content_quality_tool/attr_extraction/services.py

# # import requests
# # import json
# # from typing import Dict, List, Optional
# # from django.conf import settings


# # class ProductAttributeService:
# #     """Service class for extracting product attributes using Groq LLM."""

# #     @staticmethod
# #     def combine_product_text(
# #         title: Optional[str] = None,
# #         short_desc: Optional[str] = None,
# #         long_desc: Optional[str] = None
# #     ) -> str:
# #         """Combine product metadata into a single text block."""
# #         parts = []
# #         if title:
# #             parts.append(str(title).strip())
# #         if short_desc:
# #             parts.append(str(short_desc).strip())
# #         if long_desc:
# #             parts.append(str(long_desc).strip())

# #         combined = " ".join(parts).strip()

# #         if not combined:
# #             return "No product information available"

# #         return combined

# #     @staticmethod
# #     def extract_attributes(
# #         product_text: str,
# #         mandatory_attrs: Dict[str, List[str]],
# #         model: str = None,
# #         extract_additional: bool = True
# #     ) -> dict:
# #         """Use Groq LLM to extract attributes from any product type."""
        
# #         if model is None:
# #             model = settings.SUPPORTED_MODELS[0]

# #         # Check if product text is empty or minimal
# #         if not product_text or product_text == "No product information available":
# #             return ProductAttributeService._create_error_response(
# #                 "No product information provided",
# #                 mandatory_attrs,
# #                 extract_additional
# #             )

# #         # Create structured prompt for mandatory attributes
# #         mandatory_attr_list = []
# #         for attr_name, allowed_values in mandatory_attrs.items():
# #             mandatory_attr_list.append(f"{attr_name}: {', '.join(allowed_values)}")
# #         mandatory_attr_text = "\n".join(mandatory_attr_list)

# #         additional_instruction = ""
# #         if extract_additional:
# #             additional_instruction = """
# # 2. Extract ADDITIONAL attributes: Identify any other relevant attributes from the product text 
# #    (such as Material, Size, Color, Brand, Dimensions, Weight, Features, Specifications, etc.) 
# #    and their values. Extract attributes that are specific and relevant to this product type."""

# #         output_format = {
# #             "mandatory": {attr: "value" for attr in mandatory_attrs.keys()},
# #             "additional": {} if extract_additional else None
# #         }

# #         if not extract_additional:
# #             output_format.pop("additional")

# #         prompt = f"""
# # You are an intelligent product attribute extractor that works with ANY product type.

# # TASK:
# # 1. Extract MANDATORY attributes: For each mandatory attribute, select the most appropriate value 
# #    from the provided list. Choose the value that best matches the product description.
# # {additional_instruction}

# # Product Text:
# # {product_text}

# # Mandatory Attribute Lists (MUST select one value for each):
# # {mandatory_attr_text}

# # CRITICAL INSTRUCTIONS:
# # - Return ONLY valid JSON, nothing else
# # - No explanations, no markdown, no text before or after the JSON
# # - For mandatory attributes, choose EXACTLY ONE value from the provided list that best matches
# # - If a mandatory attribute cannot be determined from the product text, use "Not Specified"
# # - Work with whatever information is available - the product text may be incomplete (only title, or only description, etc.)
# # {f"- For additional attributes, extract any relevant information found in the product text" if extract_additional else ""}
# # - Be precise and only extract information that is explicitly stated or clearly implied

# # Required Output Format (ONLY THIS, NO OTHER TEXT):
# # {json.dumps(output_format, indent=2)}
# #         """

# #         payload = {
# #             "model": model,
# #             "messages": [
# #                 {
# #                     "role": "system",
# #                     "content": f"You are a precise attribute extraction model. Return ONLY valid JSON with {'mandatory and additional' if extract_additional else 'mandatory'} sections. No explanations, no markdown, no other text."
# #                 },
# #                 {"role": "user", "content": prompt}
# #             ],
# #             "temperature": 0.0,
# #             "max_tokens": 1500
# #         }

# #         headers = {
# #             "Authorization": f"Bearer {settings.GROQ_API_KEY}",
# #             "Content-Type": "application/json",
# #         }

# #         try:
# #             response = requests.post(
# #                 settings.GROQ_API_URL,
# #                 headers=headers,
# #                 json=payload,
# #                 timeout=30
# #             )
# #             response.raise_for_status()
# #             result_text = response.json()["choices"][0]["message"]["content"].strip()

# #             # Clean the response
# #             result_text = ProductAttributeService._clean_json_response(result_text)

# #             # Parse JSON
# #             parsed = json.loads(result_text)

# #             # Validate and restructure if needed
# #             parsed = ProductAttributeService._validate_response_structure(
# #                 parsed, mandatory_attrs, extract_additional
# #             )

# #             return parsed

# #         except requests.exceptions.RequestException as e:
# #             return ProductAttributeService._create_error_response(
# #                 str(e), mandatory_attrs, extract_additional
# #             )
# #         except json.JSONDecodeError as e:
# #             return ProductAttributeService._create_error_response(
# #                 f"Invalid JSON: {str(e)}", mandatory_attrs, extract_additional, result_text
# #             )
# #         except Exception as e:
# #             return ProductAttributeService._create_error_response(
# #                 str(e), mandatory_attrs, extract_additional
# #             )

# #     @staticmethod
# #     def _clean_json_response(text: str) -> str:
# #         """Clean LLM response to extract valid JSON."""
# #         start_idx = text.find('{')
# #         end_idx = text.rfind('}')

# #         if start_idx != -1 and end_idx != -1:
# #             text = text[start_idx:end_idx + 1]

# #         if "```json" in text:
# #             text = text.split("```json")[1].split("```")[0].strip()
# #         elif "```" in text:
# #             text = text.split("```")[1].split("```")[0].strip()
# #             if text.startswith("json"):
# #                 text = text[4:].strip()

# #         return text

# #     @staticmethod
# #     def _validate_response_structure(
# #         parsed: dict,
# #         mandatory_attrs: Dict[str, List[str]],
# #         extract_additional: bool
# #     ) -> dict:
# #         """Validate and fix the response structure."""
# #         expected_sections = ["mandatory"]
# #         if extract_additional:
# #             expected_sections.append("additional")

# #         if not all(section in parsed for section in expected_sections):
# #             if isinstance(parsed, dict):
# #                 mandatory_keys = set(mandatory_attrs.keys())
# #                 mandatory = {k: v for k, v in parsed.items() if k in mandatory_keys}
# #                 additional = {k: v for k, v in parsed.items() if k not in mandatory_keys}

# #                 result = {"mandatory": mandatory}
# #                 if extract_additional:
# #                     result["additional"] = additional
# #                 return result
# #             else:
# #                 return ProductAttributeService._create_error_response(
# #                     "Invalid response structure",
# #                     mandatory_attrs,
# #                     extract_additional,
# #                     str(parsed)
# #                 )

# #         return parsed

# #     @staticmethod
# #     def _create_error_response(
# #         error: str,
# #         mandatory_attrs: Dict[str, List[str]],
# #         extract_additional: bool,
# #         raw_output: Optional[str] = None
# #     ) -> dict:
# #         """Create a standardized error response."""
# #         response = {
# #             "mandatory": {attr: "Not Specified" for attr in mandatory_attrs.keys()},
# #             "error": error
# #         }
# #         if extract_additional:
# #             response["additional"] = {}
# #         if raw_output:
# #             response["raw_output"] = raw_output
# #         return response





# import requests
# import json
# from typing import Dict, List, Optional
# from django.conf import settings
# from concurrent.futures import ThreadPoolExecutor, as_completed


# class ProductAttributeService:
#     """Service class for extracting product attributes using Groq LLM."""

#     @staticmethod
#     def combine_product_text(
#         title: Optional[str] = None,
#         short_desc: Optional[str] = None,
#         long_desc: Optional[str] = None
#     ) -> str:
#         """Combine product metadata into a single text block."""
#         parts = []
#         if title:
#             parts.append(str(title).strip())
#         if short_desc:
#             parts.append(str(short_desc).strip())
#         if long_desc:
#             parts.append(str(long_desc).strip())

#         combined = " ".join(parts).strip()

#         if not combined:
#             return "No product information available"

#         return combined

#     @staticmethod
#     def extract_attributes(
#         product_text: str,
#         mandatory_attrs: Dict[str, List[str]],
#         model: str = None,
#         extract_additional: bool = True
#     ) -> dict:
#         """Use Groq LLM to extract attributes from any product type."""
        
#         if model is None:
#             model = settings.SUPPORTED_MODELS[0]

#         # Check if product text is empty or minimal
#         if not product_text or product_text == "No product information available":
#             return ProductAttributeService._create_error_response(
#                 "No product information provided",
#                 mandatory_attrs,
#                 extract_additional
#             )

#         # Create structured prompt for mandatory attributes
#         mandatory_attr_list = []
#         for attr_name, allowed_values in mandatory_attrs.items():
#             mandatory_attr_list.append(f"{attr_name}: {', '.join(allowed_values)}")
#         mandatory_attr_text = "\n".join(mandatory_attr_list)

#         additional_instruction = ""
#         if extract_additional:
#             additional_instruction = """
# 2. Extract ADDITIONAL attributes: Identify any other relevant attributes from the product text 
#    (such as Material, Size, Color, Brand, Dimensions, Weight, Features, Specifications, etc.) 
#    and their values. Extract attributes that are specific and relevant to this product type."""

#         output_format = {
#             "mandatory": {attr: "value" for attr in mandatory_attrs.keys()},
#             "additional": {} if extract_additional else None
#         }

#         if not extract_additional:
#             output_format.pop("additional")

#         prompt = f"""
# You are an intelligent product attribute extractor that works with ANY product type.

# TASK:
# 1. Extract MANDATORY attributes: For each mandatory attribute, select the most appropriate value 
#    from the provided list. Choose the value that best matches the product description.
# {additional_instruction}

# Product Text:
# {product_text}

# Mandatory Attribute Lists (MUST select one value for each):
# {mandatory_attr_text}

# CRITICAL INSTRUCTIONS:
# - Return ONLY valid JSON, nothing else
# - No explanations, no markdown, no text before or after the JSON
# - For mandatory attributes, choose EXACTLY ONE value from the provided list that best matches
# - If a mandatory attribute cannot be determined from the product text, use "Not Specified"
# - Work with whatever information is available - the product text may be incomplete (only title, or only description, etc.)
# {f"- For additional attributes, extract any relevant information found in the product text" if extract_additional else ""}
# - Be precise and only extract information that is explicitly stated or clearly implied

# Required Output Format (ONLY THIS, NO OTHER TEXT):
# {json.dumps(output_format, indent=2)}
#         """

#         payload = {
#             "model": model,
#             "messages": [
#                 {
#                     "role": "system",
#                     "content": f"You are a precise attribute extraction model. Return ONLY valid JSON with {'mandatory and additional' if extract_additional else 'mandatory'} sections. No explanations, no markdown, no other text."
#                 },
#                 {"role": "user", "content": prompt}
#             ],
#             "temperature": 0.0,
#             "max_tokens": 1500
#         }

#         headers = {
#             "Authorization": f"Bearer {settings.GROQ_API_KEY}",
#             "Content-Type": "application/json",
#         }

#         try:
#             response = requests.post(
#                 settings.GROQ_API_URL,
#                 headers=headers,
#                 json=payload,
#                 timeout=30
#             )
#             response.raise_for_status()
#             result_text = response.json()["choices"][0]["message"]["content"].strip()

#             # Clean the response
#             result_text = ProductAttributeService._clean_json_response(result_text)

#             # Parse JSON
#             parsed = json.loads(result_text)

#             # Validate and restructure if needed
#             parsed = ProductAttributeService._validate_response_structure(
#                 parsed, mandatory_attrs, extract_additional
#             )

#             return parsed

#         except requests.exceptions.RequestException as e:
#             return ProductAttributeService._create_error_response(
#                 str(e), mandatory_attrs, extract_additional
#             )
#         except json.JSONDecodeError as e:
#             return ProductAttributeService._create_error_response(
#                 f"Invalid JSON: {str(e)}", mandatory_attrs, extract_additional, result_text
#             )
#         except Exception as e:
#             return ProductAttributeService._create_error_response(
#                 str(e), mandatory_attrs, extract_additional
#             )

#     @staticmethod
#     def extract_attributes_batch(
#         products: List[Dict],
#         mandatory_attrs: Dict[str, List[str]],
#         model: str = None,
#         extract_additional: bool = True,
#         max_workers: int = 5
#     ) -> Dict:
#         """
#         Extract attributes for multiple products in parallel.
        
#         Args:
#             products: List of product dictionaries with keys: product_id, title, short_desc, long_desc
#             mandatory_attrs: Dictionary of mandatory attributes
#             model: Groq model to use
#             extract_additional: Whether to extract additional attributes
#             max_workers: Maximum number of parallel workers
            
#         Returns:
#             Dictionary with results, total_products, successful, and failed counts
#         """
#         results = []
#         successful = 0
#         failed = 0

#         def process_product(product_data):
#             """Process a single product."""
#             product_id = product_data.get('product_id', f"product_{len(results)}")
            
#             try:
#                 product_text = ProductAttributeService.combine_product_text(
#                     title=product_data.get('title'),
#                     short_desc=product_data.get('short_desc'),
#                     long_desc=product_data.get('long_desc')
#                 )
                
#                 result = ProductAttributeService.extract_attributes(
#                     product_text=product_text,
#                     mandatory_attrs=mandatory_attrs,
#                     model=model,
#                     extract_additional=extract_additional
#                 )
                
#                 result['product_id'] = product_id
                
#                 # Check if extraction was successful
#                 if 'error' not in result:
#                     return result, True
#                 else:
#                     return result, False
                    
#             except Exception as e:
#                 return {
#                     'product_id': product_id,
#                     'mandatory': {attr: "Not Specified" for attr in mandatory_attrs.keys()},
#                     'additional': {} if extract_additional else None,
#                     'error': f"Processing error: {str(e)}"
#                 }, False

#         # Process products in parallel
#         with ThreadPoolExecutor(max_workers=max_workers) as executor:
#             future_to_product = {
#                 executor.submit(process_product, product): product 
#                 for product in products
#             }
            
#             for future in as_completed(future_to_product):
#                 try:
#                     result, success = future.result()
#                     results.append(result)
#                     if success:
#                         successful += 1
#                     else:
#                         failed += 1
#                 except Exception as e:
#                     failed += 1
#                     results.append({
#                         'product_id': 'unknown',
#                         'mandatory': {attr: "Not Specified" for attr in mandatory_attrs.keys()},
#                         'additional': {} if extract_additional else None,
#                         'error': f"Unexpected error: {str(e)}"
#                     })

#         return {
#             'results': results,
#             'total_products': len(products),
#             'successful': successful,
#             'failed': failed
#         }

#     @staticmethod
#     def _clean_json_response(text: str) -> str:
#         """Clean LLM response to extract valid JSON."""
#         start_idx = text.find('{')
#         end_idx = text.rfind('}')

#         if start_idx != -1 and end_idx != -1:
#             text = text[start_idx:end_idx + 1]

#         if "```json" in text:
#             text = text.split("```json")[1].split("```")[0].strip()
#         elif "```" in text:
#             text = text.split("```")[1].split("```")[0].strip()
#             if text.startswith("json"):
#                 text = text[4:].strip()

#         return text

#     @staticmethod
#     def _validate_response_structure(
#         parsed: dict,
#         mandatory_attrs: Dict[str, List[str]],
#         extract_additional: bool
#     ) -> dict:
#         """Validate and fix the response structure."""
#         expected_sections = ["mandatory"]
#         if extract_additional:
#             expected_sections.append("additional")

#         if not all(section in parsed for section in expected_sections):
#             if isinstance(parsed, dict):
#                 mandatory_keys = set(mandatory_attrs.keys())
#                 mandatory = {k: v for k, v in parsed.items() if k in mandatory_keys}
#                 additional = {k: v for k, v in parsed.items() if k not in mandatory_keys}

#                 result = {"mandatory": mandatory}
#                 if extract_additional:
#                     result["additional"] = additional
#                 return result
#             else:
#                 return ProductAttributeService._create_error_response(
#                     "Invalid response structure",
#                     mandatory_attrs,
#                     extract_additional,
#                     str(parsed)
#                 )

#         return parsed

#     @staticmethod
#     def _create_error_response(
#         error: str,
#         mandatory_attrs: Dict[str, List[str]],
#         extract_additional: bool,
#         raw_output: Optional[str] = None
#     ) -> dict:
#         """Create a standardized error response."""
#         response = {
#             "mandatory": {attr: "Not Specified" for attr in mandatory_attrs.keys()},
#             "error": error
#         }
#         if extract_additional:
#             response["additional"] = {}
#         if raw_output:
#             response["raw_output"] = raw_output
#         return response






# # ==================== services.py ====================
# import requests
# import json
# from typing import Dict, List, Optional
# from django.conf import settings
# from concurrent.futures import ThreadPoolExecutor, as_completed
# from .ocr_service import OCRService


# class ProductAttributeService:
#     """Service class for extracting product attributes using Groq LLM."""

#     @staticmethod
#     def combine_product_text(
#         title: Optional[str] = None,
#         short_desc: Optional[str] = None,
#         long_desc: Optional[str] = None,
#         ocr_text: Optional[str] = None
#     ) -> str:
#         """Combine product metadata into a single text block."""
#         parts = []
#         if title:
#             parts.append(f"Title: {str(title).strip()}")
#         if short_desc:
#             parts.append(f"Description: {str(short_desc).strip()}")
#         if long_desc:
#             parts.append(f"Details: {str(long_desc).strip()}")
#         if ocr_text:
#             parts.append(f"OCR Text: {ocr_text}")
        
#         combined = "\n".join(parts).strip()
        
#         if not combined:
#             return "No product information available"
        
#         return combined

#     @staticmethod
#     def extract_attributes_from_ocr(ocr_results: Dict, model: str = None) -> Dict:
#         """Extract structured attributes from OCR text using LLM."""
#         if model is None:
#             model = settings.SUPPORTED_MODELS[0]
        
#         detected_text = ocr_results.get('detected_text', [])
#         if not detected_text:
#             return {}
        
#         # Format OCR text for prompt
#         ocr_text = "\n".join([f"Text: {item['text']}, Confidence: {item['confidence']:.2f}" 
#                               for item in detected_text])
        
#         prompt = f"""
# You are an AI model that extracts structured attributes from OCR text detected on product images.
# Given the OCR detections below, infer the possible product attributes and return them as a clean JSON object.

# OCR Text:
# {ocr_text}

# Extract relevant attributes like:
# - brand
# - model_number
# - size (waist_size, length, etc.)
# - collection
# - any other relevant product information

# Return a JSON object with only the attributes you can confidently identify.
# If an attribute is not present, do not include it in the response.
# """
        
#         payload = {
#             "model": model,
#             "messages": [
#                 {
#                     "role": "system",
#                     "content": "You are a helpful AI that extracts structured data from OCR output. Return only valid JSON."
#                 },
#                 {"role": "user", "content": prompt}
#             ],
#             "temperature": 0.2,
#             "max_tokens": 500
#         }
        
#         headers = {
#             "Authorization": f"Bearer {settings.GROQ_API_KEY}",
#             "Content-Type": "application/json",
#         }
        
#         try:
#             response = requests.post(
#                 settings.GROQ_API_URL,
#                 headers=headers,
#                 json=payload,
#                 timeout=30
#             )
#             response.raise_for_status()
#             result_text = response.json()["choices"][0]["message"]["content"].strip()
            
#             # Clean and parse JSON
#             result_text = ProductAttributeService._clean_json_response(result_text)
#             parsed = json.loads(result_text)
            
#             return parsed
#         except Exception as e:
#             return {"error": f"Failed to extract attributes from OCR: {str(e)}"}

#     @staticmethod
#     def extract_attributes(
#         product_text: str,
#         mandatory_attrs: Dict[str, List[str]],
#         model: str = None,
#         extract_additional: bool = True
#     ) -> dict:
#         """Use Groq LLM to extract attributes from any product type."""
        
#         if model is None:
#             model = settings.SUPPORTED_MODELS[0]

#         # Check if product text is empty or minimal
#         if not product_text or product_text == "No product information available":
#             return ProductAttributeService._create_error_response(
#                 "No product information provided",
#                 mandatory_attrs,
#                 extract_additional
#             )

#         # Create structured prompt for mandatory attributes
#         mandatory_attr_list = []
#         for attr_name, allowed_values in mandatory_attrs.items():
#             mandatory_attr_list.append(f"{attr_name}: {', '.join(allowed_values)}")
#         mandatory_attr_text = "\n".join(mandatory_attr_list)

#         additional_instruction = ""
#         if extract_additional:
#             additional_instruction = """
# 2. Extract ADDITIONAL attributes: Identify any other relevant attributes from the product text 
#    (such as Material, Size, Color, Brand, Dimensions, Weight, Features, Specifications, etc.) 
#    and their values. Extract attributes that are specific and relevant to this product type."""

#         output_format = {
#             "mandatory": {attr: "value" for attr in mandatory_attrs.keys()},
#             "additional": {} if extract_additional else None
#         }

#         if not extract_additional:
#             output_format.pop("additional")

#         prompt = f"""
# You are an intelligent product attribute extractor that works with ANY product type.

# TASK:
# 1. Extract MANDATORY attributes: For each mandatory attribute, select the most appropriate value 
#    from the provided list. Choose the value that best matches the product description.
# {additional_instruction}

# Product Text:
# {product_text}

# Mandatory Attribute Lists (MUST select one value for each):
# {mandatory_attr_text}

# CRITICAL INSTRUCTIONS:
# - Return ONLY valid JSON, nothing else
# - No explanations, no markdown, no text before or after the JSON
# - For mandatory attributes, choose EXACTLY ONE value from the provided list that best matches
# - If a mandatory attribute cannot be determined from the product text, use "Not Specified"
# - Work with whatever information is available - the product text may be incomplete
# {f"- For additional attributes, extract any relevant information found in the product text" if extract_additional else ""}
# - Be precise and only extract information that is explicitly stated or clearly implied

# Required Output Format (ONLY THIS, NO OTHER TEXT):
# {json.dumps(output_format, indent=2)}
#         """

#         payload = {
#             "model": model,
#             "messages": [
#                 {
#                     "role": "system",
#                     "content": f"You are a precise attribute extraction model. Return ONLY valid JSON with {'mandatory and additional' if extract_additional else 'mandatory'} sections. No explanations, no markdown, no other text."
#                 },
#                 {"role": "user", "content": prompt}
#             ],
#             "temperature": 0.0,
#             "max_tokens": 1500
#         }

#         headers = {
#             "Authorization": f"Bearer {settings.GROQ_API_KEY}",
#             "Content-Type": "application/json",
#         }

#         try:
#             response = requests.post(
#                 settings.GROQ_API_URL,
#                 headers=headers,
#                 json=payload,
#                 timeout=30
#             )
#             response.raise_for_status()
#             result_text = response.json()["choices"][0]["message"]["content"].strip()

#             # Clean the response
#             result_text = ProductAttributeService._clean_json_response(result_text)

#             # Parse JSON
#             parsed = json.loads(result_text)

#             # Validate and restructure if needed
#             parsed = ProductAttributeService._validate_response_structure(
#                 parsed, mandatory_attrs, extract_additional
#             )

#             return parsed

#         except requests.exceptions.RequestException as e:
#             return ProductAttributeService._create_error_response(
#                 str(e), mandatory_attrs, extract_additional
#             )
#         except json.JSONDecodeError as e:
#             return ProductAttributeService._create_error_response(
#                 f"Invalid JSON: {str(e)}", mandatory_attrs, extract_additional, result_text
#             )
#         except Exception as e:
#             return ProductAttributeService._create_error_response(
#                 str(e), mandatory_attrs, extract_additional
#             )

#     @staticmethod
#     def extract_attributes_batch(
#         products: List[Dict],
#         mandatory_attrs: Dict[str, List[str]],
#         model: str = None,
#         extract_additional: bool = True,
#         process_image: bool = True,
#         max_workers: int = 5
#     ) -> Dict:
#         """Extract attributes for multiple products in parallel."""
#         results = []
#         successful = 0
#         failed = 0
        
#         ocr_service = OCRService()

#         def process_product(product_data):
#             """Process a single product."""
#             product_id = product_data.get('product_id', f"product_{len(results)}")
            
#             try:
#                 # Process image if URL is provided
#                 ocr_results = None
#                 ocr_text = None
                
#                 if process_image and product_data.get('image_url'):
#                     ocr_results = ocr_service.process_image(product_data['image_url'])
                    
#                     # Extract attributes from OCR
#                     if ocr_results and ocr_results.get('detected_text'):
#                         ocr_attrs = ProductAttributeService.extract_attributes_from_ocr(
#                             ocr_results, model
#                         )
#                         ocr_results['extracted_attributes'] = ocr_attrs
                        
#                         # Format OCR text for combining with product text
#                         ocr_text = "\n".join([
#                             f"{item['text']} (confidence: {item['confidence']:.2f})"
#                             for item in ocr_results['detected_text']
#                         ])
                
#                 # Combine all product information
#                 product_text = ProductAttributeService.combine_product_text(
#                     title=product_data.get('title'),
#                     short_desc=product_data.get('short_desc'),
#                     long_desc=product_data.get('long_desc'),
#                     ocr_text=ocr_text
#                 )
                
#                 # Extract attributes from combined text
#                 result = ProductAttributeService.extract_attributes(
#                     product_text=product_text,
#                     mandatory_attrs=mandatory_attrs,
#                     model=model,
#                     extract_additional=extract_additional
#                 )
                
#                 result['product_id'] = product_id
                
#                 # Add OCR results if available
#                 if ocr_results:
#                     result['ocr_results'] = ocr_results
                
#                 # Check if extraction was successful
#                 if 'error' not in result:
#                     return result, True
#                 else:
#                     return result, False
                    
#             except Exception as e:
#                 return {
#                     'product_id': product_id,
#                     'mandatory': {attr: "Not Specified" for attr in mandatory_attrs.keys()},
#                     'additional': {} if extract_additional else None,
#                     'error': f"Processing error: {str(e)}"
#                 }, False

#         # Process products in parallel
#         with ThreadPoolExecutor(max_workers=max_workers) as executor:
#             future_to_product = {
#                 executor.submit(process_product, product): product 
#                 for product in products
#             }
            
#             for future in as_completed(future_to_product):
#                 try:
#                     result, success = future.result()
#                     results.append(result)
#                     if success:
#                         successful += 1
#                     else:
#                         failed += 1
#                 except Exception as e:
#                     failed += 1
#                     results.append({
#                         'product_id': 'unknown',
#                         'mandatory': {attr: "Not Specified" for attr in mandatory_attrs.keys()},
#                         'additional': {} if extract_additional else None,
#                         'error': f"Unexpected error: {str(e)}"
#                     })

#         return {
#             'results': results,
#             'total_products': len(products),
#             'successful': successful,
#             'failed': failed
#         }

#     @staticmethod
#     def _clean_json_response(text: str) -> str:
#         """Clean LLM response to extract valid JSON."""
#         start_idx = text.find('{')
#         end_idx = text.rfind('}')

#         if start_idx != -1 and end_idx != -1:
#             text = text[start_idx:end_idx + 1]

#         if "```json" in text:
#             text = text.split("```json")[1].split("```")[0].strip()
#         elif "```" in text:
#             text = text.split("```")[1].split("```")[0].strip()
#             if text.startswith("json"):
#                 text = text[4:].strip()

#         return text

#     @staticmethod
#     def _validate_response_structure(
#         parsed: dict,
#         mandatory_attrs: Dict[str, List[str]],
#         extract_additional: bool
#     ) -> dict:
#         """Validate and fix the response structure."""
#         expected_sections = ["mandatory"]
#         if extract_additional:
#             expected_sections.append("additional")

#         if not all(section in parsed for section in expected_sections):
#             if isinstance(parsed, dict):
#                 mandatory_keys = set(mandatory_attrs.keys())
#                 mandatory = {k: v for k, v in parsed.items() if k in mandatory_keys}
#                 additional = {k: v for k, v in parsed.items() if k not in mandatory_keys}

#                 result = {"mandatory": mandatory}
#                 if extract_additional:
#                     result["additional"] = additional
#                 return result
#             else:
#                 return ProductAttributeService._create_error_response(
#                     "Invalid response structure",
#                     mandatory_attrs,
#                     extract_additional,
#                     str(parsed)
#                 )

#         return parsed

#     @staticmethod
#     def _create_error_response(
#         error: str,
#         mandatory_attrs: Dict[str, List[str]],
#         extract_additional: bool,
#         raw_output: Optional[str] = None
#     ) -> dict:
#         """Create a standardized error response."""
#         response = {
#             "mandatory": {attr: "Not Specified" for attr in mandatory_attrs.keys()},
#             "error": error
#         }
#         if extract_additional:
#             response["additional"] = {}
#         if raw_output:
#             response["raw_output"] = raw_output
#         return response









# # ==================== services.py ====================
# import requests
# import json
# from typing import Dict, List, Optional, Tuple
# from django.conf import settings
# from concurrent.futures import ThreadPoolExecutor, as_completed
# from sentence_transformers import SentenceTransformer, util
# import numpy as np
# from .ocr_service import OCRService


# # Initialize embedding model for normalization
# model_embedder = SentenceTransformer("all-MiniLM-L6-v2")


# class ProductAttributeService:
#     """Service class for extracting product attributes using Groq LLM."""

#     @staticmethod
#     def combine_product_text(
#         title: Optional[str] = None,
#         short_desc: Optional[str] = None,
#         long_desc: Optional[str] = None,
#         ocr_text: Optional[str] = None
#     ) -> Tuple[str, Dict[str, str]]:
#         """
#         Combine product metadata into a single text block.
#         Returns: (combined_text, source_map) where source_map tracks which text came from where
#         """
#         parts = []
#         source_map = {}
        
#         if title:
#             title_str = str(title).strip()
#             parts.append(f"Title: {title_str}")
#             source_map['title'] = title_str
#         if short_desc:
#             short_str = str(short_desc).strip()
#             parts.append(f"Description: {short_str}")
#             source_map['short_desc'] = short_str
#         if long_desc:
#             long_str = str(long_desc).strip()
#             parts.append(f"Details: {long_str}")
#             source_map['long_desc'] = long_str
#         if ocr_text:
#             parts.append(f"OCR Text: {ocr_text}")
#             source_map['ocr_text'] = ocr_text
        
#         combined = "\n".join(parts).strip()
        
#         if not combined:
#             return "No product information available", {}
        
#         return combined, source_map

#     @staticmethod
#     def find_value_source(value: str, source_map: Dict[str, str]) -> str:
#         """
#         Find which source(s) contain the given value.
#         Returns the source name(s) where the value appears.
#         """
#         value_lower = value.lower()
#         # Split value into tokens for better matching
#         value_tokens = set(value_lower.replace("-", " ").split())
        
#         sources_found = []
#         source_scores = {}
        
#         for source_name, source_text in source_map.items():
#             source_lower = source_text.lower()
            
#             # Check for exact phrase match first
#             if value_lower in source_lower:
#                 source_scores[source_name] = 1.0
#                 continue
            
#             # Check for token matches
#             token_matches = sum(1 for token in value_tokens if token in source_lower)
#             if token_matches > 0:
#                 source_scores[source_name] = token_matches / len(value_tokens)
        
#         # Return source with highest score, or all sources if multiple have same score
#         if source_scores:
#             max_score = max(source_scores.values())
#             sources_found = [s for s, score in source_scores.items() if score == max_score]
            
#             # Prioritize: title > short_desc > long_desc > ocr_text
#             priority = ['title', 'short_desc', 'long_desc', 'ocr_text']
#             for p in priority:
#                 if p in sources_found:
#                     return p
            
#             return sources_found[0] if sources_found else "Not found"
        
#         return "Not found"

#     @staticmethod
#     def extract_attributes_from_ocr(ocr_results: Dict, model: str = None) -> Dict:
#         """Extract structured attributes from OCR text using LLM."""
#         if model is None:
#             model = settings.SUPPORTED_MODELS[0]
        
#         detected_text = ocr_results.get('detected_text', [])
#         if not detected_text:
#             return {}
        
#         # Format OCR text for prompt
#         ocr_text = "\n".join([f"Text: {item['text']}, Confidence: {item['confidence']:.2f}" 
#                               for item in detected_text])
        
#         prompt = f"""
# You are an AI model that extracts structured attributes from OCR text detected on product images.
# Given the OCR detections below, infer the possible product attributes and return them as a clean JSON object.

# OCR Text:
# {ocr_text}

# Extract relevant attributes like:
# - brand
# - model_number
# - size (waist_size, length, etc.)
# - collection
# - any other relevant product information

# Return a JSON object with only the attributes you can confidently identify.
# If an attribute is not present, do not include it in the response.
# """
        
#         payload = {
#             "model": model,
#             "messages": [
#                 {
#                     "role": "system",
#                     "content": "You are a helpful AI that extracts structured data from OCR output. Return only valid JSON."
#                 },
#                 {"role": "user", "content": prompt}
#             ],
#             "temperature": 0.2,
#             "max_tokens": 500
#         }
        
#         headers = {
#             "Authorization": f"Bearer {settings.GROQ_API_KEY}",
#             "Content-Type": "application/json",
#         }
        
#         try:
#             response = requests.post(
#                 settings.GROQ_API_URL,
#                 headers=headers,
#                 json=payload,
#                 timeout=30
#             )
#             response.raise_for_status()
#             result_text = response.json()["choices"][0]["message"]["content"].strip()
            
#             # Clean and parse JSON
#             result_text = ProductAttributeService._clean_json_response(result_text)
#             parsed = json.loads(result_text)
            
#             return parsed
#         except Exception as e:
#             return {"error": f"Failed to extract attributes from OCR: {str(e)}"}

#     @staticmethod
#     def calculate_attribute_relationships(
#         mandatory_attrs: Dict[str, List[str]],
#         product_text: str
#     ) -> Dict[str, float]:
#         """
#         Calculate semantic relationships between attribute values across different attributes.
#         Returns a matrix of cross-attribute value similarities.
#         """
#         pt_emb = model_embedder.encode(product_text, convert_to_tensor=True)

#         # Calculate similarities between all attribute values and product text
#         attr_scores = {}
#         for attr, values in mandatory_attrs.items():
#             attr_scores[attr] = {}
#             for val in values:
#                 contexts = [val, f"for {val}", f"use in {val}", f"suitable for {val}"]
#                 ctx_embs = [model_embedder.encode(c, convert_to_tensor=True) for c in contexts]
#                 sem_sim = max(float(util.cos_sim(pt_emb, ce).item()) for ce in ctx_embs)
#                 attr_scores[attr][val] = sem_sim

#         # Calculate cross-attribute value relationships
#         relationships = {}
#         attr_list = list(mandatory_attrs.keys())

#         for i, attr1 in enumerate(attr_list):
#             for attr2 in attr_list[i+1:]:
#                 # Calculate pairwise similarities between values of different attributes
#                 for val1 in mandatory_attrs[attr1]:
#                     for val2 in mandatory_attrs[attr2]:
#                         emb1 = model_embedder.encode(val1, convert_to_tensor=True)
#                         emb2 = model_embedder.encode(val2, convert_to_tensor=True)
#                         sim = float(util.cos_sim(emb1, emb2).item())

#                         # Store bidirectional relationships
#                         key1 = f"{attr1}:{val1}->{attr2}:{val2}"
#                         key2 = f"{attr2}:{val2}->{attr1}:{val1}"
#                         relationships[key1] = sim
#                         relationships[key2] = sim

#         return relationships

#     @staticmethod
#     def calculate_value_clusters(
#         values: List[str],
#         scores: List[Tuple[str, float]],
#         cluster_threshold: float = 0.4
#     ) -> List[List[str]]:
#         """
#         Group values into semantic clusters based on their similarity to each other.
#         Returns clusters of related values.
#         """
#         if len(values) <= 1:
#             return [[val] for val, _ in scores]

#         # Get embeddings for all values
#         embeddings = [model_embedder.encode(val, convert_to_tensor=True) for val in values]

#         # Calculate pairwise similarities
#         similarity_matrix = np.zeros((len(values), len(values)))
#         for i in range(len(values)):
#             for j in range(i+1, len(values)):
#                 sim = float(util.cos_sim(embeddings[i], embeddings[j]).item())
#                 similarity_matrix[i][j] = sim
#                 similarity_matrix[j][i] = sim

#         # Simple clustering: group values with high similarity
#         clusters = []
#         visited = set()

#         for i, (val, score) in enumerate(scores):
#             if i in visited:
#                 continue

#             cluster = [val]
#             visited.add(i)

#             # Find similar values
#             for j in range(len(values)):
#                 if j not in visited and similarity_matrix[i][j] >= cluster_threshold:
#                     cluster.append(values[j])
#                     visited.add(j)

#             clusters.append(cluster)

#         return clusters

#     @staticmethod
#     def get_dynamic_threshold(
#         attr: str,
#         val: str,
#         base_score: float,
#         extracted_attrs: Dict[str, List[Dict[str, str]]],
#         relationships: Dict[str, float],
#         mandatory_attrs: Dict[str, List[str]],
#         base_threshold: float = 0.65,
#         boost_factor: float = 0.15
#     ) -> float:
#         """
#         Calculate dynamic threshold based on relationships with already-extracted attributes.
#         """
#         threshold = base_threshold

#         # Check relationships with already extracted attributes
#         max_relationship = 0.0
#         for other_attr, other_values_list in extracted_attrs.items():
#             if other_attr == attr:
#                 continue

#             for other_val_dict in other_values_list:
#                 other_val = other_val_dict['value']
#                 key = f"{attr}:{val}->{other_attr}:{other_val}"
#                 if key in relationships:
#                     max_relationship = max(max_relationship, relationships[key])

#         # If strong relationship exists, lower threshold
#         if max_relationship > 0.6:
#             threshold = base_threshold - (boost_factor * max_relationship)

#         return max(0.3, threshold)

#     @staticmethod
#     def get_adaptive_margin(
#         scores: List[Tuple[str, float]],
#         base_margin: float = 0.15,
#         max_margin: float = 0.22
#     ) -> float:
#         """
#         Calculate adaptive margin based on score distribution.
#         """
#         if len(scores) < 2:
#             return base_margin

#         score_values = [s for _, s in scores]
#         best_score = score_values[0]

#         # If best score is very low, use adaptive margin but be more conservative
#         if best_score < 0.5:
#             # Calculate score spread in top 3-4 scores only (more selective)
#             top_scores = score_values[:min(4, len(score_values))]
#             score_range = max(top_scores) - min(top_scores)

#             # Very controlled margin increase
#             if score_range < 0.30:
#                 # Much more conservative scaling
#                 score_factor = (0.5 - best_score) * 0.35
#                 adaptive = base_margin + score_factor + (0.30 - score_range) * 0.2
#                 return min(adaptive, max_margin)

#         return base_margin

#     @staticmethod
#     def _lexical_evidence(product_text: str, label: str) -> float:
#         """Calculate lexical overlap between product text and label."""
#         pt = product_text.lower()
#         tokens = [t for t in label.lower().replace("-", " ").split() if t]
#         if not tokens:
#             return 0.0
#         hits = sum(1 for t in tokens if t in pt)
#         return hits / len(tokens)

#     @staticmethod
#     def normalize_against_product_text(
#         product_text: str,
#         mandatory_attrs: Dict[str, List[str]],
#         source_map: Dict[str, str],
#         threshold_abs: float = 0.65,
#         margin: float = 0.15,
#         allow_multiple: bool = False,
#         sem_weight: float = 0.8,
#         lex_weight: float = 0.2,
#         extracted_attrs: Optional[Dict[str, List[Dict[str, str]]]] = None,
#         relationships: Optional[Dict[str, float]] = None,
#         use_dynamic_thresholds: bool = True,
#         use_adaptive_margin: bool = True,
#         use_semantic_clustering: bool = True
#     ) -> dict:
#         """
#         Score each allowed value against the product_text with dynamic thresholds.
#         Returns dict with values in array format: [{"value": "...", "source": "..."}]
#         """
#         if extracted_attrs is None:
#             extracted_attrs = {}
#         if relationships is None:
#             relationships = {}

#         pt_emb = model_embedder.encode(product_text, convert_to_tensor=True)
#         extracted = {}

#         for attr, allowed_values in mandatory_attrs.items():
#             scores: List[Tuple[str, float]] = []

#             for val in allowed_values:
#                 contexts = [val, f"for {val}", f"use in {val}", f"suitable for {val}", f"{val} room"]
#                 ctx_embs = [model_embedder.encode(c, convert_to_tensor=True) for c in contexts]
#                 sem_sim = max(float(util.cos_sim(pt_emb, ce).item()) for ce in ctx_embs)

#                 lex_score = ProductAttributeService._lexical_evidence(product_text, val)
#                 final_score = sem_weight * sem_sim + lex_weight * lex_score
#                 scores.append((val, final_score))

#             scores.sort(key=lambda x: x[1], reverse=True)
#             best_val, best_score = scores[0]

#             # Calculate adaptive margin if enabled
#             effective_margin = margin
#             if allow_multiple and use_adaptive_margin:
#                 effective_margin = ProductAttributeService.get_adaptive_margin(scores, margin)

#             if not allow_multiple:
#                 source = ProductAttributeService.find_value_source(best_val, source_map)
#                 extracted[attr] = [{"value": best_val, "source": source}]
#             else:
#                 candidates = [best_val]
#                 use_base_threshold = best_score >= threshold_abs

#                 # Get semantic clusters if enabled
#                 clusters = []
#                 if use_semantic_clustering:
#                     clusters = ProductAttributeService.calculate_value_clusters(
#                         allowed_values, scores, cluster_threshold=0.4
#                     )
#                     best_cluster = next((c for c in clusters if best_val in c), [best_val])

#                 for val, sc in scores[1:]:
#                     # Calculate dynamic threshold for this value
#                     if use_dynamic_thresholds and extracted_attrs:
#                         dynamic_thresh = ProductAttributeService.get_dynamic_threshold(
#                             attr, val, sc, extracted_attrs, relationships,
#                             mandatory_attrs, threshold_abs
#                         )
#                     else:
#                         dynamic_thresh = threshold_abs

#                     within_margin = (best_score - sc) <= effective_margin
#                     above_threshold = sc >= dynamic_thresh

#                     # Check if in same semantic cluster as best value
#                     in_cluster = False
#                     if use_semantic_clustering and clusters:
#                         in_cluster = any(best_val in c and val in c for c in clusters)

#                     if use_base_threshold:
#                         # Best score is good, require threshold OR (cluster + margin)
#                         if above_threshold and within_margin:
#                             candidates.append(val)
#                         elif in_cluster and within_margin:
#                             candidates.append(val)
#                     else:
#                         # Best score is low, use margin OR cluster logic
#                         if within_margin:
#                             candidates.append(val)
#                         elif in_cluster and (best_score - sc) <= effective_margin * 2.0:
#                             # Extended margin for cluster members
#                             candidates.append(val)

#                 # Map each candidate to its source and create array format
#                 extracted[attr] = []
#                 for candidate in candidates:
#                     source = ProductAttributeService.find_value_source(candidate, source_map)
#                     extracted[attr].append({"value": candidate, "source": source})

#         return extracted

#     @staticmethod
#     def extract_attributes(
#         product_text: str,
#         mandatory_attrs: Dict[str, List[str]],
#         source_map: Dict[str, str] = None,
#         model: str = None,
#         extract_additional: bool = True,
#         multiple: Optional[List[str]] = None,
#         threshold_abs: float = 0.65,
#         margin: float = 0.15,
#         use_dynamic_thresholds: bool = True,
#         use_adaptive_margin: bool = True,
#         use_semantic_clustering: bool = True
#     ) -> dict:
#         """
#         Use Groq LLM to extract attributes from any product type with enhanced multi-value selection.
#         Now returns values in array format: [{"value": "...", "source": "..."}]
#         """
        
#         if model is None:
#             model = settings.SUPPORTED_MODELS[0]

#         if multiple is None:
#             multiple = []

#         if source_map is None:
#             source_map = {}

#         # Check if product text is empty or minimal
#         if not product_text or product_text == "No product information available":
#             return ProductAttributeService._create_error_response(
#                 "No product information provided",
#                 mandatory_attrs,
#                 extract_additional
#             )

#         # Create structured prompt for mandatory attributes
#         mandatory_attr_list = []
#         for attr_name, allowed_values in mandatory_attrs.items():
#             mandatory_attr_list.append(f"{attr_name}: {', '.join(allowed_values)}")
#         mandatory_attr_text = "\n".join(mandatory_attr_list)

#         additional_instruction = ""
#         if extract_additional:
#             additional_instruction = """
# 2. Extract ADDITIONAL attributes: Identify any other relevant attributes from the product text 
#    that are NOT in the mandatory list. Only include attributes where you can find actual values
#    in the product text. Do NOT include attributes with "Not Specified" or empty values.
   
#    Examples of attributes to look for (only if present): Brand, Material, Size, Color, Dimensions,
#    Weight, Features, Style, Theme, Pattern, Finish, Care Instructions, etc."""

#         output_format = {
#             "mandatory": {attr: "value or list of values" for attr in mandatory_attrs.keys()},
#         }

#         if extract_additional:
#             output_format["additional"] = {
#                 "example_attribute_1": "actual value found",
#                 "example_attribute_2": "actual value found"
#             }
#             output_format["additional"]["_note"] = "Only include attributes with actual values found in text"

#         prompt = f"""
# You are an intelligent product attribute extractor that works with ANY product type.

# TASK:
# 1. Extract MANDATORY attributes: For each mandatory attribute, select the most appropriate value(s)
#    from the provided list. Choose the value(s) that best match the product description.
# {additional_instruction}

# Product Text:
# {product_text}

# Mandatory Attribute Lists (MUST select from these allowed values):
# {mandatory_attr_text}

# CRITICAL INSTRUCTIONS:
# - Return ONLY valid JSON, nothing else
# - No explanations, no markdown, no text before or after the JSON
# - For mandatory attributes, choose the value(s) from the provided list that best match
# - If a mandatory attribute cannot be determined from the product text, use "Not Specified"
# - Prefer exact matches from the allowed values list over generic synonyms
# - If multiple values are plausible, you MAY return more than one
# {f"- For additional attributes: ONLY include attributes where you found actual values in the product text. DO NOT include attributes with 'Not Specified', 'None', 'N/A', or empty values. If you cannot find a value for an attribute, simply don't include that attribute." if extract_additional else ""}
# - Be precise and only extract information that is explicitly stated or clearly implied

# Required Output Format:
# {json.dumps(output_format, indent=2)}
#         """

#         payload = {
#             "model": model,
#             "messages": [
#                 {
#                     "role": "system",
#                     "content": f"You are a precise attribute extraction model. Return ONLY valid JSON with {'mandatory and additional' if extract_additional else 'mandatory'} sections. No explanations, no markdown, no other text."
#                 },
#                 {"role": "user", "content": prompt}
#             ],
#             "temperature": 0.0,
#             "max_tokens": 1500
#         }

#         headers = {
#             "Authorization": f"Bearer {settings.GROQ_API_KEY}",
#             "Content-Type": "application/json",
#         }

#         try:
#             response = requests.post(
#                 settings.GROQ_API_URL,
#                 headers=headers,
#                 json=payload,
#                 timeout=30
#             )
#             response.raise_for_status()
#             result_text = response.json()["choices"][0]["message"]["content"].strip()

#             # Clean the response
#             result_text = ProductAttributeService._clean_json_response(result_text)

#             # Parse JSON
#             parsed = json.loads(result_text)

#             # Validate and restructure with source tracking
#             parsed = ProductAttributeService._validate_response_structure(
#                 parsed, mandatory_attrs, extract_additional, source_map
#             )

#             # Clean up and add source tracking to additional attributes in array format
#             if extract_additional and "additional" in parsed:
#                 cleaned_additional = {}
#                 for k, v in parsed["additional"].items():
#                     if v and v not in ["Not Specified", "None", "N/A", "", "not specified", "none", "n/a"]:
#                         if not (isinstance(v, str) and v.lower() in ["not specified", "none", "n/a", ""]):
#                             # Convert to array format if not already
#                             if isinstance(v, list):
#                                 cleaned_additional[k] = []
#                                 for item in v:
#                                     if isinstance(item, dict) and "value" in item:
#                                         if "source" not in item:
#                                             item["source"] = ProductAttributeService.find_value_source(
#                                                 item["value"], source_map
#                                             )
#                                         cleaned_additional[k].append(item)
#                                     else:
#                                         source = ProductAttributeService.find_value_source(str(item), source_map)
#                                         cleaned_additional[k].append({"value": str(item), "source": source})
#                             else:
#                                 source = ProductAttributeService.find_value_source(str(v), source_map)
#                                 cleaned_additional[k] = [{"value": str(v), "source": source}]
#                 parsed["additional"] = cleaned_additional

#             # Calculate attribute relationships if using dynamic thresholds
#             relationships = {}
#             if use_dynamic_thresholds:
#                 relationships = ProductAttributeService.calculate_attribute_relationships(
#                     mandatory_attrs, product_text
#                 )

#             # Process attributes in order, allowing earlier ones to influence later ones
#             extracted_so_far = {}
#             for attr in mandatory_attrs.keys():
#                 allow_multiple = attr in multiple

#                 result = ProductAttributeService.normalize_against_product_text(
#                     product_text=product_text,
#                     mandatory_attrs={attr: mandatory_attrs[attr]},
#                     source_map=source_map,
#                     threshold_abs=threshold_abs,
#                     margin=margin,
#                     allow_multiple=allow_multiple,
#                     extracted_attrs=extracted_so_far,
#                     relationships=relationships,
#                     use_dynamic_thresholds=use_dynamic_thresholds,
#                     use_adaptive_margin=use_adaptive_margin,
#                     use_semantic_clustering=use_semantic_clustering
#                 )

#                 # Result is already in array format from normalize_against_product_text
#                 parsed["mandatory"][attr] = result[attr]
#                 extracted_so_far[attr] = result[attr]

#             return parsed

#         except requests.exceptions.RequestException as e:
#             return ProductAttributeService._create_error_response(
#                 str(e), mandatory_attrs, extract_additional
#             )
#         except json.JSONDecodeError as e:
#             return ProductAttributeService._create_error_response(
#                 f"Invalid JSON: {str(e)}", mandatory_attrs, extract_additional, result_text
#             )
#         except Exception as e:
#             return ProductAttributeService._create_error_response(
#                 str(e), mandatory_attrs, extract_additional
#             )

#     @staticmethod
#     def extract_attributes_batch(
#         products: List[Dict],
#         mandatory_attrs: Dict[str, List[str]],
#         model: str = None,
#         extract_additional: bool = True,
#         process_image: bool = True,
#         max_workers: int = 5,
#         multiple: Optional[List[str]] = None,
#         threshold_abs: float = 0.65,
#         margin: float = 0.15,
#         use_dynamic_thresholds: bool = True,
#         use_adaptive_margin: bool = True,
#         use_semantic_clustering: bool = True
#     ) -> Dict:
#         """Extract attributes for multiple products in parallel with enhanced multi-value selection and source tracking."""
#         results = []
#         successful = 0
#         failed = 0
        
#         ocr_service = OCRService()

#         if multiple is None:
#             multiple = []

#         def process_product(product_data):
#             """Process a single product."""
#             product_id = product_data.get('product_id', f"product_{len(results)}")
            
#             try:
#                 # Process image if URL is provided
#                 ocr_results = None
#                 ocr_text = None
                
#                 if process_image and product_data.get('image_url'):
#                     ocr_results = ocr_service.process_image(product_data['image_url'])
                    
#                     # Extract attributes from OCR
#                     if ocr_results and ocr_results.get('detected_text'):
#                         ocr_attrs = ProductAttributeService.extract_attributes_from_ocr(
#                             ocr_results, model
#                         )
#                         ocr_results['extracted_attributes'] = ocr_attrs
                        
#                         # Format OCR text for combining with product text
#                         ocr_text = "\n".join([
#                             f"{item['text']} (confidence: {item['confidence']:.2f})"
#                             for item in ocr_results['detected_text']
#                         ])
                
#                 # Combine all product information with source tracking
#                 product_text, source_map = ProductAttributeService.combine_product_text(
#                     title=product_data.get('title'),
#                     short_desc=product_data.get('short_desc'),
#                     long_desc=product_data.get('long_desc'),
#                     ocr_text=ocr_text
#                 )
                
#                 # Extract attributes from combined text with enhanced features
#                 result = ProductAttributeService.extract_attributes(
#                     product_text=product_text,
#                     mandatory_attrs=mandatory_attrs,
#                     source_map=source_map,
#                     model=model,
#                     extract_additional=extract_additional,
#                     multiple=multiple,
#                     threshold_abs=threshold_abs,
#                     margin=margin,
#                     use_dynamic_thresholds=use_dynamic_thresholds,
#                     use_adaptive_margin=use_adaptive_margin,
#                     use_semantic_clustering=use_semantic_clustering
#                 )
                
#                 result['product_id'] = product_id
                
#                 # Add OCR results if available
#                 if ocr_results:
#                     result['ocr_results'] = ocr_results
                
#                 # Check if extraction was successful
#                 if 'error' not in result:
#                     return result, True
#                 else:
#                     return result, False
                    
#             except Exception as e:
#                 return {
#                     'product_id': product_id,
#                     'mandatory': {attr: [{"value": "Not Specified", "source": "error"}] for attr in mandatory_attrs.keys()},
#                     'additional': {} if extract_additional else None,
#                     'error': f"Processing error: {str(e)}"
#                 }, False

#         # Process products in parallel
#         with ThreadPoolExecutor(max_workers=max_workers) as executor:
#             future_to_product = {
#                 executor.submit(process_product, product): product 
#                 for product in products
#             }
            
#             for future in as_completed(future_to_product):
#                 try:
#                     result, success = future.result()
#                     results.append(result)
#                     if success:
#                         successful += 1
#                     else:
#                         failed += 1
#                 except Exception as e:
#                     failed += 1
#                     results.append({
#                         'product_id': 'unknown',
#                         'mandatory': {attr: [{"value": "Not Specified", "source": "error"}] for attr in mandatory_attrs.keys()},
#                         'additional': {} if extract_additional else None,
#                         'error': f"Unexpected error: {str(e)}"
#                     })

#         return {
#             'results': results,
#             'total_products': len(products),
#             'successful': successful,
#             'failed': failed
#         }

#     @staticmethod
#     def _clean_json_response(text: str) -> str:
#         """Clean LLM response to extract valid JSON."""
#         start_idx = text.find('{')
#         end_idx = text.rfind('}')

#         if start_idx != -1 and end_idx != -1:
#             text = text[start_idx:end_idx + 1]

#         if "```json" in text:
#             text = text.split("```json")[1].split("```")[0].strip()
#         elif "```" in text:
#             text = text.split("```")[1].split("```")[0].strip()
#             if text.startswith("json"):
#                 text = text[4:].strip()

#         return text

#     @staticmethod
#     def _validate_response_structure(
#         parsed: dict,
#         mandatory_attrs: Dict[str, List[str]],
#         extract_additional: bool,
#         source_map: Dict[str, str] = None
#     ) -> dict:
#         """Validate and fix the response structure, ensuring array format with source tracking."""
#         if source_map is None:
#             source_map = {}
        
#         expected_sections = ["mandatory"]
#         if extract_additional:
#             expected_sections.append("additional")

#         if not all(section in parsed for section in expected_sections):
#             if isinstance(parsed, dict):
#                 mandatory_keys = set(mandatory_attrs.keys())
#                 mandatory = {k: v for k, v in parsed.items() if k in mandatory_keys}
#                 additional = {k: v for k, v in parsed.items() if k not in mandatory_keys}

#                 result = {"mandatory": mandatory}
#                 if extract_additional:
#                     result["additional"] = additional
#                 parsed = result
#             else:
#                 return ProductAttributeService._create_error_response(
#                     "Invalid response structure",
#                     mandatory_attrs,
#                     extract_additional,
#                     str(parsed)
#                 )

#         # Convert mandatory attributes to array format with source tracking
#         if "mandatory" in parsed:
#             converted_mandatory = {}
#             for attr, value in parsed["mandatory"].items():
#                 if isinstance(value, list):
#                     # Already in array format, ensure each item has source
#                     converted_mandatory[attr] = []
#                     for item in value:
#                         if isinstance(item, dict) and "value" in item:
#                             # Already has proper structure
#                             if "source" not in item:
#                                 item["source"] = ProductAttributeService.find_value_source(
#                                     item["value"], source_map
#                                 )
#                             converted_mandatory[attr].append(item)
#                         else:
#                             # Convert string to proper format
#                             source = ProductAttributeService.find_value_source(str(item), source_map)
#                             converted_mandatory[attr].append({"value": str(item), "source": source})
#                 else:
#                     # Single value - convert to array format
#                     source = ProductAttributeService.find_value_source(str(value), source_map)
#                     converted_mandatory[attr] = [{"value": str(value), "source": source}]
            
#             parsed["mandatory"] = converted_mandatory

#         return parsed

#     @staticmethod
#     def _create_error_response(
#         error: str,
#         mandatory_attrs: Dict[str, List[str]],
#         extract_additional: bool,
#         raw_output: Optional[str] = None
#     ) -> dict:
#         """Create a standardized error response in array format."""
#         response = {
#             "mandatory": {attr: [{"value": "Not Specified", "source": "error"}] for attr in mandatory_attrs.keys()},
#             "error": error
#         }
#         if extract_additional:
#             response["additional"] = {}
#         if raw_output:
#             response["raw_output"] = raw_output
#         return response









# # ==================== services.py ====================
# import requests
# import json
# from typing import Dict, List, Optional, Tuple
# from django.conf import settings
# from concurrent.futures import ThreadPoolExecutor, as_completed
# from sentence_transformers import SentenceTransformer, util
# import numpy as np
# from .ocr_service import OCRService


# # Initialize embedding model for normalization
# model_embedder = SentenceTransformer("all-MiniLM-L6-v2")


# class ProductAttributeService:
#     """Service class for extracting product attributes using Groq LLM."""

#     @staticmethod
#     def combine_product_text(
#         title: Optional[str] = None,
#         short_desc: Optional[str] = None,
#         long_desc: Optional[str] = None,
#         ocr_text: Optional[str] = None
#     ) -> Tuple[str, Dict[str, str]]:
#         """
#         Combine product metadata into a single text block.
#         Returns: (combined_text, source_map) where source_map tracks which text came from where
#         """
#         parts = []
#         source_map = {}
        
#         if title:
#             title_str = str(title).strip()
#             parts.append(f"Title: {title_str}")
#             source_map['title'] = title_str
#         if short_desc:
#             short_str = str(short_desc).strip()
#             parts.append(f"Description: {short_str}")
#             source_map['short_desc'] = short_str
#         if long_desc:
#             long_str = str(long_desc).strip()
#             parts.append(f"Details: {long_str}")
#             source_map['long_desc'] = long_str
#         if ocr_text:
#             parts.append(f"OCR Text: {ocr_text}")
#             source_map['ocr_text'] = ocr_text
        
#         combined = "\n".join(parts).strip()
        
#         if not combined:
#             return "No product information available", {}
        
#         return combined, source_map

#     @staticmethod
#     def find_value_source(value: str, source_map: Dict[str, str]) -> str:
#         """
#         Find which source(s) contain the given value.
#         Returns the source name(s) where the value appears.
#         """
#         value_lower = value.lower()
#         # Split value into tokens for better matching
#         value_tokens = set(value_lower.replace("-", " ").split())
        
#         sources_found = []
#         source_scores = {}
        
#         for source_name, source_text in source_map.items():
#             source_lower = source_text.lower()
            
#             # Check for exact phrase match first
#             if value_lower in source_lower:
#                 source_scores[source_name] = 1.0
#                 continue
            
#             # Check for token matches
#             token_matches = sum(1 for token in value_tokens if token in source_lower)
#             if token_matches > 0:
#                 source_scores[source_name] = token_matches / len(value_tokens)
        
#         # Return source with highest score, or all sources if multiple have same score
#         if source_scores:
#             max_score = max(source_scores.values())
#             sources_found = [s for s, score in source_scores.items() if score == max_score]
            
#             # Prioritize: title > short_desc > long_desc > ocr_text
#             priority = ['title', 'short_desc', 'long_desc', 'ocr_text']
#             for p in priority:
#                 if p in sources_found:
#                     return p
            
#             return sources_found[0] if sources_found else "Not found"
        
#         return "Not found"

#     @staticmethod
#     def format_visual_attributes(visual_attributes: Dict) -> Dict:
#         """
#         Convert visual attributes to array format with source tracking.
#         Source is always 'image' for visual attributes.
#         """
#         formatted = {}
        
#         for key, value in visual_attributes.items():
#             if isinstance(value, list):
#                 # Already a list (like color_palette)
#                 formatted[key] = [{"value": str(item), "source": "image"} for item in value]
#             elif isinstance(value, dict):
#                 # Nested dictionary - format recursively
#                 nested_formatted = {}
#                 for nested_key, nested_value in value.items():
#                     if isinstance(nested_value, list):
#                         nested_formatted[nested_key] = [{"value": str(item), "source": "image"} for item in nested_value]
#                     else:
#                         nested_formatted[nested_key] = [{"value": str(nested_value), "source": "image"}]
#                 formatted[key] = nested_formatted
#             else:
#                 # Single value
#                 formatted[key] = [{"value": str(value), "source": "image"}]
        
#         return formatted

#     @staticmethod
#     def extract_attributes_from_ocr(ocr_results: Dict, model: str = None) -> Dict:
#         """Extract structured attributes from OCR text using LLM."""
#         if model is None:
#             model = settings.SUPPORTED_MODELS[0]
        
#         detected_text = ocr_results.get('detected_text', [])
#         if not detected_text:
#             return {}
        
#         # Format OCR text for prompt
#         ocr_text = "\n".join([f"Text: {item['text']}, Confidence: {item['confidence']:.2f}" 
#                               for item in detected_text])
        
#         prompt = f"""
# You are an AI model that extracts structured attributes from OCR text detected on product images.
# Given the OCR detections below, infer the possible product attributes and return them as a clean JSON object.

# OCR Text:
# {ocr_text}

# Extract relevant attributes like:
# - brand
# - model_number
# - size (waist_size, length, etc.)
# - collection
# - any other relevant product information

# Return a JSON object with only the attributes you can confidently identify.
# If an attribute is not present, do not include it in the response.
# """
        
#         payload = {
#             "model": model,
#             "messages": [
#                 {
#                     "role": "system",
#                     "content": "You are a helpful AI that extracts structured data from OCR output. Return only valid JSON."
#                 },
#                 {"role": "user", "content": prompt}
#             ],
#             "temperature": 0.2,
#             "max_tokens": 500
#         }
        
#         headers = {
#             "Authorization": f"Bearer {settings.GROQ_API_KEY}",
#             "Content-Type": "application/json",
#         }
        
#         try:
#             response = requests.post(
#                 settings.GROQ_API_URL,
#                 headers=headers,
#                 json=payload,
#                 timeout=30
#             )
#             response.raise_for_status()
#             result_text = response.json()["choices"][0]["message"]["content"].strip()
            
#             # Clean and parse JSON
#             result_text = ProductAttributeService._clean_json_response(result_text)
#             parsed = json.loads(result_text)
            
#             # Convert to array format with source tracking
#             formatted_attributes = {}
#             for key, value in parsed.items():
#                 if key == "error":
#                     continue
                
#                 # Handle nested dictionaries (like size)
#                 if isinstance(value, dict):
#                     nested_formatted = {}
#                     for nested_key, nested_value in value.items():
#                         nested_formatted[nested_key] = [{"value": str(nested_value), "source": "image"}]
#                     formatted_attributes[key] = nested_formatted
#                 elif isinstance(value, list):
#                     # Already a list, convert each item
#                     formatted_attributes[key] = [{"value": str(item), "source": "image"} for item in value]
#                 else:
#                     # Single value
#                     formatted_attributes[key] = [{"value": str(value), "source": "image"}]
            
#             return formatted_attributes
#         except Exception as e:
#             return {"error": f"Failed to extract attributes from OCR: {str(e)}"}

#     @staticmethod
#     def calculate_attribute_relationships(
#         mandatory_attrs: Dict[str, List[str]],
#         product_text: str
#     ) -> Dict[str, float]:
#         """
#         Calculate semantic relationships between attribute values across different attributes.
#         Returns a matrix of cross-attribute value similarities.
#         """
#         pt_emb = model_embedder.encode(product_text, convert_to_tensor=True)

#         # Calculate similarities between all attribute values and product text
#         attr_scores = {}
#         for attr, values in mandatory_attrs.items():
#             attr_scores[attr] = {}
#             for val in values:
#                 contexts = [val, f"for {val}", f"use in {val}", f"suitable for {val}"]
#                 ctx_embs = [model_embedder.encode(c, convert_to_tensor=True) for c in contexts]
#                 sem_sim = max(float(util.cos_sim(pt_emb, ce).item()) for ce in ctx_embs)
#                 attr_scores[attr][val] = sem_sim

#         # Calculate cross-attribute value relationships
#         relationships = {}
#         attr_list = list(mandatory_attrs.keys())

#         for i, attr1 in enumerate(attr_list):
#             for attr2 in attr_list[i+1:]:
#                 # Calculate pairwise similarities between values of different attributes
#                 for val1 in mandatory_attrs[attr1]:
#                     for val2 in mandatory_attrs[attr2]:
#                         emb1 = model_embedder.encode(val1, convert_to_tensor=True)
#                         emb2 = model_embedder.encode(val2, convert_to_tensor=True)
#                         sim = float(util.cos_sim(emb1, emb2).item())

#                         # Store bidirectional relationships
#                         key1 = f"{attr1}:{val1}->{attr2}:{val2}"
#                         key2 = f"{attr2}:{val2}->{attr1}:{val1}"
#                         relationships[key1] = sim
#                         relationships[key2] = sim

#         return relationships

#     @staticmethod
#     def calculate_value_clusters(
#         values: List[str],
#         scores: List[Tuple[str, float]],
#         cluster_threshold: float = 0.4
#     ) -> List[List[str]]:
#         """
#         Group values into semantic clusters based on their similarity to each other.
#         Returns clusters of related values.
#         """
#         if len(values) <= 1:
#             return [[val] for val, _ in scores]

#         # Get embeddings for all values
#         embeddings = [model_embedder.encode(val, convert_to_tensor=True) for val in values]

#         # Calculate pairwise similarities
#         similarity_matrix = np.zeros((len(values), len(values)))
#         for i in range(len(values)):
#             for j in range(i+1, len(values)):
#                 sim = float(util.cos_sim(embeddings[i], embeddings[j]).item())
#                 similarity_matrix[i][j] = sim
#                 similarity_matrix[j][i] = sim

#         # Simple clustering: group values with high similarity
#         clusters = []
#         visited = set()

#         for i, (val, score) in enumerate(scores):
#             if i in visited:
#                 continue

#             cluster = [val]
#             visited.add(i)

#             # Find similar values
#             for j in range(len(values)):
#                 if j not in visited and similarity_matrix[i][j] >= cluster_threshold:
#                     cluster.append(values[j])
#                     visited.add(j)

#             clusters.append(cluster)

#         return clusters

#     @staticmethod
#     def get_dynamic_threshold(
#         attr: str,
#         val: str,
#         base_score: float,
#         extracted_attrs: Dict[str, List[Dict[str, str]]],
#         relationships: Dict[str, float],
#         mandatory_attrs: Dict[str, List[str]],
#         base_threshold: float = 0.65,
#         boost_factor: float = 0.15
#     ) -> float:
#         """
#         Calculate dynamic threshold based on relationships with already-extracted attributes.
#         """
#         threshold = base_threshold

#         # Check relationships with already extracted attributes
#         max_relationship = 0.0
#         for other_attr, other_values_list in extracted_attrs.items():
#             if other_attr == attr:
#                 continue

#             for other_val_dict in other_values_list:
#                 other_val = other_val_dict['value']
#                 key = f"{attr}:{val}->{other_attr}:{other_val}"
#                 if key in relationships:
#                     max_relationship = max(max_relationship, relationships[key])

#         # If strong relationship exists, lower threshold
#         if max_relationship > 0.6:
#             threshold = base_threshold - (boost_factor * max_relationship)

#         return max(0.3, threshold)

#     @staticmethod
#     def get_adaptive_margin(
#         scores: List[Tuple[str, float]],
#         base_margin: float = 0.15,
#         max_margin: float = 0.22
#     ) -> float:
#         """
#         Calculate adaptive margin based on score distribution.
#         """
#         if len(scores) < 2:
#             return base_margin

#         score_values = [s for _, s in scores]
#         best_score = score_values[0]

#         # If best score is very low, use adaptive margin but be more conservative
#         if best_score < 0.5:
#             # Calculate score spread in top 3-4 scores only (more selective)
#             top_scores = score_values[:min(4, len(score_values))]
#             score_range = max(top_scores) - min(top_scores)

#             # Very controlled margin increase
#             if score_range < 0.30:
#                 # Much more conservative scaling
#                 score_factor = (0.5 - best_score) * 0.35
#                 adaptive = base_margin + score_factor + (0.30 - score_range) * 0.2
#                 return min(adaptive, max_margin)

#         return base_margin

#     @staticmethod
#     def _lexical_evidence(product_text: str, label: str) -> float:
#         """Calculate lexical overlap between product text and label."""
#         pt = product_text.lower()
#         tokens = [t for t in label.lower().replace("-", " ").split() if t]
#         if not tokens:
#             return 0.0
#         hits = sum(1 for t in tokens if t in pt)
#         return hits / len(tokens)

#     @staticmethod
#     def normalize_against_product_text(
#         product_text: str,
#         mandatory_attrs: Dict[str, List[str]],
#         source_map: Dict[str, str],
#         threshold_abs: float = 0.65,
#         margin: float = 0.15,
#         allow_multiple: bool = False,
#         sem_weight: float = 0.8,
#         lex_weight: float = 0.2,
#         extracted_attrs: Optional[Dict[str, List[Dict[str, str]]]] = None,
#         relationships: Optional[Dict[str, float]] = None,
#         use_dynamic_thresholds: bool = True,
#         use_adaptive_margin: bool = True,
#         use_semantic_clustering: bool = True
#     ) -> dict:
#         """
#         Score each allowed value against the product_text with dynamic thresholds.
#         Returns dict with values in array format: [{"value": "...", "source": "..."}]
#         """
#         if extracted_attrs is None:
#             extracted_attrs = {}
#         if relationships is None:
#             relationships = {}

#         pt_emb = model_embedder.encode(product_text, convert_to_tensor=True)
#         extracted = {}

#         for attr, allowed_values in mandatory_attrs.items():
#             scores: List[Tuple[str, float]] = []

#             for val in allowed_values:
#                 contexts = [val, f"for {val}", f"use in {val}", f"suitable for {val}", f"{val} room"]
#                 ctx_embs = [model_embedder.encode(c, convert_to_tensor=True) for c in contexts]
#                 sem_sim = max(float(util.cos_sim(pt_emb, ce).item()) for ce in ctx_embs)

#                 lex_score = ProductAttributeService._lexical_evidence(product_text, val)
#                 final_score = sem_weight * sem_sim + lex_weight * lex_score
#                 scores.append((val, final_score))

#             scores.sort(key=lambda x: x[1], reverse=True)
#             best_val, best_score = scores[0]

#             # Calculate adaptive margin if enabled
#             effective_margin = margin
#             if allow_multiple and use_adaptive_margin:
#                 effective_margin = ProductAttributeService.get_adaptive_margin(scores, margin)

#             if not allow_multiple:
#                 source = ProductAttributeService.find_value_source(best_val, source_map)
#                 extracted[attr] = [{"value": best_val, "source": source}]
#             else:
#                 candidates = [best_val]
#                 use_base_threshold = best_score >= threshold_abs

#                 # Get semantic clusters if enabled
#                 clusters = []
#                 if use_semantic_clustering:
#                     clusters = ProductAttributeService.calculate_value_clusters(
#                         allowed_values, scores, cluster_threshold=0.4
#                     )
#                     best_cluster = next((c for c in clusters if best_val in c), [best_val])

#                 for val, sc in scores[1:]:
#                     # Calculate dynamic threshold for this value
#                     if use_dynamic_thresholds and extracted_attrs:
#                         dynamic_thresh = ProductAttributeService.get_dynamic_threshold(
#                             attr, val, sc, extracted_attrs, relationships,
#                             mandatory_attrs, threshold_abs
#                         )
#                     else:
#                         dynamic_thresh = threshold_abs

#                     within_margin = (best_score - sc) <= effective_margin
#                     above_threshold = sc >= dynamic_thresh

#                     # Check if in same semantic cluster as best value
#                     in_cluster = False
#                     if use_semantic_clustering and clusters:
#                         in_cluster = any(best_val in c and val in c for c in clusters)

#                     if use_base_threshold:
#                         # Best score is good, require threshold OR (cluster + margin)
#                         if above_threshold and within_margin:
#                             candidates.append(val)
#                         elif in_cluster and within_margin:
#                             candidates.append(val)
#                     else:
#                         # Best score is low, use margin OR cluster logic
#                         if within_margin:
#                             candidates.append(val)
#                         elif in_cluster and (best_score - sc) <= effective_margin * 2.0:
#                             # Extended margin for cluster members
#                             candidates.append(val)

#                 # Map each candidate to its source and create array format
#                 extracted[attr] = []
#                 for candidate in candidates:
#                     source = ProductAttributeService.find_value_source(candidate, source_map)
#                     extracted[attr].append({"value": candidate, "source": source})

#         return extracted

#     @staticmethod
#     def extract_attributes(
#         product_text: str,
#         mandatory_attrs: Dict[str, List[str]],
#         source_map: Dict[str, str] = None,
#         model: str = None,
#         extract_additional: bool = True,
#         multiple: Optional[List[str]] = None,
#         threshold_abs: float = 0.65,
#         margin: float = 0.15,
#         use_dynamic_thresholds: bool = True,
#         use_adaptive_margin: bool = True,
#         use_semantic_clustering: bool = True
#     ) -> dict:
#         """
#         Use Groq LLM to extract attributes from any product type with enhanced multi-value selection.
#         Now returns values in array format: [{"value": "...", "source": "..."}]
#         """
        
#         if model is None:
#             model = settings.SUPPORTED_MODELS[0]

#         if multiple is None:
#             multiple = []

#         if source_map is None:
#             source_map = {}

#         # Check if product text is empty or minimal
#         if not product_text or product_text == "No product information available":
#             return ProductAttributeService._create_error_response(
#                 "No product information provided",
#                 mandatory_attrs,
#                 extract_additional
#             )

#         # Create structured prompt for mandatory attributes
#         mandatory_attr_list = []
#         for attr_name, allowed_values in mandatory_attrs.items():
#             mandatory_attr_list.append(f"{attr_name}: {', '.join(allowed_values)}")
#         mandatory_attr_text = "\n".join(mandatory_attr_list)

#         additional_instruction = ""
#         if extract_additional:
#             additional_instruction = """
# 2. Extract ADDITIONAL attributes: Identify any other relevant attributes from the product text 
#    that are NOT in the mandatory list. Only include attributes where you can find actual values
#    in the product text. Do NOT include attributes with "Not Specified" or empty values.
   
#    Examples of attributes to look for (only if present): Brand, Material, Size, Color, Dimensions,
#    Weight, Features, Style, Theme, Pattern, Finish, Care Instructions, etc."""

#         output_format = {
#             "mandatory": {attr: "value or list of values" for attr in mandatory_attrs.keys()},
#         }

#         if extract_additional:
#             output_format["additional"] = {
#                 "example_attribute_1": "actual value found",
#                 "example_attribute_2": "actual value found"
#             }
#             output_format["additional"]["_note"] = "Only include attributes with actual values found in text"

#         prompt = f"""
# You are an intelligent product attribute extractor that works with ANY product type.

# TASK:
# 1. Extract MANDATORY attributes: For each mandatory attribute, select the most appropriate value(s)
#    from the provided list. Choose the value(s) that best match the product description.
# {additional_instruction}

# Product Text:
# {product_text}

# Mandatory Attribute Lists (MUST select from these allowed values):
# {mandatory_attr_text}

# CRITICAL INSTRUCTIONS:
# - Return ONLY valid JSON, nothing else
# - No explanations, no markdown, no text before or after the JSON
# - For mandatory attributes, choose the value(s) from the provided list that best match
# - If a mandatory attribute cannot be determined from the product text, use "Not Specified"
# - Prefer exact matches from the allowed values list over generic synonyms
# - If multiple values are plausible, you MAY return more than one
# {f"- For additional attributes: ONLY include attributes where you found actual values in the product text. DO NOT include attributes with 'Not Specified', 'None', 'N/A', or empty values. If you cannot find a value for an attribute, simply don't include that attribute." if extract_additional else ""}
# - Be precise and only extract information that is explicitly stated or clearly implied

# Required Output Format:
# {json.dumps(output_format, indent=2)}
#         """

#         payload = {
#             "model": model,
#             "messages": [
#                 {
#                     "role": "system",
#                     "content": f"You are a precise attribute extraction model. Return ONLY valid JSON with {'mandatory and additional' if extract_additional else 'mandatory'} sections. No explanations, no markdown, no other text."
#                 },
#                 {"role": "user", "content": prompt}
#             ],
#             "temperature": 0.0,
#             "max_tokens": 1500
#         }

#         headers = {
#             "Authorization": f"Bearer {settings.GROQ_API_KEY}",
#             "Content-Type": "application/json",
#         }

#         try:
#             response = requests.post(
#                 settings.GROQ_API_URL,
#                 headers=headers,
#                 json=payload,
#                 timeout=30
#             )
#             response.raise_for_status()
#             result_text = response.json()["choices"][0]["message"]["content"].strip()

#             # Clean the response
#             result_text = ProductAttributeService._clean_json_response(result_text)

#             # Parse JSON
#             parsed = json.loads(result_text)

#             # Validate and restructure with source tracking
#             parsed = ProductAttributeService._validate_response_structure(
#                 parsed, mandatory_attrs, extract_additional, source_map
#             )

#             # Clean up and add source tracking to additional attributes in array format
#             if extract_additional and "additional" in parsed:
#                 cleaned_additional = {}
#                 for k, v in parsed["additional"].items():
#                     if v and v not in ["Not Specified", "None", "N/A", "", "not specified", "none", "n/a"]:
#                         if not (isinstance(v, str) and v.lower() in ["not specified", "none", "n/a", ""]):
#                             # Convert to array format if not already
#                             if isinstance(v, list):
#                                 cleaned_additional[k] = []
#                                 for item in v:
#                                     if isinstance(item, dict) and "value" in item:
#                                         if "source" not in item:
#                                             item["source"] = ProductAttributeService.find_value_source(
#                                                 item["value"], source_map
#                                             )
#                                         cleaned_additional[k].append(item)
#                                     else:
#                                         source = ProductAttributeService.find_value_source(str(item), source_map)
#                                         cleaned_additional[k].append({"value": str(item), "source": source})
#                             else:
#                                 source = ProductAttributeService.find_value_source(str(v), source_map)
#                                 cleaned_additional[k] = [{"value": str(v), "source": source}]
#                 parsed["additional"] = cleaned_additional

#             # Calculate attribute relationships if using dynamic thresholds
#             relationships = {}
#             if use_dynamic_thresholds:
#                 relationships = ProductAttributeService.calculate_attribute_relationships(
#                     mandatory_attrs, product_text
#                 )

#             # Process attributes in order, allowing earlier ones to influence later ones
#             extracted_so_far = {}
#             for attr in mandatory_attrs.keys():
#                 allow_multiple = attr in multiple

#                 result = ProductAttributeService.normalize_against_product_text(
#                     product_text=product_text,
#                     mandatory_attrs={attr: mandatory_attrs[attr]},
#                     source_map=source_map,
#                     threshold_abs=threshold_abs,
#                     margin=margin,
#                     allow_multiple=allow_multiple,
#                     extracted_attrs=extracted_so_far,
#                     relationships=relationships,
#                     use_dynamic_thresholds=use_dynamic_thresholds,
#                     use_adaptive_margin=use_adaptive_margin,
#                     use_semantic_clustering=use_semantic_clustering
#                 )

#                 # Result is already in array format from normalize_against_product_text
#                 parsed["mandatory"][attr] = result[attr]
#                 extracted_so_far[attr] = result[attr]

#             return parsed

#         except requests.exceptions.RequestException as e:
#             return ProductAttributeService._create_error_response(
#                 str(e), mandatory_attrs, extract_additional
#             )
#         except json.JSONDecodeError as e:
#             return ProductAttributeService._create_error_response(
#                 f"Invalid JSON: {str(e)}", mandatory_attrs, extract_additional, result_text
#             )
#         except Exception as e:
#             return ProductAttributeService._create_error_response(
#                 str(e), mandatory_attrs, extract_additional
#             )

#     @staticmethod
#     def extract_attributes_batch(
#         products: List[Dict],
#         mandatory_attrs: Dict[str, List[str]],
#         model: str = None,
#         extract_additional: bool = True,
#         process_image: bool = True,
#         max_workers: int = 5,
#         multiple: Optional[List[str]] = None,
#         threshold_abs: float = 0.65,
#         margin: float = 0.15,
#         use_dynamic_thresholds: bool = True,
#         use_adaptive_margin: bool = True,
#         use_semantic_clustering: bool = True
#     ) -> Dict:
#         """Extract attributes for multiple products in parallel with enhanced multi-value selection and source tracking."""
#         results = []
#         successful = 0
#         failed = 0
        
#         ocr_service = OCRService()

#         if multiple is None:
#             multiple = []

#         def process_product(product_data):
#             """Process a single product."""
#             product_id = product_data.get('product_id', f"product_{len(results)}")
            
#             try:
#                 # Process image if URL is provided
#                 ocr_results = None
#                 ocr_text = None
                
#                 if process_image and product_data.get('image_url'):
#                     ocr_results = ocr_service.process_image(product_data['image_url'])
                    
#                     # Extract attributes from OCR
#                     if ocr_results and ocr_results.get('detected_text'):
#                         ocr_attrs = ProductAttributeService.extract_attributes_from_ocr(
#                             ocr_results, model
#                         )
#                         ocr_results['extracted_attributes'] = ocr_attrs
                        
#                         # Format OCR text for combining with product text
#                         ocr_text = "\n".join([
#                             f"{item['text']} (confidence: {item['confidence']:.2f})"
#                             for item in ocr_results['detected_text']
#                         ])
                
#                 # Combine all product information with source tracking
#                 product_text, source_map = ProductAttributeService.combine_product_text(
#                     title=product_data.get('title'),
#                     short_desc=product_data.get('short_desc'),
#                     long_desc=product_data.get('long_desc'),
#                     ocr_text=ocr_text
#                 )
                
#                 # Extract attributes from combined text with enhanced features
#                 result = ProductAttributeService.extract_attributes(
#                     product_text=product_text,
#                     mandatory_attrs=mandatory_attrs,
#                     source_map=source_map,
#                     model=model,
#                     extract_additional=extract_additional,
#                     multiple=multiple,
#                     threshold_abs=threshold_abs,
#                     margin=margin,
#                     use_dynamic_thresholds=use_dynamic_thresholds,
#                     use_adaptive_margin=use_adaptive_margin,
#                     use_semantic_clustering=use_semantic_clustering
#                 )
                
#                 result['product_id'] = product_id
                
#                 # Add OCR results if available (already in correct format)
#                 if ocr_results:
#                     result['ocr_results'] = ocr_results
                
#                 # Check if extraction was successful
#                 if 'error' not in result:
#                     return result, True
#                 else:
#                     return result, False
                    
#             except Exception as e:
#                 return {
#                     'product_id': product_id,
#                     'mandatory': {attr: [{"value": "Not Specified", "source": "error"}] for attr in mandatory_attrs.keys()},
#                     'additional': {} if extract_additional else None,
#                     'error': f"Processing error: {str(e)}"
#                 }, False

#         # Process products in parallel
#         with ThreadPoolExecutor(max_workers=max_workers) as executor:
#             future_to_product = {
#                 executor.submit(process_product, product): product 
#                 for product in products
#             }
            
#             for future in as_completed(future_to_product):
#                 try:
#                     result, success = future.result()
#                     results.append(result)
#                     if success:
#                         successful += 1
#                     else:
#                         failed += 1
#                 except Exception as e:
#                     failed += 1
#                     results.append({
#                         'product_id': 'unknown',
#                         'mandatory': {attr: [{"value": "Not Specified", "source": "error"}] for attr in mandatory_attrs.keys()},
#                         'additional': {} if extract_additional else None,
#                         'error': f"Unexpected error: {str(e)}"
#                     })

#         return {
#             'results': results,
#             'total_products': len(products),
#             'successful': successful,
#             'failed': failed
#         }

#     @staticmethod
#     def _clean_json_response(text: str) -> str:
#         """Clean LLM response to extract valid JSON."""
#         start_idx = text.find('{')
#         end_idx = text.rfind('}')

#         if start_idx != -1 and end_idx != -1:
#             text = text[start_idx:end_idx + 1]

#         if "```json" in text:
#             text = text.split("```json")[1].split("```")[0].strip()
#         elif "```" in text:
#             text = text.split("```")[1].split("```")[0].strip()
#             if text.startswith("json"):
#                 text = text[4:].strip()

#         return text

#     @staticmethod
#     def _validate_response_structure(
#         parsed: dict,
#         mandatory_attrs: Dict[str, List[str]],
#         extract_additional: bool,
#         source_map: Dict[str, str] = None
#     ) -> dict:
#         """Validate and fix the response structure, ensuring array format with source tracking."""
#         if source_map is None:
#             source_map = {}
        
#         expected_sections = ["mandatory"]
#         if extract_additional:
#             expected_sections.append("additional")

#         if not all(section in parsed for section in expected_sections):
#             if isinstance(parsed, dict):
#                 mandatory_keys = set(mandatory_attrs.keys())
#                 mandatory = {k: v for k, v in parsed.items() if k in mandatory_keys}
#                 additional = {k: v for k, v in parsed.items() if k not in mandatory_keys}

#                 result = {"mandatory": mandatory}
#                 if extract_additional:
#                     result["additional"] = additional
#                 parsed = result
#             else:
#                 return ProductAttributeService._create_error_response(
#                     "Invalid response structure",
#                     mandatory_attrs,
#                     extract_additional,
#                     str(parsed)
#                 )

#         # Convert mandatory attributes to array format with source tracking
#         if "mandatory" in parsed:
#             converted_mandatory = {}
#             for attr, value in parsed["mandatory"].items():
#                 if isinstance(value, list):
#                     # Already in array format, ensure each item has source
#                     converted_mandatory[attr] = []
#                     for item in value:
#                         if isinstance(item, dict) and "value" in item:
#                             # Already has proper structure
#                             if "source" not in item:
#                                 item["source"] = ProductAttributeService.find_value_source(
#                                     item["value"], source_map
#                                 )
#                             converted_mandatory[attr].append(item)
#                         else:
#                             # Convert string to proper format
#                             source = ProductAttributeService.find_value_source(str(item), source_map)
#                             converted_mandatory[attr].append({"value": str(item), "source": source})
#                 else:
#                     # Single value - convert to array format
#                     source = ProductAttributeService.find_value_source(str(value), source_map)
#                     converted_mandatory[attr] = [{"value": str(value), "source": source}]
            
#             parsed["mandatory"] = converted_mandatory

#         return parsed

#     @staticmethod
#     def _create_error_response(
#         error: str,
#         mandatory_attrs: Dict[str, List[str]],
#         extract_additional: bool,
#         raw_output: Optional[str] = None
#     ) -> dict:
#         """Create a standardized error response in array format."""
#         response = {
#             "mandatory": {attr: [{"value": "Not Specified", "source": "error"}] for attr in mandatory_attrs.keys()},
#             "error": error
#         }
#         if extract_additional:
#             response["additional"] = {}
#         if raw_output:
#             response["raw_output"] = raw_output
#         return response




























































# ==================== services.py ====================
import requests
import json
import re
from typing import Dict, List, Optional, Tuple
from django.conf import settings
from concurrent.futures import ThreadPoolExecutor, as_completed
from sentence_transformers import SentenceTransformer, util
import numpy as np
from .ocr_service import OCRService


# Initialize embedding model for normalization
model_embedder = SentenceTransformer("all-MiniLM-L6-v2")


class ProductAttributeService:
    """Service class for extracting product attributes using Groq LLM."""

    @staticmethod
    def normalize_dimension_text(text: str) -> str:
        """
        Normalize dimension text to match format like '16x20', '20x30', etc.
        Handles formats like '16 x 20', '16x1.5x20', '16 x 1.5 x 20 Inches'
        Returns the normalized dimension (e.g., '16x20') or empty string if not found.
        """
        if not text:
            return ""
        
        # Convert to lowercase and remove common units
        text = text.lower()
        text = re.sub(r'\s*(inches|inch|in|cm|centimeters|mm|millimeters)\s*', '', text, flags=re.IGNORECASE)
        
        # Extract all numbers from the text
        numbers = re.findall(r'\d+\.?\d*', text)
        
        if not numbers:
            return ""
        
        # Convert to floats first to handle decimals properly
        float_numbers = []
        for num in numbers:
            try:
                float_numbers.append(float(num))
            except:
                continue
        
        if len(float_numbers) < 2:
            return ""
        
        # If we have 3 dimensions, remove the smallest one (usually thickness/depth)
        # This handles cases like "16 x 1.5 x 20" -> keep 16 and 20, drop 1.5
        if len(float_numbers) >= 3:
            # Sort and remove the smallest
            float_numbers_sorted = sorted(float_numbers, reverse=True)
            float_numbers = float_numbers_sorted[:2]
        
        # Convert to integers for dimension matching
        int_numbers = [int(num) for num in float_numbers]
        
        # Sort to ensure consistent order (smaller x larger)
        int_numbers.sort()
        
        # Return formatted dimension
        return f"{int_numbers[0]}x{int_numbers[1]}"

    @staticmethod
    def normalize_value_for_matching(value: str, attr_name: str = "") -> str:
        """
        Normalize a value based on its attribute type for better matching.
        Currently handles dimensions specially, can be extended for other attributes.
        """
        # Check if this is a dimension-related attribute
        dimension_keywords = ['dimension', 'size', 'measurement']
        if any(keyword in attr_name.lower() for keyword in dimension_keywords):
            normalized = ProductAttributeService.normalize_dimension_text(value)
            if normalized:
                return normalized
        
        # For other attributes, just return cleaned value
        return value.strip()

    @staticmethod
    def combine_product_text(
        title: Optional[str] = None,
        short_desc: Optional[str] = None,
        long_desc: Optional[str] = None,
        ocr_text: Optional[str] = None
    ) -> Tuple[str, Dict[str, str]]:
        """
        Combine product metadata into a single text block.
        Returns: (combined_text, source_map) where source_map tracks which text came from where
        """
        parts = []
        source_map = {}
        
        if title:
            title_str = str(title).strip()
            parts.append(f"Title: {title_str}")
            source_map['title'] = title_str
        if short_desc:
            short_str = str(short_desc).strip()
            parts.append(f"Description: {short_str}")
            source_map['short_desc'] = short_str
        if long_desc:
            long_str = str(long_desc).strip()
            parts.append(f"Details: {long_str}")
            source_map['long_desc'] = long_str
        if ocr_text:
            parts.append(f"OCR Text: {ocr_text}")
            source_map['ocr_text'] = ocr_text
        
        combined = "\n".join(parts).strip()
        
        if not combined:
            return "No product information available", {}
        
        return combined, source_map

    @staticmethod
    def find_value_source(value: str, source_map: Dict[str, str], attr_name: str = "") -> str:
        """
        Find which source(s) contain the given value.
        Returns the source name(s) where the value appears.
        Now handles normalized matching for dimensions.
        """
        value_lower = value.lower()
        # Split value into tokens for better matching
        value_tokens = set(value_lower.replace("-", " ").replace("x", " ").split())
        
        # Check if this is a dimension-related attribute
        is_dimension_attr = any(keyword in attr_name.lower() for keyword in ['dimension', 'size', 'measurement'])
        
        sources_found = []
        source_scores = {}
        
        for source_name, source_text in source_map.items():
            source_lower = source_text.lower()
            
            # Check for exact phrase match first
            if value_lower in source_lower:
                source_scores[source_name] = 1.0
                continue
            
            # For dimensions, check normalized match
            if is_dimension_attr:
                # Normalize the value (e.g., "16x20" stays "16x20")
                normalized_value = ProductAttributeService.normalize_dimension_text(value)
                if not normalized_value:
                    normalized_value = value.replace("x", " ").strip()
                
                # Normalize the source text to extract dimensions
                normalized_source = ProductAttributeService.normalize_dimension_text(source_text)
                
                # Direct match
                if normalized_value == normalized_source:
                    source_scores[source_name] = 0.95
                    continue
                
                # Also check if the dimension numbers appear in the source
                # Extract dimension parts (e.g., "16x20" -> ["16", "20"])
                dim_parts = normalized_value.split("x") if "x" in normalized_value else []
                if len(dim_parts) == 2:
                    # Check if both numbers appear in the source
                    if all(part in source_text for part in dim_parts):
                        source_scores[source_name] = 0.85
                        continue
            
            # Check for token matches
            token_matches = sum(1 for token in value_tokens if token and token in source_lower)
            if token_matches > 0 and len(value_tokens) > 0:
                source_scores[source_name] = token_matches / len(value_tokens)
        
        # Return source with highest score, or all sources if multiple have same score
        if source_scores:
            max_score = max(source_scores.values())
            sources_found = [s for s, score in source_scores.items() if score == max_score]
            
            # Prioritize: title > short_desc > long_desc > ocr_text
            priority = ['title', 'short_desc', 'long_desc', 'ocr_text']
            for p in priority:
                if p in sources_found:
                    return p
            
            return sources_found[0] if sources_found else "Not found"
        
        return "Not found"

    @staticmethod
    def format_visual_attributes(visual_attributes: Dict) -> Dict:
        """
        Convert visual attributes to array format with source tracking.
        Source is always 'image' for visual attributes.
        """
        formatted = {}
        
        for key, value in visual_attributes.items():
            if isinstance(value, list):
                # Already a list (like color_palette)
                formatted[key] = [{"value": str(item), "source": "image"} for item in value]
            elif isinstance(value, dict):
                # Nested dictionary - format recursively
                nested_formatted = {}
                for nested_key, nested_value in value.items():
                    if isinstance(nested_value, list):
                        nested_formatted[nested_key] = [{"value": str(item), "source": "image"} for item in nested_value]
                    else:
                        nested_formatted[nested_key] = [{"value": str(nested_value), "source": "image"}]
                formatted[key] = nested_formatted
            else:
                # Single value
                formatted[key] = [{"value": str(value), "source": "image"}]
        
        return formatted

    @staticmethod
    def extract_attributes_from_ocr(ocr_results: Dict, model: str = None) -> Dict:
        """Extract structured attributes from OCR text using LLM."""
        if model is None:
            model = settings.SUPPORTED_MODELS[0]
        
        detected_text = ocr_results.get('detected_text', [])
        if not detected_text:
            return {}
        
        # Format OCR text for prompt
        ocr_text = "\n".join([f"Text: {item['text']}, Confidence: {item['confidence']:.2f}" 
                              for item in detected_text])
        
        prompt = f"""
You are an AI model that extracts structured attributes from OCR text detected on product images.
Given the OCR detections below, infer the possible product attributes and return them as a clean JSON object.

OCR Text:
{ocr_text}

Extract relevant attributes like:
- brand
- model_number
- size (waist_size, length, etc.)
- collection
- any other relevant product information

Return a JSON object with only the attributes you can confidently identify.
If an attribute is not present, do not include it in the response.
"""
        
        payload = {
            "model": model,
            "messages": [
                {
                    "role": "system",
                    "content": "You are a helpful AI that extracts structured data from OCR output. Return only valid JSON."
                },
                {"role": "user", "content": prompt}
            ],
            "temperature": 0.2,
            "max_tokens": 500
        }
        
        headers = {
            "Authorization": f"Bearer {settings.GROQ_API_KEY}",
            "Content-Type": "application/json",
        }
        
        try:
            response = requests.post(
                settings.GROQ_API_URL,
                headers=headers,
                json=payload,
                timeout=30
            )
            response.raise_for_status()
            result_text = response.json()["choices"][0]["message"]["content"].strip()
            
            # Clean and parse JSON
            result_text = ProductAttributeService._clean_json_response(result_text)
            parsed = json.loads(result_text)
            
            # Convert to array format with source tracking
            formatted_attributes = {}
            for key, value in parsed.items():
                if key == "error":
                    continue
                
                # Handle nested dictionaries (like size)
                if isinstance(value, dict):
                    nested_formatted = {}
                    for nested_key, nested_value in value.items():
                        nested_formatted[nested_key] = [{"value": str(nested_value), "source": "image"}]
                    formatted_attributes[key] = nested_formatted
                elif isinstance(value, list):
                    # Already a list, convert each item
                    formatted_attributes[key] = [{"value": str(item), "source": "image"} for item in value]
                else:
                    # Single value
                    formatted_attributes[key] = [{"value": str(value), "source": "image"}]
            
            return formatted_attributes
        except Exception as e:
            return {"error": f"Failed to extract attributes from OCR: {str(e)}"}

    @staticmethod
    def calculate_attribute_relationships(
        mandatory_attrs: Dict[str, List[str]],
        product_text: str
    ) -> Dict[str, float]:
        """
        Calculate semantic relationships between attribute values across different attributes.
        Returns a matrix of cross-attribute value similarities.
        """
        pt_emb = model_embedder.encode(product_text, convert_to_tensor=True)

        # Calculate similarities between all attribute values and product text
        attr_scores = {}
        for attr, values in mandatory_attrs.items():
            attr_scores[attr] = {}
            for val in values:
                contexts = [val, f"for {val}", f"use in {val}", f"suitable for {val}"]
                ctx_embs = [model_embedder.encode(c, convert_to_tensor=True) for c in contexts]
                sem_sim = max(float(util.cos_sim(pt_emb, ce).item()) for ce in ctx_embs)
                attr_scores[attr][val] = sem_sim

        # Calculate cross-attribute value relationships
        relationships = {}
        attr_list = list(mandatory_attrs.keys())

        for i, attr1 in enumerate(attr_list):
            for attr2 in attr_list[i+1:]:
                # Calculate pairwise similarities between values of different attributes
                for val1 in mandatory_attrs[attr1]:
                    for val2 in mandatory_attrs[attr2]:
                        emb1 = model_embedder.encode(val1, convert_to_tensor=True)
                        emb2 = model_embedder.encode(val2, convert_to_tensor=True)
                        sim = float(util.cos_sim(emb1, emb2).item())

                        # Store bidirectional relationships
                        key1 = f"{attr1}:{val1}->{attr2}:{val2}"
                        key2 = f"{attr2}:{val2}->{attr1}:{val1}"
                        relationships[key1] = sim
                        relationships[key2] = sim

        return relationships

    @staticmethod
    def calculate_value_clusters(
        values: List[str],
        scores: List[Tuple[str, float]],
        cluster_threshold: float = 0.4
    ) -> List[List[str]]:
        """
        Group values into semantic clusters based on their similarity to each other.
        Returns clusters of related values.
        """
        if len(values) <= 1:
            return [[val] for val, _ in scores]

        # Get embeddings for all values
        embeddings = [model_embedder.encode(val, convert_to_tensor=True) for val in values]

        # Calculate pairwise similarities
        similarity_matrix = np.zeros((len(values), len(values)))
        for i in range(len(values)):
            for j in range(i+1, len(values)):
                sim = float(util.cos_sim(embeddings[i], embeddings[j]).item())
                similarity_matrix[i][j] = sim
                similarity_matrix[j][i] = sim

        # Simple clustering: group values with high similarity
        clusters = []
        visited = set()

        for i, (val, score) in enumerate(scores):
            if i in visited:
                continue

            cluster = [val]
            visited.add(i)

            # Find similar values
            for j in range(len(values)):
                if j not in visited and similarity_matrix[i][j] >= cluster_threshold:
                    cluster.append(values[j])
                    visited.add(j)

            clusters.append(cluster)

        return clusters

    @staticmethod
    def get_dynamic_threshold(
        attr: str,
        val: str,
        base_score: float,
        extracted_attrs: Dict[str, List[Dict[str, str]]],
        relationships: Dict[str, float],
        mandatory_attrs: Dict[str, List[str]],
        base_threshold: float = 0.65,
        boost_factor: float = 0.15
    ) -> float:
        """
        Calculate dynamic threshold based on relationships with already-extracted attributes.
        """
        threshold = base_threshold

        # Check relationships with already extracted attributes
        max_relationship = 0.0
        for other_attr, other_values_list in extracted_attrs.items():
            if other_attr == attr:
                continue

            for other_val_dict in other_values_list:
                other_val = other_val_dict['value']
                key = f"{attr}:{val}->{other_attr}:{other_val}"
                if key in relationships:
                    max_relationship = max(max_relationship, relationships[key])

        # If strong relationship exists, lower threshold
        if max_relationship > 0.6:
            threshold = base_threshold - (boost_factor * max_relationship)

        return max(0.3, threshold)

    @staticmethod
    def get_adaptive_margin(
        scores: List[Tuple[str, float]],
        base_margin: float = 0.15,
        max_margin: float = 0.22
    ) -> float:
        """
        Calculate adaptive margin based on score distribution.
        """
        if len(scores) < 2:
            return base_margin

        score_values = [s for _, s in scores]
        best_score = score_values[0]

        # If best score is very low, use adaptive margin but be more conservative
        if best_score < 0.5:
            # Calculate score spread in top 3-4 scores only (more selective)
            top_scores = score_values[:min(4, len(score_values))]
            score_range = max(top_scores) - min(top_scores)

            # Very controlled margin increase
            if score_range < 0.30:
                # Much more conservative scaling
                score_factor = (0.5 - best_score) * 0.35
                adaptive = base_margin + score_factor + (0.30 - score_range) * 0.2
                return min(adaptive, max_margin)

        return base_margin

    @staticmethod
    def _lexical_evidence(product_text: str, label: str) -> float:
        """Calculate lexical overlap between product text and label."""
        pt = product_text.lower()
        tokens = [t for t in label.lower().replace("-", " ").split() if t]
        if not tokens:
            return 0.0
        hits = sum(1 for t in tokens if t in pt)
        return hits / len(tokens)

    @staticmethod
    def normalize_against_product_text(
        product_text: str,
        mandatory_attrs: Dict[str, List[str]],
        source_map: Dict[str, str],
        threshold_abs: float = 0.65,
        margin: float = 0.15,
        allow_multiple: bool = False,
        sem_weight: float = 0.8,
        lex_weight: float = 0.2,
        extracted_attrs: Optional[Dict[str, List[Dict[str, str]]]] = None,
        relationships: Optional[Dict[str, float]] = None,
        use_dynamic_thresholds: bool = True,
        use_adaptive_margin: bool = True,
        use_semantic_clustering: bool = True
    ) -> dict:
        """
        Score each allowed value against the product_text with dynamic thresholds.
        Returns dict with values in array format: [{"value": "...", "source": "..."}]
        """
        if extracted_attrs is None:
            extracted_attrs = {}
        if relationships is None:
            relationships = {}

        pt_emb = model_embedder.encode(product_text, convert_to_tensor=True)
        extracted = {}

        for attr, allowed_values in mandatory_attrs.items():
            scores: List[Tuple[str, float]] = []
            
            # Normalize product text for dimension matching if needed
            normalized_product_text = product_text
            is_dimension_attr = any(keyword in attr.lower() for keyword in ['dimension', 'size', 'measurement'])

            for val in allowed_values:
                # For dimension attributes, try normalized matching first
                if is_dimension_attr:
                    normalized_val = ProductAttributeService.normalize_dimension_text(val)
                    normalized_pt = ProductAttributeService.normalize_dimension_text(product_text)
                    
                    # If we find exact normalized match, give it highest score
                    if normalized_val and normalized_pt and normalized_val == normalized_pt:
                        scores.append((val, 0.99))
                        continue
                    # Also check if dimension appears anywhere in the text
                    elif normalized_val and normalized_val in normalized_pt:
                        scores.append((val, 0.95))
                        continue
                
                # Standard semantic matching
                contexts = [val, f"for {val}", f"use in {val}", f"suitable for {val}", f"{val} room"]
                ctx_embs = [model_embedder.encode(c, convert_to_tensor=True) for c in contexts]
                sem_sim = max(float(util.cos_sim(pt_emb, ce).item()) for ce in ctx_embs)

                lex_score = ProductAttributeService._lexical_evidence(product_text, val)
                final_score = sem_weight * sem_sim + lex_weight * lex_score
                scores.append((val, final_score))

            scores.sort(key=lambda x: x[1], reverse=True)
            best_val, best_score = scores[0]

            # Calculate adaptive margin if enabled
            effective_margin = margin
            if allow_multiple and use_adaptive_margin:
                effective_margin = ProductAttributeService.get_adaptive_margin(scores, margin)

            # Special handling for dimension attributes with exact matches
            # If we have a very high score (0.90+), it means we found an exact/normalized match
            # In this case, don't apply multiple selection logic - just return the best match
            if is_dimension_attr and best_score >= 0.90:
                source = ProductAttributeService.find_value_source(best_val, source_map, attr)
                extracted[attr] = [{"value": best_val, "source": source}]
                continue

            if not allow_multiple:
                source = ProductAttributeService.find_value_source(best_val, source_map, attr)
                extracted[attr] = [{"value": best_val, "source": source}]
            else:
                candidates = [best_val]
                use_base_threshold = best_score >= threshold_abs

                # Get semantic clusters if enabled
                clusters = []
                if use_semantic_clustering:
                    clusters = ProductAttributeService.calculate_value_clusters(
                        allowed_values, scores, cluster_threshold=0.4
                    )
                    best_cluster = next((c for c in clusters if best_val in c), [best_val])

                for val, sc in scores[1:]:
                    # Skip values with very low scores - they're likely not relevant
                    # For dimension attributes, be even more strict
                    min_score = 0.4 if is_dimension_attr else 0.3
                    if sc < min_score:
                        continue
                    
                    # Calculate dynamic threshold for this value
                    if use_dynamic_thresholds and extracted_attrs:
                        dynamic_thresh = ProductAttributeService.get_dynamic_threshold(
                            attr, val, sc, extracted_attrs, relationships,
                            mandatory_attrs, threshold_abs
                        )
                    else:
                        dynamic_thresh = threshold_abs

                    within_margin = (best_score - sc) <= effective_margin
                    above_threshold = sc >= dynamic_thresh

                    # Check if in same semantic cluster as best value
                    in_cluster = False
                    if use_semantic_clustering and clusters:
                        in_cluster = any(best_val in c and val in c for c in clusters)

                    if use_base_threshold:
                        # Best score is good, require threshold OR (cluster + margin)
                        if above_threshold and within_margin:
                            candidates.append(val)
                        elif in_cluster and within_margin:
                            candidates.append(val)
                    else:
                        # Best score is low, use margin OR cluster logic
                        if within_margin:
                            candidates.append(val)
                        elif in_cluster and (best_score - sc) <= effective_margin * 2.0:
                            # Extended margin for cluster members
                            candidates.append(val)

                # Map each candidate to its source and create array format
                extracted[attr] = []
                for candidate in candidates:
                    source = ProductAttributeService.find_value_source(candidate, source_map, attr)
                    extracted[attr].append({"value": candidate, "source": source})

        return extracted

    @staticmethod
    def extract_attributes(
        product_text: str,
        mandatory_attrs: Dict[str, List[str]],
        source_map: Dict[str, str] = None,
        model: str = None,
        extract_additional: bool = True,
        multiple: Optional[List[str]] = None,
        threshold_abs: float = 0.65,
        margin: float = 0.15,
        use_dynamic_thresholds: bool = True,
        use_adaptive_margin: bool = True,
        use_semantic_clustering: bool = True
    ) -> dict:
        """
        Use Groq LLM to extract attributes from any product type with enhanced multi-value selection.
        Now returns values in array format: [{"value": "...", "source": "..."}]
        """
        
        if model is None:
            model = settings.SUPPORTED_MODELS[0]

        if multiple is None:
            multiple = []

        if source_map is None:
            source_map = {}

        # Check if product text is empty or minimal
        if not product_text or product_text == "No product information available":
            return ProductAttributeService._create_error_response(
                "No product information provided",
                mandatory_attrs,
                extract_additional
            )

        # Create structured prompt for mandatory attributes
        mandatory_attr_list = []
        for attr_name, allowed_values in mandatory_attrs.items():
            mandatory_attr_list.append(f"{attr_name}: {', '.join(allowed_values)}")
        mandatory_attr_text = "\n".join(mandatory_attr_list)

        additional_instruction = ""
        if extract_additional:
            additional_instruction = """
2. Extract ADDITIONAL attributes: Identify any other relevant attributes from the product text 
   that are NOT in the mandatory list. Only include attributes where you can find actual values
   in the product text. Do NOT include attributes with "Not Specified" or empty values.
   
   Examples of attributes to look for (only if present): Brand, Material, Size, Color, Dimensions,
   Weight, Features, Style, Theme, Pattern, Finish, Care Instructions, etc."""

        output_format = {
            "mandatory": {attr: "value or list of values" for attr in mandatory_attrs.keys()},
        }

        if extract_additional:
            output_format["additional"] = {
                "example_attribute_1": "actual value found",
                "example_attribute_2": "actual value found"
            }
            output_format["additional"]["_note"] = "Only include attributes with actual values found in text"

        prompt = f"""
You are an intelligent product attribute extractor that works with ANY product type.

TASK:
1. Extract MANDATORY attributes: For each mandatory attribute, select the most appropriate value(s)
   from the provided list. Choose the value(s) that best match the product description.
{additional_instruction}

Product Text:
{product_text}

Mandatory Attribute Lists (MUST select from these allowed values):
{mandatory_attr_text}

CRITICAL INSTRUCTIONS:
- Return ONLY valid JSON, nothing else
- No explanations, no markdown, no text before or after the JSON
- For mandatory attributes, choose the value(s) from the provided list that best match
- If a mandatory attribute cannot be determined from the product text, use "Not Specified"
- Prefer exact matches from the allowed values list over generic synonyms
- If multiple values are plausible, you MAY return more than one
{f"- For additional attributes: ONLY include attributes where you found actual values in the product text. DO NOT include attributes with 'Not Specified', 'None', 'N/A', or empty values. If you cannot find a value for an attribute, simply don't include that attribute." if extract_additional else ""}
- Be precise and only extract information that is explicitly stated or clearly implied

Required Output Format:
{json.dumps(output_format, indent=2)}
        """

        payload = {
            "model": model,
            "messages": [
                {
                    "role": "system",
                    "content": f"You are a precise attribute extraction model. Return ONLY valid JSON with {'mandatory and additional' if extract_additional else 'mandatory'} sections. No explanations, no markdown, no other text."
                },
                {"role": "user", "content": prompt}
            ],
            "temperature": 0.0,
            "max_tokens": 1500
        }

        headers = {
            "Authorization": f"Bearer {settings.GROQ_API_KEY}",
            "Content-Type": "application/json",
        }

        try:
            response = requests.post(
                settings.GROQ_API_URL,
                headers=headers,
                json=payload,
                timeout=30
            )
            response.raise_for_status()
            result_text = response.json()["choices"][0]["message"]["content"].strip()

            # Clean the response
            result_text = ProductAttributeService._clean_json_response(result_text)

            # Parse JSON
            parsed = json.loads(result_text)

            # Validate and restructure with source tracking
            parsed = ProductAttributeService._validate_response_structure(
                parsed, mandatory_attrs, extract_additional, source_map
            )

            # Clean up and add source tracking to additional attributes in array format
            if extract_additional and "additional" in parsed:
                cleaned_additional = {}
                for k, v in parsed["additional"].items():
                    if v and v not in ["Not Specified", "None", "N/A", "", "not specified", "none", "n/a"]:
                        if not (isinstance(v, str) and v.lower() in ["not specified", "none", "n/a", ""]):
                            # Convert to array format if not already
                            if isinstance(v, list):
                                cleaned_additional[k] = []
                                for item in v:
                                    if isinstance(item, dict) and "value" in item:
                                        if "source" not in item:
                                            item["source"] = ProductAttributeService.find_value_source(
                                                item["value"], source_map, k
                                            )
                                        cleaned_additional[k].append(item)
                                    else:
                                        source = ProductAttributeService.find_value_source(str(item), source_map, k)
                                        cleaned_additional[k].append({"value": str(item), "source": source})
                            else:
                                source = ProductAttributeService.find_value_source(str(v), source_map, k)
                                cleaned_additional[k] = [{"value": str(v), "source": source}]
                parsed["additional"] = cleaned_additional

            # Calculate attribute relationships if using dynamic thresholds
            relationships = {}
            if use_dynamic_thresholds:
                relationships = ProductAttributeService.calculate_attribute_relationships(
                    mandatory_attrs, product_text
                )

            # Process attributes in order, allowing earlier ones to influence later ones
            extracted_so_far = {}
            for attr in mandatory_attrs.keys():
                allow_multiple = attr in multiple

                result = ProductAttributeService.normalize_against_product_text(
                    product_text=product_text,
                    mandatory_attrs={attr: mandatory_attrs[attr]},
                    source_map=source_map,
                    threshold_abs=threshold_abs,
                    margin=margin,
                    allow_multiple=allow_multiple,
                    extracted_attrs=extracted_so_far,
                    relationships=relationships,
                    use_dynamic_thresholds=use_dynamic_thresholds,
                    use_adaptive_margin=use_adaptive_margin,
                    use_semantic_clustering=use_semantic_clustering
                )

                # Result is already in array format from normalize_against_product_text
                parsed["mandatory"][attr] = result[attr]
                extracted_so_far[attr] = result[attr]

            return parsed

        except requests.exceptions.RequestException as e:
            return ProductAttributeService._create_error_response(
                str(e), mandatory_attrs, extract_additional
            )
        except json.JSONDecodeError as e:
            return ProductAttributeService._create_error_response(
                f"Invalid JSON: {str(e)}", mandatory_attrs, extract_additional, result_text
            )
        except Exception as e:
            return ProductAttributeService._create_error_response(
                str(e), mandatory_attrs, extract_additional
            )

    @staticmethod
    def extract_attributes_batch(
        products: List[Dict],
        mandatory_attrs: Dict[str, List[str]],
        model: str = None,
        extract_additional: bool = True,
        process_image: bool = True,
        max_workers: int = 5,
        multiple: Optional[List[str]] = None,
        threshold_abs: float = 0.65,
        margin: float = 0.15,
        use_dynamic_thresholds: bool = True,
        use_adaptive_margin: bool = True,
        use_semantic_clustering: bool = True
    ) -> Dict:
        """Extract attributes for multiple products in parallel with enhanced multi-value selection and source tracking."""
        results = []
        successful = 0
        failed = 0
        
        ocr_service = OCRService()

        if multiple is None:
            multiple = []

        def process_product(product_data):
            """Process a single product."""
            product_id = product_data.get('product_id', f"product_{len(results)}")
            
            try:
                # Process image if URL is provided
                ocr_results = None
                ocr_text = None
                
                if process_image and product_data.get('image_url'):
                    ocr_results = ocr_service.process_image(product_data['image_url'])
                    
                    # Extract attributes from OCR
                    if ocr_results and ocr_results.get('detected_text'):
                        ocr_attrs = ProductAttributeService.extract_attributes_from_ocr(
                            ocr_results, model
                        )
                        ocr_results['extracted_attributes'] = ocr_attrs
                        
                        # Format OCR text for combining with product text
                        ocr_text = "\n".join([
                            f"{item['text']} (confidence: {item['confidence']:.2f})"
                            for item in ocr_results['detected_text']
                        ])
                
                # Combine all product information with source tracking
                product_text, source_map = ProductAttributeService.combine_product_text(
                    title=product_data.get('title'),
                    short_desc=product_data.get('short_desc'),
                    long_desc=product_data.get('long_desc'),
                    ocr_text=ocr_text
                )
                
                # Extract attributes from combined text with enhanced features
                result = ProductAttributeService.extract_attributes(
                    product_text=product_text,
                    mandatory_attrs=mandatory_attrs,
                    source_map=source_map,
                    model=model,
                    extract_additional=extract_additional,
                    multiple=multiple,
                    threshold_abs=threshold_abs,
                    margin=margin,
                    use_dynamic_thresholds=use_dynamic_thresholds,
                    use_adaptive_margin=use_adaptive_margin,
                    use_semantic_clustering=use_semantic_clustering
                )
                
                result['product_id'] = product_id
                
                # Add OCR results if available (already in correct format)
                if ocr_results:
                    result['ocr_results'] = ocr_results
                
                # Check if extraction was successful
                if 'error' not in result:
                    return result, True
                else:
                    return result, False
                    
            except Exception as e:
                return {
                    'product_id': product_id,
                    'mandatory': {attr: [{"value": "Not Specified", "source": "error"}] for attr in mandatory_attrs.keys()},
                    'additional': {} if extract_additional else None,
                    'error': f"Processing error: {str(e)}"
                }, False

        # Process products in parallel
        with ThreadPoolExecutor(max_workers=max_workers) as executor:
            future_to_product = {
                executor.submit(process_product, product): product 
                for product in products
            }
            
            for future in as_completed(future_to_product):
                try:
                    result, success = future.result()
                    results.append(result)
                    if success:
                        successful += 1
                    else:
                        failed += 1
                except Exception as e:
                    failed += 1
                    results.append({
                        'product_id': 'unknown',
                        'mandatory': {attr: [{"value": "Not Specified", "source": "error"}] for attr in mandatory_attrs.keys()},
                        'additional': {} if extract_additional else None,
                        'error': f"Unexpected error: {str(e)}"
                    })

        return {
            'results': results,
            'total_products': len(products),
            'successful': successful,
            'failed': failed
        }

    @staticmethod
    def _clean_json_response(text: str) -> str:
        """Clean LLM response to extract valid JSON."""
        start_idx = text.find('{')
        end_idx = text.rfind('}')

        if start_idx != -1 and end_idx != -1:
            text = text[start_idx:end_idx + 1]

        if "```json" in text:
            text = text.split("```json")[1].split("```")[0].strip()
        elif "```" in text:
            text = text.split("```")[1].split("```")[0].strip()
            if text.startswith("json"):
                text = text[4:].strip()

        return text

    @staticmethod
    def _validate_response_structure(
        parsed: dict,
        mandatory_attrs: Dict[str, List[str]],
        extract_additional: bool,
        source_map: Dict[str, str] = None
    ) -> dict:
        """Validate and fix the response structure, ensuring array format with source tracking."""
        if source_map is None:
            source_map = {}
        
        expected_sections = ["mandatory"]
        if extract_additional:
            expected_sections.append("additional")

        if not all(section in parsed for section in expected_sections):
            if isinstance(parsed, dict):
                mandatory_keys = set(mandatory_attrs.keys())
                mandatory = {k: v for k, v in parsed.items() if k in mandatory_keys}
                additional = {k: v for k, v in parsed.items() if k not in mandatory_keys}

                result = {"mandatory": mandatory}
                if extract_additional:
                    result["additional"] = additional
                parsed = result
            else:
                return ProductAttributeService._create_error_response(
                    "Invalid response structure",
                    mandatory_attrs,
                    extract_additional,
                    str(parsed)
                )

        # Convert mandatory attributes to array format with source tracking
        if "mandatory" in parsed:
            converted_mandatory = {}
            for attr, value in parsed["mandatory"].items():
                if isinstance(value, list):
                    # Already in array format, ensure each item has source
                    converted_mandatory[attr] = []
                    for item in value:
                        if isinstance(item, dict) and "value" in item:
                            # Already has proper structure
                            if "source" not in item:
                                item["source"] = ProductAttributeService.find_value_source(
                                    item["value"], source_map, attr
                                )
                            converted_mandatory[attr].append(item)
                        else:
                            # Convert string to proper format
                            source = ProductAttributeService.find_value_source(str(item), source_map, attr)
                            converted_mandatory[attr].append({"value": str(item), "source": source})
                else:
                    # Single value - convert to array format
                    source = ProductAttributeService.find_value_source(str(value), source_map, attr)
                    converted_mandatory[attr] = [{"value": str(value), "source": source}]
            
            parsed["mandatory"] = converted_mandatory

        return parsed

    @staticmethod
    def _create_error_response(
        error: str,
        mandatory_attrs: Dict[str, List[str]],
        extract_additional: bool,
        raw_output: Optional[str] = None
    ) -> dict:
        """Create a standardized error response in array format."""
        response = {
            "mandatory": {attr: [{"value": "Not Specified", "source": "error"}] for attr in mandatory_attrs.keys()},
            "error": error
        }
        if extract_additional:
            response["additional"] = {}
        if raw_output:
            response["raw_output"] = raw_output
        return response