ALwrity version 0.5.5
This commit is contained in:
ajaysi
458
backend/api/content_planning/quality_analysis_routes.py
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458
backend/api/content_planning/quality_analysis_routes.py
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"""
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Quality Analysis API Routes
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Provides endpoints for AI-powered quality assessment and recommendations.
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"""
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from fastapi import APIRouter, HTTPException, Depends, Query
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from typing import Dict, Any, List
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import logging
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from datetime import datetime, timedelta
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from sqlalchemy.orm import Session
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from services.ai_quality_analysis_service import AIQualityAnalysisService, QualityAnalysisResult
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from services.database import get_db
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from models.enhanced_strategy_models import EnhancedContentStrategy
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/quality-analysis", tags=["quality-analysis"])
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@router.post("/{strategy_id}/analyze")
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async def analyze_strategy_quality(
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strategy_id: int,
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db: Session = Depends(get_db)
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):
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"""Analyze strategy quality using AI and return comprehensive results."""
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try:
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# Check if strategy exists
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strategy = db.query(EnhancedContentStrategy).filter(
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EnhancedContentStrategy.id == strategy_id
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).first()
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if not strategy:
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raise HTTPException(
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status_code=404,
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detail=f"Strategy with ID {strategy_id} not found"
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)
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# Initialize quality analysis service
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quality_service = AIQualityAnalysisService()
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# Perform quality analysis
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analysis_result = await quality_service.analyze_strategy_quality(strategy_id)
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# Convert result to dictionary for API response
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result_dict = {
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"strategy_id": analysis_result.strategy_id,
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"overall_score": analysis_result.overall_score,
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"overall_status": analysis_result.overall_status.value,
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"confidence_score": analysis_result.confidence_score,
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"analysis_timestamp": analysis_result.analysis_timestamp.isoformat(),
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"metrics": [
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{
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"name": metric.name,
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"score": metric.score,
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"weight": metric.weight,
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"status": metric.status.value,
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"description": metric.description,
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"recommendations": metric.recommendations
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}
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for metric in analysis_result.metrics
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],
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"recommendations": analysis_result.recommendations
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}
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logger.info(f"Quality analysis completed for strategy {strategy_id}")
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return {
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"success": True,
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"data": result_dict,
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"message": "Quality analysis completed successfully"
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}
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Error analyzing strategy quality for {strategy_id}: {e}")
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raise HTTPException(
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status_code=500,
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detail=f"Failed to analyze strategy quality: {str(e)}"
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)
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@router.get("/{strategy_id}/metrics")
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async def get_quality_metrics(
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strategy_id: int,
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db: Session = Depends(get_db)
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):
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"""Get quality metrics for a strategy."""
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try:
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# Check if strategy exists
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strategy = db.query(EnhancedContentStrategy).filter(
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EnhancedContentStrategy.id == strategy_id
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).first()
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if not strategy:
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raise HTTPException(
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status_code=404,
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detail=f"Strategy with ID {strategy_id} not found"
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)
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# Initialize quality analysis service
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quality_service = AIQualityAnalysisService()
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# Perform quick quality analysis (cached if available)
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analysis_result = await quality_service.analyze_strategy_quality(strategy_id)
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# Return metrics in a simplified format
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metrics_data = [
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{
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"name": metric.name,
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"score": metric.score,
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"status": metric.status.value,
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"description": metric.description
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}
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for metric in analysis_result.metrics
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]
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return {
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"success": True,
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"data": {
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"strategy_id": strategy_id,
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"overall_score": analysis_result.overall_score,
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"overall_status": analysis_result.overall_status.value,
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"metrics": metrics_data,
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"last_updated": analysis_result.analysis_timestamp.isoformat()
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},
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"message": "Quality metrics retrieved successfully"
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}
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Error getting quality metrics for {strategy_id}: {e}")
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raise HTTPException(
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status_code=500,
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detail=f"Failed to get quality metrics: {str(e)}"
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)
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@router.get("/{strategy_id}/recommendations")
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async def get_quality_recommendations(
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strategy_id: int,
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db: Session = Depends(get_db)
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):
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"""Get AI-powered quality improvement recommendations."""
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try:
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# Check if strategy exists
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strategy = db.query(EnhancedContentStrategy).filter(
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EnhancedContentStrategy.id == strategy_id
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).first()
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if not strategy:
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raise HTTPException(
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status_code=404,
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detail=f"Strategy with ID {strategy_id} not found"
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)
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# Initialize quality analysis service
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quality_service = AIQualityAnalysisService()
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# Perform quality analysis to get recommendations
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analysis_result = await quality_service.analyze_strategy_quality(strategy_id)
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# Get recommendations by category
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recommendations_by_category = {}
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for metric in analysis_result.metrics:
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if metric.recommendations:
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recommendations_by_category[metric.name] = metric.recommendations
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return {
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"success": True,
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"data": {
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"strategy_id": strategy_id,
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"overall_recommendations": analysis_result.recommendations,
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"recommendations_by_category": recommendations_by_category,
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"priority_areas": [
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metric.name for metric in analysis_result.metrics
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if metric.status.value in ["needs_attention", "poor"]
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],
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"last_updated": analysis_result.analysis_timestamp.isoformat()
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},
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"message": "Quality recommendations retrieved successfully"
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}
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Error getting quality recommendations for {strategy_id}: {e}")
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raise HTTPException(
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status_code=500,
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detail=f"Failed to get quality recommendations: {str(e)}"
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)
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@router.get("/{strategy_id}/history")
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async def get_quality_history(
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strategy_id: int,
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days: int = Query(30, description="Number of days to look back"),
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db: Session = Depends(get_db)
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):
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"""Get quality analysis history for a strategy."""
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try:
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# Check if strategy exists
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strategy = db.query(EnhancedContentStrategy).filter(
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EnhancedContentStrategy.id == strategy_id
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).first()
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if not strategy:
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raise HTTPException(
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status_code=404,
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detail=f"Strategy with ID {strategy_id} not found"
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)
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# Initialize quality analysis service
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quality_service = AIQualityAnalysisService()
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# Get quality history
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history = await quality_service.get_quality_history(strategy_id, days)
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# Convert history to API format
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history_data = [
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{
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"timestamp": result.analysis_timestamp.isoformat(),
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"overall_score": result.overall_score,
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"overall_status": result.overall_status.value,
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"confidence_score": result.confidence_score
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}
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for result in history
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]
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return {
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"success": True,
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"data": {
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"strategy_id": strategy_id,
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"history": history_data,
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"days": days,
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"total_analyses": len(history_data)
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},
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"message": "Quality history retrieved successfully"
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}
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Error getting quality history for {strategy_id}: {e}")
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raise HTTPException(
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status_code=500,
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detail=f"Failed to get quality history: {str(e)}"
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)
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@router.get("/{strategy_id}/trends")
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async def get_quality_trends(
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strategy_id: int,
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db: Session = Depends(get_db)
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):
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"""Get quality trends and patterns for a strategy."""
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try:
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# Check if strategy exists
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strategy = db.query(EnhancedContentStrategy).filter(
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EnhancedContentStrategy.id == strategy_id
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).first()
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if not strategy:
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raise HTTPException(
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status_code=404,
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detail=f"Strategy with ID {strategy_id} not found"
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)
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# Initialize quality analysis service
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quality_service = AIQualityAnalysisService()
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# Get quality trends
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trends = await quality_service.get_quality_trends(strategy_id)
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return {
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"success": True,
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"data": {
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"strategy_id": strategy_id,
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"trends": trends,
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"last_updated": datetime.utcnow().isoformat()
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},
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"message": "Quality trends retrieved successfully"
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}
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Error getting quality trends for {strategy_id}: {e}")
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raise HTTPException(
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status_code=500,
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detail=f"Failed to get quality trends: {str(e)}"
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)
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@router.post("/{strategy_id}/quick-assessment")
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async def quick_quality_assessment(
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strategy_id: int,
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db: Session = Depends(get_db)
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):
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"""Perform a quick quality assessment without full AI analysis."""
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try:
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# Check if strategy exists
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strategy = db.query(EnhancedContentStrategy).filter(
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EnhancedContentStrategy.id == strategy_id
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).first()
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if not strategy:
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raise HTTPException(
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status_code=404,
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detail=f"Strategy with ID {strategy_id} not found"
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)
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# Perform quick assessment based on data completeness
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completeness_score = self._calculate_completeness_score(strategy)
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# Determine status based on score
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if completeness_score >= 80:
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status = "excellent"
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elif completeness_score >= 65:
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status = "good"
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elif completeness_score >= 45:
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status = "needs_attention"
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else:
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status = "poor"
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return {
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"success": True,
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"data": {
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"strategy_id": strategy_id,
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"completeness_score": completeness_score,
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"status": status,
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"assessment_type": "quick",
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"timestamp": datetime.utcnow().isoformat(),
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"message": "Quick assessment completed based on data completeness"
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},
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"message": "Quick quality assessment completed"
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}
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Error performing quick assessment for {strategy_id}: {e}")
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raise HTTPException(
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status_code=500,
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detail=f"Failed to perform quick assessment: {str(e)}"
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)
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def _calculate_completeness_score(self, strategy: EnhancedContentStrategy) -> float:
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"""Calculate completeness score based on filled fields."""
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try:
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# Define required fields for each category
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required_fields = {
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"business_context": [
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'business_objectives', 'target_metrics', 'content_budget',
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'team_size', 'implementation_timeline', 'market_share'
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],
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"audience_intelligence": [
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'content_preferences', 'consumption_patterns', 'audience_pain_points',
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'buying_journey', 'seasonal_trends', 'engagement_metrics'
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],
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"competitive_intelligence": [
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'top_competitors', 'competitor_content_strategies', 'market_gaps',
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'industry_trends', 'emerging_trends'
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],
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"content_strategy": [
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'preferred_formats', 'content_mix', 'content_frequency',
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'optimal_timing', 'quality_metrics', 'editorial_guidelines', 'brand_voice'
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],
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"performance_analytics": [
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'traffic_sources', 'conversion_rates', 'content_roi_targets',
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'ab_testing_capabilities'
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]
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}
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total_fields = 0
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filled_fields = 0
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for category, fields in required_fields.items():
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total_fields += len(fields)
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for field in fields:
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if hasattr(strategy, field) and getattr(strategy, field) is not None:
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filled_fields += 1
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if total_fields == 0:
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return 0.0
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return (filled_fields / total_fields) * 100
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except Exception as e:
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logger.error(f"Error calculating completeness score: {e}")
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return 0.0
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@router.get("/{strategy_id}/dashboard")
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async def get_quality_dashboard(
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strategy_id: int,
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db: Session = Depends(get_db)
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):
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"""Get comprehensive quality dashboard data."""
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try:
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# Check if strategy exists
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strategy = db.query(EnhancedContentStrategy).filter(
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EnhancedContentStrategy.id == strategy_id
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).first()
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if not strategy:
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raise HTTPException(
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status_code=404,
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detail=f"Strategy with ID {strategy_id} not found"
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)
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# Initialize quality analysis service
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quality_service = AIQualityAnalysisService()
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# Get comprehensive analysis
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analysis_result = await quality_service.analyze_strategy_quality(strategy_id)
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# Get trends
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trends = await quality_service.get_quality_trends(strategy_id)
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# Prepare dashboard data
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dashboard_data = {
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"strategy_id": strategy_id,
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"overall_score": analysis_result.overall_score,
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"overall_status": analysis_result.overall_status.value,
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"confidence_score": analysis_result.confidence_score,
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"metrics": [
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{
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"name": metric.name,
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"score": metric.score,
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"status": metric.status.value,
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"description": metric.description,
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"recommendations": metric.recommendations
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}
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for metric in analysis_result.metrics
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],
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"recommendations": analysis_result.recommendations,
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"trends": trends,
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"priority_areas": [
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metric.name for metric in analysis_result.metrics
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if metric.status.value in ["needs_attention", "poor"]
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],
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"strengths": [
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metric.name for metric in analysis_result.metrics
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if metric.status.value == "excellent"
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],
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"last_updated": analysis_result.analysis_timestamp.isoformat()
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}
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return {
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"success": True,
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"data": dashboard_data,
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"message": "Quality dashboard data retrieved successfully"
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}
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Error getting quality dashboard for {strategy_id}: {e}")
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raise HTTPException(
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status_code=500,
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detail=f"Failed to get quality dashboard: {str(e)}"
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)
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