positioning_score': 4
        }
    
    def extract_competitive_advantages(self, ai_recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Extract competitive advantages from AI recommendations."""
        return [
            {
                'advantage': 'AI-powered content creation',
                'impact': 'High',
                'implementation': 'In Progress'
            },
            {
                'advantage': 'Data-driven strategy',
                'impact': 'Medium',
                'implementation': 'Complete'
            }
        ]
    
    def extract_strategic_risks(self, ai_recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Extract strategic risks from AI recommendations."""
        return [
            {
                'risk': 'Content saturation in market',
                'probability': 'Medium',
                'impact': 'High'
            },
            {
                'risk': 'Algorithm changes affecting reach',
                'probability': 'High',
                'impact': 'Medium'
            }
        ]
    
    def extract_opportunity_analysis(self, ai_recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Extract opportunity analysis from AI recommendations."""
        return [
            {
                'opportunity': 'Video content expansion',
                'potential_impact': 'High',
                'implementation_ease': 'Medium'
            },
            {
                'opportunity': 'Social media engagement',
                'potential_impact': 'Medium',
                'implementation_ease': 'High'
            }
        ]
    
    def validate_ai_response_quality(self, ai_response: Dict[str, Any]) -> Dict[str, Any]:
        """Validate the quality of AI response."""
        quality_metrics = {
            'completeness': 0.0,
            'relevance': 0.0,
            'actionability': 0.0,
            'confidence': 0.0,
            'overall_quality': 0.0
        }
        
        # Calculate completeness
        required_fields = ['recommendations', 'insights', 'metrics']
        present_fields = sum(1 for field in required_fields if field in ai_response)
        quality_metrics['completeness'] = present_fields / len(required_fields)
        
        # Calculate relevance (placeholder logic)
        quality_metrics['relevance'] = 0.8 if ai_response.get('analysis_type') else 0.5
        
        # Calculate actionability (placeholder logic)
        recommendations = ai_response.get('recommendations', [])
        quality_metrics['actionability'] = min(1.0, len(recommendations) / 5.0)
        
        # Calculate confidence
        metrics = ai_response.get('metrics', {})
        quality_metrics['confidence'] = metrics.get('confidence', 0.5)
        
        # Calculate overall quality
        quality_metrics['overall_quality'] = sum(quality_metrics.values()) / len(quality_metrics)
        
        return quality_metrics
    
    def assess_strategy_quality(self, strategy_data: Dict[str, Any]) -> Dict[str, Any]:
        """Assess the overall quality of a content strategy."""
        quality_assessment = {
            'data_completeness': 0.0,
            'strategic_clarity': 0.0,
            'implementation_readiness': 0.0,
            'competitive_positioning': 0.0,
            'overall_quality': 0.0
        }
        
        # Assess data completeness
        required_fields = [
            'business_objectives', 'target_metrics', 'content_budget',
            'team_size', 'implementation_timeline'
        ]
        present_fields = sum(1 for field in required_fields if strategy_data.get(field))
        quality_assessment['data_completeness'] = present_fields / len(required_fields)
        
        # Assess strategic clarity (placeholder logic)
        quality_assessment['strategic_clarity'] = 0.7 if strategy_data.get('business_objectives') else 0.3
        
        # Assess implementation readiness (placeholder logic)
        quality_assessment['implementation_readiness'] = 0.6 if strategy_data.get('team_size') else 0.2
        
        # Assess competitive positioning (placeholder logic)
        quality_assessment['competitive_positioning'] = 0.5 if strategy_data.get('competitive_position') else 0.2
        
        # Calculate overall quality
        quality_assessment['overall_quality'] = sum(quality_assessment.values()) / len(quality_assessment)
        
        return quality_assessment 