content_quality_tool/core/views.py

# views.py (FULLY FIXED with comprehensive logging)
from django.shortcuts import render, get_object_or_404
from django.http import HttpResponse, JsonResponse
from django.views import View
from django.core.cache import cache
from django.db.models import Q
from django.views.decorators.csrf import csrf_exempt
from django.utils.decorators import method_decorator
from django.conf import settings
import json
import logging
# logging.basicConfig(
#     level=logging.INFO,  # or DEBUG if you want more detail
#     format="%(asctime)s [%(levelname)s] %(name)s: %(message)s"
# )
import os
from core.services.image_scorer import ImageQualityScorer
from core.models import AttributeScore, CategoryAttributeRule, ProductContentRule, Product
from core.services.attribute_scorer import AttributeQualityScorer

logger = logging.getLogger(__name__)


def categorize_issues_and_suggestions(issues, suggestions):
    """Categorize issues and suggestions by component"""
    categorized = {
        'attributes': {'issues': [], 'suggestions': []},
        'title': {'issues': [], 'suggestions': []},
        'description': {'issues': [], 'suggestions': []},
        'seo': {'issues': [], 'suggestions': []},
        'content_rules': {'issues': [], 'suggestions': []},
        'images': {'issues': [], 'suggestions': []},
        'general': {'issues': [], 'suggestions': []}
    }
    
    for issue in issues:
        issue_lower = issue.lower()
        
        if issue.startswith('Title:'):
            categorized['title']['issues'].append(issue.replace('Title:', '').strip())
        elif issue.startswith('Description:'):
            categorized['description']['issues'].append(issue.replace('Description:', '').strip())
        elif issue.startswith('SEO:'):
            categorized['seo']['issues'].append(issue.replace('SEO:', '').strip())
        elif issue.startswith('Image:'):
            categorized['images']['issues'].append(issue.replace('Image:', '').strip())
        elif any(keyword in issue_lower for keyword in ['image', 'photo', 'picture', 'resolution', 'blur', 'background']):
            categorized['images']['issues'].append(issue)
        elif any(field in issue_lower for field in ['seo_title', 'seo title', 'seo_description', 'seo description']):
            categorized['seo']['issues'].append(issue)
        elif 'content rule' in issue_lower:
            categorized['content_rules']['issues'].append(issue)
        elif any(keyword in issue_lower for keyword in ['mandatory field', 'attribute', 'valid values', 'placeholder', 'standardiz']):
            categorized['attributes']['issues'].append(issue)
        else:
            categorized['general']['issues'].append(issue)
    
    for suggestion in suggestions:
        suggestion_lower = suggestion.lower()
        
        if any(prefix in suggestion_lower for prefix in ['title:', 'expand title', 'shorten title', 'add brand name']):
            categorized['title']['suggestions'].append(suggestion)
        elif any(prefix in suggestion_lower for prefix in ['description:', 'expand description', 'write comprehensive', 'add features']):
            categorized['description']['suggestions'].append(suggestion)
        elif any(prefix in suggestion_lower for prefix in ['seo:', 'improve seo', 'add keywords', 'search', 'discoverability']):
            categorized['seo']['suggestions'].append(suggestion)
        elif any(keyword in suggestion_lower for keyword in ['image', 'photo', 'resolution', 'compress', 'background', 'white background']):
            categorized['images']['suggestions'].append(suggestion)
        elif any(field in suggestion_lower for field in ['seo_title', 'seo title', 'seo_description', 'seo description']):
            categorized['seo']['suggestions'].append(suggestion)
        elif 'content rule' in suggestion_lower:
            categorized['content_rules']['suggestions'].append(suggestion)
        elif any(keyword in suggestion_lower for keyword in ['add required', 'add mandatory', 'provide a', 'attribute', 'standardize']):
            categorized['attributes']['suggestions'].append(suggestion)
        else:
            categorized['general']['suggestions'].append(suggestion)
    
    return {k: v for k, v in categorized.items() if v['issues'] or v['suggestions']}


def resolve_image_path(image_rel_path):
    """
    Resolve relative image path to full absolute path with comprehensive logging
    """
    logger.info(f"[IMAGE PATH] Starting resolution for: {image_rel_path}")
    
    if not image_rel_path:
        logger.warning("[IMAGE PATH] No image path provided")
        return None
    
    # Log settings
    logger.info(f"[IMAGE PATH] MEDIA_ROOT: {settings.MEDIA_ROOT}")
    logger.info(f"[IMAGE PATH] MEDIA_URL: {settings.MEDIA_URL}")
    
    # Remove MEDIA_URL prefix if present
    cleaned_path = image_rel_path.replace(settings.MEDIA_URL, "").lstrip("/\\")
    logger.info(f"[IMAGE PATH] Cleaned path: {cleaned_path}")
    
    # Build full path
    full_path = os.path.join(settings.MEDIA_ROOT, cleaned_path)
    logger.info(f"[IMAGE PATH] Full path before normalization: {full_path}")
    
    # Normalize path (resolve .., ., etc.)
    full_path = os.path.normpath(full_path)
    logger.info(f"[IMAGE PATH] Normalized path: {full_path}")
    
    # Security check
    media_root_normalized = os.path.normpath(settings.MEDIA_ROOT)
    logger.info(f"[IMAGE PATH] Normalized MEDIA_ROOT: {media_root_normalized}")
    
    if not full_path.startswith(media_root_normalized):
        logger.error(f"[IMAGE PATH] SECURITY: Path outside MEDIA_ROOT! Path: {full_path}, MEDIA_ROOT: {media_root_normalized}")
        return None
    
    # Check if file exists
    if not os.path.exists(full_path):
        logger.error(f"[IMAGE PATH] FILE NOT FOUND: {full_path}")
        # List directory contents for debugging
        dir_path = os.path.dirname(full_path)
        if os.path.exists(dir_path):
            files = os.listdir(dir_path)
            logger.info(f"[IMAGE PATH] Files in {dir_path}: {files}")
        else:
            logger.error(f"[IMAGE PATH] Directory does not exist: {dir_path}")
        return None
    
    logger.info(f"[IMAGE PATH] ✓ Image file found and validated: {full_path}")
    return full_path


@method_decorator(csrf_exempt, name='dispatch')
class AttributeScoreView(View):
    """Enhanced API view with Image scoring support"""

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.scorer = AttributeQualityScorer(use_ai=True)
        self.image_scorer = ImageQualityScorer(use_ai=True)
        logger.info("[INIT] AttributeScoreView initialized with image scorer")

    def post(self, request, *args, **kwargs):
        """Score a single product with image scoring"""
        logger.info("[POST] ========== NEW SCORING REQUEST ==========")
        
        try:
            data = json.loads(request.body)
            logger.info(f"[POST] Request data keys: {data.keys()}")
            
            product_data = data.get('product', {})
            logger.info(f"[POST] Product data keys: {product_data.keys()}")
            
            sku = product_data.get('sku')
            use_ai = data.get('use_ai', True)

            if not sku:
                logger.error("[POST] SKU is missing")
                return JsonResponse({'error': 'SKU is required'}, status=400)

            logger.info(f"[POST] Processing SKU: {sku}")

            category = product_data.get('category', '')
            if not category:
                logger.error(f"[POST] Category missing for SKU: {sku}")
                return JsonResponse({'error': 'Category is required'}, status=400)

            logger.info(f"[POST] Category: {category}")

            # Get or create product
            product, created = Product.objects.get_or_create(
                sku=sku,
                defaults={
                    'title': product_data.get('title', ''),
                    'description': product_data.get('description', ''),
                    'short_description': product_data.get('short_description', ''),
                    'seo_title': product_data.get('seo_title', ''),
                    'seo_description': product_data.get('seo_description', ''),
                    'category': category,
                    'attributes': product_data.get('attributes', {})
                }
            )
            logger.info(f"[POST] Product {'created' if created else 'retrieved'}: {sku}")

            if not created:
                product.title = product_data.get('title', product.title)
                product.description = product_data.get('description', product.description)
                product.short_description = product_data.get('short_description', product.short_description)
                product.seo_title = product_data.get('seo_title', product.seo_title)
                product.seo_description = product_data.get('seo_description', product.seo_description)
                product.attributes = product_data.get('attributes', product.attributes)
                product.save()
                logger.info(f"[POST] Product updated: {sku}")

            # Get rules
            cache_key = f"category_rules_{category}"
            category_rules = cache.get(cache_key)
            if category_rules is None:
                category_rules = list(CategoryAttributeRule.objects.filter(category=category).values())
                cache.set(cache_key, category_rules, 3600)
                logger.info(f"[POST] Loaded {len(category_rules)} category rules for {category}")
            
            if not category_rules:
                logger.error(f"[POST] No attribute rules for category: {category}")
                return JsonResponse({'error': f'No attribute rules defined for {category}'}, status=400)

            # Get content rules
            content_cache_key = f"content_rules_{category}"
            content_rules = cache.get(content_cache_key)
            if content_rules is None:
                content_rules = list(
                    ProductContentRule.objects.filter(
                        Q(category__isnull=True) | Q(category=category)
                    ).values()
                )
                cache.set(content_cache_key, content_rules, 3600)
                logger.info(f"[POST] Loaded {len(content_rules)} content rules for {category}")

            # Build product dict
            product_dict = {
                'sku': product.sku,
                'category': product.category,
                'title': product.title,
                'description': product.description,
                'short_description': product.short_description,
                'seo_title': product.seo_title,
                'seo_description': product.seo_description,
                'attributes': product.attributes
            }

            logger.info("[POST] Starting attribute and content scoring...")
            # Score product attributes and content
            score_result = self.scorer.score_product(
                product_dict,
                category_rules,
                content_rules=content_rules,
                generate_ai_suggestions=use_ai
            )
            logger.info(f"[POST] Attribute scoring complete. Score: {score_result.get('final_score')}")

            # ========== IMAGE SCORING ==========
            logger.info("[POST] ========== STARTING IMAGE SCORING ==========")
            image_score_result = None
            
            # Check for image_path in product_data
            image_rel_path = product_data.get('image_path')
            logger.info(f"[POST] Image path from payload: {image_rel_path}")
            
            if image_rel_path:
                image_path = resolve_image_path(image_rel_path)
                
                if image_path:
                    try:
                        logger.info(f"[POST] Calling image scorer for: {image_path}")
                        image_score_result = self.image_scorer.score_image(
                            product_dict,
                            image_path=image_path
                        )
                        logger.info(f"[POST] ✓ Image scoring complete. Score: {image_score_result.get('image_score')}")
                        logger.info(f"[POST] Image issues: {len(image_score_result.get('issues', []))}")
                        logger.info(f"[POST] Image suggestions: {len(image_score_result.get('suggestions', []))}")
                    except Exception as img_err:
                        logger.error(f"[POST] ✗ Image scoring FAILED: {img_err}", exc_info=True)
                        image_score_result = {
                            'image_score': None,
                            'breakdown': {},
                            'issues': [f'Image scoring error: {str(img_err)}'],
                            'suggestions': ['Check image file format and accessibility'],
                            'image_metadata': {}
                        }
                else:
                    logger.warning(f"[POST] Image path resolution failed for: {image_rel_path}")
                    image_score_result = {
                        'image_score': None,
                        'breakdown': {},
                        'issues': ['Image file not found or inaccessible'],
                        'suggestions': [f'Verify image exists at path: {image_rel_path}'],
                        'image_metadata': {}
                    }
            else:
                logger.warning("[POST] No image_path provided in payload")
                image_score_result = {
                    'image_score': None,
                    'breakdown': {},
                    'issues': ['No image provided'],
                    'suggestions': ['Upload a product image to improve quality score'],
                    'image_metadata': {}
                }

            logger.info("[POST] ========== IMAGE SCORING COMPLETE ==========")

            # Combine issues and suggestions
            all_issues = score_result['issues'] + (image_score_result.get('issues', []) if image_score_result else [])
            all_suggestions = score_result['suggestions'] + (image_score_result.get('suggestions', []) if image_score_result else [])

            logger.info(f"[POST] Total issues: {len(all_issues)} (Attributes: {len(score_result['issues'])}, Images: {len(image_score_result.get('issues', []))})")
            logger.info(f"[POST] Total suggestions: {len(all_suggestions)}")

            # Categorize feedback
            categorized = categorize_issues_and_suggestions(all_issues, all_suggestions)
            logger.info(f"[POST] Categorized feedback into {len(categorized)} categories")

            # Save score
            AttributeScore.objects.create(
                product=product,
                score=score_result['final_score'],
                max_score=score_result['max_score'],
                details={
                    **score_result['breakdown'],
                    'image_score': image_score_result.get('image_score') if image_score_result else None,
                    'image_breakdown': image_score_result.get('breakdown', {}) if image_score_result else {}
                },
                issues=all_issues,
                suggestions=all_suggestions,
                ai_suggestions=score_result.get('ai_suggestions', {}),
                processing_time=score_result.get('processing_time', 0)
            )
            logger.info(f"[POST] Score saved to database for SKU: {sku}")

            # Enhanced response
            response_data = {
                'success': True,
                'product_sku': sku,
                'created': created,
                'score_result': {
                    'final_score': score_result['final_score'],
                    'max_score': score_result['max_score'],
                    'breakdown': score_result['breakdown'],
                    'image_score': image_score_result.get('image_score') if image_score_result else None,
                    'image_breakdown': image_score_result.get('breakdown', {}) if image_score_result else {},
                    'image_metadata': image_score_result.get('image_metadata', {}) if image_score_result else {},
                    'categorized_feedback': categorized,
                    'ai_suggestions': {
                        'content': score_result.get('ai_suggestions', {}),
                        'image': image_score_result.get('ai_improvements') if image_score_result else None
                    },
                    'processing_time': score_result.get('processing_time', 0),
                    'issues': all_issues,
                    'suggestions': all_suggestions
                }
            }
            
            logger.info(f"[POST] ========== REQUEST COMPLETE: {sku} ==========")
            return JsonResponse(response_data)

        except json.JSONDecodeError as e:
            logger.error(f"[POST] JSON decode error: {e}", exc_info=True)
            return JsonResponse({'error': 'Invalid JSON'}, status=400)
        except Exception as e:
            logger.error(f"[POST] Unexpected error: {str(e)}", exc_info=True)
            return JsonResponse({'error': str(e)}, status=500)


@method_decorator(csrf_exempt, name='dispatch')
class BatchScoreView(View):
    """Batch scoring with image support"""

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.attribute_scorer = AttributeQualityScorer(use_ai=True)
        self.image_scorer = ImageQualityScorer(use_ai=True)
        logger.info("[INIT] BatchScoreView initialized")


    def get(self, request):
        """
        Automatically score all products from the database (no payload needed).
        """
        logger.info("[BATCH][GET] ========== AUTO BATCH SCORING STARTED ==========")
        try:
            # Fetch all products
            products = list(Product.objects.all().values())
            logger.info(f"[BATCH][GET] Found {len(products)} products in DB")

            if not products:
                return JsonResponse({'error': 'No products found in the database.'}, status=404)

            results = []
            errors = []

            for idx, product_data in enumerate(products, 1):
                sku = product_data.get('sku')
                category = product_data.get('category')

                logger.info(f"[BATCH][GET] [{idx}/{len(products)}] Processing: {sku}")

                if not sku or not category:
                    errors.append({'sku': sku, 'error': 'Missing SKU or category'})
                    continue

                try:
                    # Load rules
                    category_rules = list(CategoryAttributeRule.objects.filter(category=category).values())
                    if not category_rules:
                        errors.append({'sku': sku, 'error': f'No attribute rules for category {category}'})
                        continue

                    content_rules = list(
                        ProductContentRule.objects.filter(
                            Q(category__isnull=True) | Q(category=category)
                        ).values()
                    )

                    # Score product attributes/content
                    score_result = self.attribute_scorer.score_product(
                        product_data,
                        category_rules,
                        content_rules=content_rules,
                        generate_ai_suggestions=True
                    )

                    # Score product image
                    image_path = resolve_image_path(product_data.get('image_path'))
                    image_score_result = None

                    if image_path:
                        try:
                            logger.info(f"[BATCH][GET] Scoring image for {sku}")
                            image_score_result = self.image_scorer.score_image(
                                product_data,
                                image_path=image_path
                            )
                        except Exception as img_err:
                            logger.warning(f"[BATCH][GET] Image scoring failed for {sku}: {img_err}")
                            image_score_result = {
                                'image_score': None,
                                'breakdown': {},
                                'issues': ['Image scoring failed'],
                                'suggestions': ['Check image file'],
                                'image_metadata': {}
                            }
                    else:
                        image_score_result = {
                            'image_score': None,
                            'breakdown': {},
                            'issues': ['No image provided'],
                            'suggestions': ['Upload product image'],
                            'image_metadata': {}
                        }

                    # Combine issues and suggestions
                    all_issues = score_result['issues'] + image_score_result.get('issues', [])
                    all_suggestions = score_result['suggestions'] + image_score_result.get('suggestions', [])
                    categorized = categorize_issues_and_suggestions(all_issues, all_suggestions)
                    
                    score_result['breakdown']['image_score'] =  image_score_result.get('image_score')
                    score_result['breakdown']['attributes'] =  score_result['breakdown']['mandatory_fields']
                    
                    results.append({
                        'sku': sku,
                        'title': product_data['title'],
                        'description': product_data['description'],
                        'image_path': product_data['image_path'],
                        'created_at': product_data['created_at'],
                        'final_score': score_result['final_score'],
                        'max_score': score_result['max_score'],
                        'breakdown': score_result['breakdown'],
                        'image_score': image_score_result.get('image_score'),
                        'image_breakdown': image_score_result.get('breakdown', {}),
                        'image_metadata': image_score_result.get('image_metadata', {}),
                        'ai_suggestions': {
                            'content': score_result.get('ai_suggestions', {}),
                            'image': image_score_result.get('ai_improvements', {})
                        },
                        'categorized_feedback': categorized,
                        'processing_time': score_result.get('processing_time', 0),
                        'issues': all_issues,
                        'suggestions': all_suggestions
                    })




                except Exception as e:
                    logger.error(f"[BATCH][GET] Error scoring {sku}: {str(e)}", exc_info=True)
                    errors.append({'sku': sku, 'error': str(e)})

            logger.info(f"[BATCH][GET] ===== COMPLETE: {len(results)} success, {len(errors)} errors =====")
            return JsonResponse({
                'success': True,
                'processed': len(results),
                'results': results,
                'errors': errors
            })

        except Exception as e:
            logger.error(f"[BATCH][GET] Batch scoring error: {str(e)}", exc_info=True)
            return JsonResponse({'error': str(e)}, status=500)


    def post(self, request):
        logger.info("[BATCH] ========== NEW BATCH REQUEST ==========")
        
        try:
            data = json.loads(request.body)
            products = data.get('products', [])
            
            logger.info(f"[BATCH] Processing {len(products)} products")
            print(products)
            if not products:
                return JsonResponse({'error': 'No products provided'}, status=400)

            results = []
            errors = []

            for idx, product_data in enumerate(products[:100], 1):
                sku = product_data.get('sku')
                
                category = product_data.get('category')
                
                logger.info(f"[BATCH] [{idx}/{len(products)}] Processing: {sku}")

                if not sku or not category:
                    errors.append({'sku': sku, 'error': 'Missing SKU or category'})
                    continue

                try:
                    # Get rules
                    category_rules = list(CategoryAttributeRule.objects.filter(category=category).values())
                    if not category_rules:
                        errors.append({'sku': sku, 'error': f'No attribute rules for category {category}'})
                        continue

                    content_rules = list(
                        ProductContentRule.objects.filter(
                            Q(category__isnull=True) | Q(category=category)
                        ).values()
                    )

                    # Score attributes
                    score_result = self.attribute_scorer.score_product(
                        product_data,
                        category_rules,
                        content_rules=content_rules,
                        generate_ai_suggestions=True
                    )

                    # Score image
                    image_path = resolve_image_path(product_data.get('image_path'))
                    image_score_result = None

                    if image_path:
                        try:
                            logger.info(f"[BATCH] Scoring image for {sku}")
                            image_score_result = self.image_scorer.score_image(
                                product_data,
                                image_path=image_path
                            )
                            logger.info(f"[BATCH] Image score for {sku}: {image_score_result.get('image_score')}")
                        except Exception as img_err:
                            logger.warning(f"[BATCH] Image scoring failed for {sku}: {img_err}")
                            image_score_result = {
                                'image_score': None,
                                'breakdown': {},
                                'issues': ['Image scoring failed'],
                                'suggestions': ['Check image file'],
                                'image_metadata': {}
                            }
                    else:
                        logger.info(f"[BATCH] No valid image for {sku}")
                        image_score_result = {
                            'image_score': None,
                            'breakdown': {},
                            'issues': ['No image provided'],
                            'suggestions': ['Upload product image'],
                            'image_metadata': {}
                        }

                    all_issues = score_result['issues'] + image_score_result.get('issues', [])
                    all_suggestions = score_result['suggestions'] + image_score_result.get('suggestions', [])
                    categorized = categorize_issues_and_suggestions(all_issues, all_suggestions)

                    results.append({
                        'sku': sku,
                        'title': product_data['title'],
                        'description': product_data['description'],
                        'image_path': image_path,
                        'final_score': score_result['final_score'],
                        'max_score': score_result['max_score'],
                        'breakdown': score_result['breakdown'],
                        'image_score': image_score_result.get('image_score'),
                        'image_breakdown': image_score_result.get('breakdown', {}),
                        'image_metadata': image_score_result.get('image_metadata', {}),
                        'ai_suggestions': {
                            'content': score_result.get('ai_suggestions', {}),
                            'image': image_score_result.get('ai_improvements', {})
                        },
                        'categorized_feedback': categorized,
                        'processing_time': score_result.get('processing_time', 0),
                        'issues': all_issues,
                        'suggestions': all_suggestions
                    })

                except Exception as e:
                    logger.error(f"[BATCH] Error scoring {sku}: {str(e)}", exc_info=True)
                    errors.append({'sku': sku, 'error': str(e)})

            logger.info(f"[BATCH] ========== BATCH COMPLETE: {len(results)} success, {len(errors)} errors ==========")
            
            return JsonResponse({
                'success': True,
                'processed': len(results),
                'results': results,
                'errors': errors
            })

        except Exception as e:
            logger.error(f"[BATCH] Batch scoring error: {str(e)}", exc_info=True)
            return JsonResponse({'error': str(e)}, status=500)



import json
import logging
import concurrent.futures
from django.http import JsonResponse
from django.utils.decorators import method_decorator
from django.views import View
from django.views.decorators.csrf import csrf_exempt
from django.db.models import Q
from textblob import download_corpora
from concurrent.futures import ThreadPoolExecutor, as_completed
import time

from core.models import Product, CategoryAttributeRule, ProductContentRule
# from .scorers.attribute_scorer import AttributeQualityScorer
# from .scorers.image_scorer import ImageQualityScorer
# from .utils import resolve_image_path, categorize_issues_and_suggestions

logger = logging.getLogger(__name__)

# Try to ensure corpora are downloaded once at import
try:
    download_corpora.download_all()
    logger.info("[INIT] TextBlob corpora verified.")
except Exception as e:
    logger.warning(f"[INIT] Failed to auto-download TextBlob corpora: {e}")


@method_decorator(csrf_exempt, name='dispatch')
class BatchScoreViewV2(View):
    """Optimized Batch scoring with image support and parallel processing"""

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.attribute_scorer = AttributeQualityScorer(use_ai=True)
        self.image_scorer = ImageQualityScorer(use_ai=True)
        logger.info("[INIT] BatchScoreView initialized with AI scorers preloaded")

    def _process_single_product(self, product_data, idx, total):
        """Process a single product safely (run inside thread pool)"""
        start = time.time()
        sku = product_data.get('sku')
        category = product_data.get('category')
        img_raw_path = product_data.get('image_path')
        if not sku or not category:
            return {'sku': sku, 'error': 'Missing SKU or category'}

        try:
            logger.info(f"[BATCH][{idx}/{total}] Processing SKU: {sku}")

            # Fetch rules
            category_rules = list(CategoryAttributeRule.objects.filter(category=category).values())
            if not category_rules:
                return {'sku': sku, 'error': f'No attribute rules for category {category}'}

            content_rules = list(
                ProductContentRule.objects.filter(
                    Q(category__isnull=True) | Q(category=category)
                ).values()
            )

            # Score attributes
            score_result = self.attribute_scorer.score_product(
                product_data,
                category_rules,
                content_rules=content_rules,
                generate_ai_suggestions=True
            )

            # Score image
            image_path = resolve_image_path(product_data.get('image_path'))
            image_score_result = None

            if image_path:
                try:
                    image_score_result = self.image_scorer.score_image(
                        product_data,
                        image_path=image_path
                    )
                except Exception as img_err:
                    logger.warning(f"[IMG] Scoring failed for {sku}: {img_err}")
                    image_score_result = {
                        'image_score': None,
                        'breakdown': {},
                        'issues': ['Image scoring failed'],
                        'suggestions': ['Check image file'],
                        'image_metadata': {}
                    }
            else:
                image_score_result = {
                    'image_score': None,
                    'breakdown': {},
                    'issues': ['No image provided'],
                    'suggestions': ['Upload product image'],
                    'image_metadata': {}
                }

            # Combine results
            all_issues = score_result['issues'] + image_score_result.get('issues', [])
            all_suggestions = score_result['suggestions'] + image_score_result.get('suggestions', [])
            categorized = categorize_issues_and_suggestions(all_issues, all_suggestions)

            total_time = round(time.time() - start, 2)

            score_result['breakdown']['attributes'] =  score_result['breakdown']['mandatory_fields']

            return {
                'sku': sku,
                'title': product_data.get('title'),
                'description': product_data.get('description'),
                'image_path': img_raw_path,
                'final_score': score_result['final_score'],
                'max_score': score_result['max_score'],
                'breakdown': {**score_result['breakdown'], 'image_score': image_score_result.get('image_score')},
                'image_score': image_score_result.get('image_score'),
                'image_breakdown': image_score_result.get('breakdown', {}),
                'image_metadata': image_score_result.get('image_metadata', {}),
                'ai_suggestions': {
                    'content': score_result.get('ai_suggestions', {}),
                    'image': image_score_result.get('ai_improvements', {})
                },
                'categorized_feedback': categorized,
                'issues': all_issues,
                'suggestions': all_suggestions,
                'processing_time': total_time
            }




        except Exception as e:
            logger.error(f"[BATCH][{sku}] Error: {e}", exc_info=True)
            return {'sku': sku, 'error': str(e)}

    def _run_batch(self, products):
        """Run products in parallel using thread pool"""
        results, errors = [], []
        total = len(products)
        start_time = time.time()

        # Use up to 8 threads (tune based on your CPU)
        with ThreadPoolExecutor(max_workers=8) as executor:
            future_to_product = {
                executor.submit(self._process_single_product, product, idx + 1, total): product
                for idx, product in enumerate(products)
            }

            for future in as_completed(future_to_product):
                res = future.result()
                if "error" in res:
                    errors.append(res)
                else:
                    results.append(res)

        elapsed = round(time.time() - start_time, 2)
        logger.info(f"[BATCH] Completed {len(results)} products in {elapsed}s ({len(errors)} errors)")
        return results, errors, elapsed

    def get(self, request):
        """Score all products from DB automatically"""
        try:
            products = list(Product.objects.all().values())
            if not products:
                return JsonResponse({'error': 'No products found in database'}, status=404)

            results, errors, elapsed = self._run_batch(products)
            return JsonResponse({
                'success': True,
                'processed': len(results),
                'results': results,
                'errors': errors,
                'elapsed_seconds': elapsed
            })

        except Exception as e:
            logger.error(f"[BATCH][GET] Error: {e}", exc_info=True)
            return JsonResponse({'error': str(e)}, status=500)

    def post(self, request):
        """Batch score via payload"""
        try:
            data = json.loads(request.body)
            products = data.get('products', [])
            if not products:
                return JsonResponse({'error': 'No products provided'}, status=400)

            results, errors, elapsed = self._run_batch(products)
            return JsonResponse({
                'success': True,
                'processed': len(results),
                'results': results,
                'errors': errors,
                'elapsed_seconds': elapsed
            })

        except Exception as e:
            logger.error(f"[BATCH][POST] Error: {e}", exc_info=True)
            return JsonResponse({'error': str(e)}, status=500)







@method_decorator(csrf_exempt, name='dispatch')
class ContentRulesView(View):
    """API to manage ProductContentRules"""

    def get(self, request):
        try:
            category = request.GET.get('category')
            
            if category:
                rules = ProductContentRule.objects.filter(
                    Q(category__isnull=True) | Q(category=category)
                )
            else:
                rules = ProductContentRule.objects.all()
            
            rules_data = list(rules.values())
            
            return JsonResponse({
                'success': True,
                'count': len(rules_data),
                'rules': rules_data
            })
            
        except Exception as e:
            logger.error(f"Error fetching content rules: {e}", exc_info=True)
            return JsonResponse({'error': str(e)}, status=500)

    def post(self, request):
        try:
            data = json.loads(request.body)
            
            if 'field_name' not in data:
                return JsonResponse({'error': 'field_name is required'}, status=400)
            
            rule = ProductContentRule.objects.create(
                category=data.get('category'),
                field_name=data['field_name'],
                is_mandatory=data.get('is_mandatory', True),
                min_length=data.get('min_length'),
                max_length=data.get('max_length'),
                min_word_count=data.get('min_word_count'),
                max_word_count=data.get('max_word_count'),
                must_contain_keywords=data.get('must_contain_keywords', []),
                validation_regex=data.get('validation_regex', ''),
                description=data.get('description', '')
            )
            
            if data.get('category'):
                cache.delete(f"content_rules_{data['category']}")
            
            return JsonResponse({
                'success': True,
                'rule_id': rule.id,
                'message': 'Content rule created successfully'
            })
            
        except Exception as e:
            logger.error(f"Error creating content rule: {e}", exc_info=True)
            return JsonResponse({'error': str(e)}, status=500)
        







# views.py
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status
import tempfile
import os # Import os to remove the temporary file
# from core.models import Product, CategoryAttributeRule, ProductContentRule # Not strictly needed here
from .management.commands.load_excel_data import Command as ExcelLoader

class ExcelUploadView(APIView):
    def post(self, request):
        excel_file = request.FILES.get('file')
        if not excel_file:
            return Response({'error': 'No file uploaded'}, status=status.HTTP_400_BAD_REQUEST)

        tmp_path = None
        try:
            # 1. Save uploaded file to a temporary location
            with tempfile.NamedTemporaryFile(delete=False, suffix=".xlsx") as tmp:
                for chunk in excel_file.chunks():
                    tmp.write(chunk)
                tmp_path = tmp.name

            # 2. Execute the modified loader command
            loader = ExcelLoader()
            # The handle method now returns a dictionary with 'success' and 'message'
            # We pass the path as a keyword argument to match the command structure
            result = loader.handle(excel_path=tmp_path) 

            # 3. Return the response based on the loader result
            if result.get('success'):
                # Send the detailed message back in the API response
                return Response({'success': True, 'message': result['message']}, status=status.HTTP_200_OK)
            else:
                # Handle error case returned by the loader (e.g., file read failure)
                return Response({'success': False, 'error': result.get('error', 'An unknown error occurred during loading.')}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)

        except Exception as e:
            # Catch unexpected exceptions during the process
            return Response({'success': False, 'error': f'Upload failed: {str(e)}'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)

        finally:
            import time
            import gc
            if tmp_path and os.path.exists(tmp_path):
                try:
                    # Force garbage collection to ensure pandas releases the file handle
                    gc.collect()
                    time.sleep(0.3)  # brief pause for Windows file handle release
                    os.remove(tmp_path)
                except PermissionError:
                    # Retry once after short delay (Windows-specific fix)
                    time.sleep(0.5)
                    try:
                        os.remove(tmp_path)
                    except Exception:
                        pass  # give up silently; OS will clean temp files later


import os
import urllib.parse
from django.http import HttpResponse
from django.conf import settings

def open_outlook_mail(request):
    try:
        body_path = os.path.join(settings.BASE_DIR, 'core', 'scripts', 'mailbody.txt')
        with open(body_path, 'r', encoding='utf-8') as f:
            body = f.read().strip()

        subject = "ETS Competitor Intelligence - October 28, 2025"
        recipient = "manager@example.com"

        encoded_subject = urllib.parse.quote(subject)
        encoded_body = urllib.parse.quote(body)
        mailto_url = f"mailto:{recipient}?subject={encoded_subject}&body={encoded_body}"

        html = f"""
        <html>
        <head><title>Opening Outlook...</title></head>
        <body style="font-family: Arial, sans-serif; text-align: center; margin-top: 80px;">
            <script>
                window.onload = function() {{
                    window.location.href = "{mailto_url}";
                }};
            </script>
            <h2>📧 Opening Outlook...</h2>
            <p>If Outlook does not open automatically, <a href="{mailto_url}">click here</a>.</p>
        </body>
        </html>
        """
        return HttpResponse(html)

    except Exception as e:
        return HttpResponse(f"<h3>Error:</h3><p>{str(e)}</p>", status=500)






# views.py
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status
from django.db.models import Avg, Count
from .models import Product, AttributeScore  # adjust the import path as needed

from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status
from django.db.models import Avg, Count

class ProductTypeQualityMetricsView(APIView):
    """
    API endpoint to fetch product type quality metrics.
    Returns metrics for all products, ignoring query parameters.
    """

    def get(self, request):
        print("****************** get called")
        # Fetch all products, no filtering
        queryset = Product.objects.all()
        print("queryset", queryset)

        scored = (
            AttributeScore.objects.filter(product__in=queryset)
            .annotate(
                title_quality=Avg('details__title_quality'),
                description_quality=Avg('details__description_quality'),
                image_quality=Avg('details__image_score'),
                attributes_quality=Avg('details__attributes'),
            )
            .values('product__product_type')
            .annotate(
                product_count=Count('product', distinct=True),
                scored_product_count=Count('id'),
                avg_overall_score=Avg('score'),
                avg_title_quality=Avg('details__title_quality'),
                avg_description_quality=Avg('details__description_quality'),
                avg_image_quality=Avg('details__image_score'),
                avg_attributes_quality=Avg('details__attributes'),
            )
        )

        results = [
            {
                "product_type": item['product__product_type'],
                "product_count": item['product_count'],
                "scored_product_count": item['scored_product_count'],
                "avg_overall_score": round(item['avg_overall_score'] or 0, 2),
                "avg_title_quality_percent": round(item['avg_title_quality'] or 0, 2),
                "avg_description_quality_percent": round(item['avg_description_quality'] or 0, 2),
                "avg_image_quality_percent": round(item['avg_image_quality'] or 0, 2),
                "avg_attributes_quality_percent": round(item['avg_attributes_quality'] or 0, 2),
            }
            for item in scored
        ]

        return Response(results, status=status.HTTP_200_OK)


# class ProductTypeQualityMetricsView(APIView):
    """
    API endpoint to fetch product type quality metrics.
    Supports optional ?product_type=<type> query param.
    """

    def get(self, request):
        product_type_filter = request.query_params.get('product_type', None)
        queryset = Product.objects.all()
        print("queryset",queryset)

        if product_type_filter:
            queryset = queryset.filter(product_type=product_type_filter)

        scored = (
            AttributeScore.objects.filter(product__in=queryset)
            .annotate(
                title_quality=Avg('details__title_quality'),
                description_quality=Avg('details__description_quality'),
                image_quality=Avg('details__image_score'),
                attributes_quality=Avg('details__attributes'),
            )
            .values('product__category')
            .annotate(
                product_count=Count('product', distinct=True),
                scored_product_count=Count('id'),
                avg_overall_score=Avg('score'),
                avg_title_quality=Avg('details__title_quality'),
                avg_description_quality=Avg('details__description_quality'),
                avg_image_quality=Avg('details__image_score'),
                avg_attributes_quality=Avg('details__attributes'),
            )
        )

        results = [
            {
                "product_type": item['product__product_type'],
                "product_count": item['product_count'],
                "scored_product_count": item['scored_product_count'],
                "avg_overall_score": round(item['avg_overall_score'] or 0, 2),
                "avg_title_quality_percent": round(item['avg_title_quality'] or 0, 2),
                "avg_description_quality_percent": round(item['avg_description_quality'] or 0, 2),
                "avg_image_quality_percent": round(item['avg_image_quality'] or 0, 2),
                "avg_attributes_quality_percent": round(item['avg_attributes_quality'] or 0, 2),
            }
            for item in scored
        ]

        return Response(results, status=status.HTTP_200_OK)