import os
import time
import asyncio
import hashlib
from typing import Optional
from datetime import datetime
import re
from concurrent.futures import ThreadPoolExecutor
from typing import List, Dict, Any

from groq import Groq
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline, AutoModelForCausalLM
import torch
import numpy as np

from core.models.knowledge_base import OptimizedGazaKnowledgeBase
from core.fact_checker import MedicalFactChecker, clean_ocr_artifacts
from core.utils.config import (
    MEDICAL_SYSTEM_PROMPT,
    GROQ_API_KEY,
    FLAN_MODEL_NAME,
    FALLBACK_MODEL_NAME,
    MAX_CACHE_SIZE,
    MAX_CONTEXT_CHARS
)

from transformers import pipeline

print("🧪 Logger test: ", 'logger' in globals())
from core.utils.logger import logger


from transformers import pipeline

class ArabicTranslator:
    def __init__(self):
        try:
            self.en_to_ar = pipeline("translation", model="facebook/m2m100_418M", src_lang="en", tgt_lang="ar")
            self.ar_to_en = pipeline("translation", model="facebook/m2m100_418M", src_lang="ar", tgt_lang="en")
            print("✅ Translation models loaded")
        except Exception as e:
            print(f"❌ Failed to load translation models: {e}")
            self.en_to_ar = None
            self.ar_to_en = None

    def translate_to_english(self, text):
        if not self.ar_to_en:
            print("⚠️ Arabic-to-English translation model not available.")
            return text
        return self.ar_to_en(text[:1000], max_length=1024)[0]["translation_text"]

    def translate_to_arabic(self, text):
        if not self.en_to_ar:
            print("⚠️ English-to-Arabic translation model not available.")
            return text
        # Strip markdown artifacts (prevent "* * *")
        clean_text = re.sub(r'[*_`~#>]', '', text)
        MAX_INPUT = 900  # Stay below token limits
        if len(clean_text) > MAX_INPUT:
            print(f"⚠️ Input too long ({len(clean_text)}), truncating for translation.")
            clean_text = clean_text[:MAX_INPUT]
        return self.en_to_ar(clean_text, max_length=1024)[0]["translation_text"]


    def translate(self, text: str, direction: str = "to_en") -> str:
        if direction == "to_en":
            return self.translate_to_english(text)
        elif direction == "to_ar":
            return self.translate_to_arabic(text)
        else:
            raise ValueError("Invalid translation direction: choose 'to_en' or 'to_ar'")



class OptimizedGazaRAGSystem:
    """Optimized RAG system using pre-made assets"""

    
    def __init__(self, vector_store_dir: str = "./vector_store"):
        self.knowledge_base = OptimizedGazaKnowledgeBase(vector_store_dir)
        self.fact_checker = MedicalFactChecker()
        self.groq_client = None 
        self.llm = None
        self.tokenizer = None
        self.use_native_generation = True  # or False by config/env
        self.system_prompt = self._create_system_prompt()
        self.arabic_translator = ArabicTranslator()
        self.generation_pipeline = None
        self.response_cache = {}
        self.executor = ThreadPoolExecutor(max_workers=2)
        self.definitive_patterns = [
            re.compile(r, re.IGNORECASE) for r in [
                r'will\s+(?:cure|heal|fix)\b',  # Only block definitive claims
                r'guaranteed\s+to',
                r'completely\s+(?:safe|effective)\b',
                r'\b(?:inject|syringe)\b'  # Added dangerous procedures
            ]
        ]

        translated_test = self.arabic_translator.translate("How do I treat a wound?")
        print("🔥 Arabic test translation:", translated_test)




        
    def initialize(self):
        """Initialize the optimized RAG system"""
        logger.info("🚀 Initializing Optimized Gaza RAG System...")
        self.knowledge_base.initialize()
        logger.info("✅ Optimized Gaza RAG System ready!")


    def _initialize_groq(self):
        """Initialize Groq client with proper error handling"""
        try:
            api_key = os.getenv("GROQ_API_KEY")
            if not api_key:
                logger.warning("⚠️ GROQ_API_KEY environment variable not set")
                return None
            client = Groq(api_key=api_key)
            # Test the connection with a simple API call
            try:
                client.models.list()  # Simple API call to verify key
                logger.info("✅ Groq client initialized successfully")
                return client
            except Exception as auth_error:
                logger.error(f"❌ Groq API key invalid: {auth_error}")
                return None
        except Exception as e:
            logger.error(f"❌ Groq initialization failed: {e}")
            return None


    def generate_raw_text(self, prompt: str) -> str:
        """Direct text generation without RAG, safety checks, or translation."""
        if not self.generation_pipeline:
            self._initialize_llm()
        output = self.generation_pipeline(prompt)
        return output[0]["generated_text"].strip() if output else ""



    def _format_kb_response(self, text: str) -> str:
        """Conditionally expands short KB entries using FLAN→Groq pipeline"""
        clean_text = clean_ocr_artifacts(text).strip()

        def _get_groq_client():
            from groq import Groq
            return Groq(api_key=os.getenv("GROQ_API_KEY"))
            
        # FIXED HEURISTIC: Increased word count threshold and narrowed keyword list
        is_detailed = len(clean_text.split()) > 200 and any(
            kw in clean_text.lower()
            for kw in ["fracture", "bleeding", "wound", "infection"]
        )
        
        if is_detailed:
            return f"📚 **Comprehensive Medical Guidance:**\n\n{clean_text}"
            # Otherwise, enrich it dynamically using FLAN + Groq
        try:
            refined_prompt = self._create_prompt_from_rag(query=clean_text, rag_results=[])
            enriched = self._generate_with_groq(query=clean_text, refined_prompt=refined_prompt)
            return f"📚 **Comprehensive Medical Guidance:**\n\n{enriched}"
        except Exception as e:
            logger.warning(f"[FormatKB] Enrichment failed: {e}")
            return f"📚 **Basic Medical Information:**\n\n{clean_text}\n\n⚠️ Could not expand this content automatically."

   
    

    def _create_system_prompt(self) -> str:
        """Enhanced system prompt for Gaza context"""
        MEDICAL_SYSTEM_PROMPT = """
[STRICT GAZA MEDICAL PROTOCOL]
You are a WHO-certified medical assistant for Gaza. You MUST:
1. Follow WHO war-zone protocols
2. Reject unsafe treatments (ESPECIALLY syringe use for burns)
3. Prioritize resource-scarce solutions
4. Add Islamic medical considerations
5. Format responses clearly with:
   - 🩹 Immediate Actions
   - ⚠️ Contraindications  
   - 💡 Resource Alternatives
6. Include source references [Source X]
7. Always add: "📞 Verify with Gaza Red Crescent (101)" for serious cases

OUTPUT EXAMPLE:
### Burn Treatment ###
🩹 Cool with clean water for 10-20 mins [Source 1]
⚠️ Never apply ice directly [Source 2]  
💡 Use clean damp cloth if water scarce [Source 3]

📍 Gaza Context: Adapt based on available supplies
📞 Verify with Gaza Red Crescent (101) if severe
"""
        return  MEDICAL_SYSTEM_PROMPT

         
    def _initialize_llm(self):
        """Load medical FLAN-T5 model with proper error handling and optimizations"""
        model_name = "rivapereira123/medical-flan-t5"
        try:
            logger.info(f"🔄 Loading medical FLAN-T5 model: {model_name}")
            self.tokenizer = AutoTokenizer.from_pretrained(
            model_name,
            cache_dir="./model_cache"  # Optional local caching
            )
            self.llm = AutoModelForSeq2SeqLM.from_pretrained(
            model_name,
            low_cpu_mem_usage=True,  # Critical for CPU
            )
            
            self.generation_pipeline = pipeline(
            "text2text-generation",
            model=self.llm,
            tokenizer=self.tokenizer,
            max_length=512,  # Prevent OOM errors
            truncation=True
            )
            logger.info("✅ Medical FLAN-T5 loaded successfully (CPU mode)")
        except Exception as e:
            logger.error(f"❌ Critical error loading model: {str(e)}")
            logger.warning("⚠️ Medical QA features will be disabled")
            self.llm = None
            self.tokenizer = None
            self.generation_pipeline = None


    
    def _initialize_fallback_llm(self):
        """Enhanced fallback model with better error handling"""
        try:
            logger.info("🔄 Loading fallback model...")
            fallback_model = "microsoft/DialoGPT-small"
            self.tokenizer = AutoTokenizer.from_pretrained(fallback_model)
            self.llm = AutoModelForCausalLM.from_pretrained(
                fallback_model,
                torch_dtype=torch.float32,
                low_cpu_mem_usage=True
            )
            
            if self.tokenizer.pad_token is None:
                self.tokenizer.pad_token = self.tokenizer.eos_token
            
            self.generation_pipeline = pipeline(
                "text-generation",
                model=self.llm,
                tokenizer=self.tokenizer,
                return_full_text=False
            )
            
            logger.info("✅ Fallback model loaded successfully")
            
        except Exception as e:
            logger.error(f"❌ Fallback model failed: {e}")
            self.llm = None
            self.generation_pipeline = None



    





    
    
    async def generate_response_async(self, query: str, progress_callback=None, language="English") -> Dict[str, Any]:
        """Async response generation with progress tracking"""
        start_time = time.time()
        

        # Step 0: Translate Arabic → English if needed
        original_query = query
        original_language = language
        is_arabic_request = language.lower() == "arabic" or self._is_arabic(query)

        if is_arabic_request and self.arabic_translator:
            logger.info("🈸 Arabic input detected - translating to English for processing")
            query = self.arabic_translator.translate_to_english(query)
        
        if progress_callback:
            progress_callback(0.1, "🔍 Checking cache...")
        
        # Check cache first (using English query for cache key)
        # Check cache first (using English query for cache key)
        query_hash = hashlib.md5(query.encode()).hexdigest()
        if query_hash in self.response_cache:
            cached_response = self.response_cache[query_hash]
            # Translate cached response if needed
            if is_arabic_request and self.arabic_translator:
                cached_response["response"] = self.arabic_translator.translate_to_arabic(cached_response["response"])
            cached_response["cached"] = True
            cached_response["response_time"] = 0.1
            if progress_callback:
                progress_callback(1.0, "💾 Retrieved from cache!")
            return cached_response

        
        try:
            if progress_callback:
                progress_callback(0.2, "🤖 Initializing LLM...")
            
            # Initialize LLM only when needed
            if self.llm is None:
                await asyncio.get_event_loop().run_in_executor(
                    self.executor, self._initialize_llm
                )
            
            if progress_callback:
                progress_callback(0.4, "🔍 Searching knowledge base...")
                
            # Enhanced knowledge retrieval using pre-made index
            search_results = await asyncio.get_event_loop().run_in_executor(
                self.executor, self.knowledge_base.search, query, 5
            )
            
            if progress_callback:
                progress_callback(0.6, "📝 Preparing context...")
            
            context = self._prepare_context(search_results)
            
            if progress_callback:
                progress_callback(0.8, "🧠 Generating response...")
            
            # Generate response
            english_response = await asyncio.get_event_loop().run_in_executor(
                self.executor, self._generate_response, query, context
            )
            
            if progress_callback:
                progress_callback(0.9, "🛡️ Validating safety...")
            
            # Enhanced safety check
            safety_check = self.fact_checker.check_medical_accuracy(english_response, context)
            
            # Step 2: Translate response → Arabic if needed

            # Prepare final response structure
            final_response = self._prepare_final_response(
                english_response, 
                search_results, 
                safety_check,
                time.time() - start_time
            )


            # Step 3: Translate final response to Arabic if requested
            if is_arabic_request and self.arabic_translator:
                logger.info("🌐 Translating final response to Arabic")
                final_response["response"] = self.arabic_translator.translate_to_arabic(final_response["response"])
                final_response["translated"] = True
                final_response["original_language"] = "Arabic"
            else:
                final_response["translated"] = False
                final_response["original_language"] = "English"
            
            # Cache the English version (for consistency)
            if len(self.response_cache) < 100:
                english_cache_response = final_response.copy()
                english_cache_response["response"] = english_response  # Store English version
                self.response_cache[query_hash] = english_cache_response
            
            if progress_callback:
                progress_callback(1.0, "✅ Complete!")
            
            return final_response
            
        except Exception as e:
            logger.error(f"❌ Error generating response: {e}")
            if progress_callback:
                progress_callback(1.0, f"❌ Error: {str(e)}")
            return self._create_error_response(str(e))


    # def _generate_with_flan(self, query: str, context: Optional[str] = None) -> str:
    #     """Generate a response using the FLAN model directly (no Groq)."""
    #     if not self.generation_pipeline:
    #         raise RuntimeError("FLAN generation pipeline not initialized")
            
    #     # Build simple instructional prompt
    #     prompt = f"""
    #     You are a medical assistant working in Gaza.
    #     Query:
    #     {query}
    #     Context:
    #     {context if context else "No additional context"}
    #     Respond with:
    #     - 🩹 Immediate Actions
    #     - ⚠️ Contraindications
    #     - 💡 Alternatives
    #     - 🚨 When to seek emergency help
    #     """.strip()
        
    #     result = self.generation_pipeline(prompt)
    #     return result[0]["generated_text"].strip() if result else "⚠️ No response generated."




    def _create_prompt_from_rag(self, query: str, rag_results: List[Dict[str, Any]]) -> str:
        """Use FLAN-T5 to condense RAG results into a clean prompt"""
        if not self.llm or not rag_results:
            return query  # Fallback to original query
            # Create context string from RAG results
            context = "\n".join([f"[Source {i+1}]: {res['text']}" 
                                 for i, res in enumerate(rag_results[:6])])
                                # Create prompt for FLAN
        prompt = f"""You are a medical researcher, Expand this medical context into a concise prompt for detailed response:
    
Original Query: {query}
    
Context:
{context}

Create a comprehensive response that includes:
1. Step-by-step treatment instructions
2. Gaza-specific adaptations
3. Alternative methods for resource-limited situations
4. Clear danger signs requiring professional care
5. Proper wound care timeline
    
Create a detailed question incorporating key points from the context, Structure your response with:
- Immediate Actions - Contraindications - Follow-up Care - Emergency Indicators:"""
        # Generate with FLAN-T5
        inputs = self.tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
        outputs = self.llm.generate(
            input_ids=inputs.input_ids,
            max_new_tokens=200,
            num_beams=3,
            early_stopping=True
        )
        
        refined_prompt = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        return f"{refined_prompt}\n\nContext References:\n{context}"



    def _is_arabic(self, text):
        return any('\u0600' <= c <= '\u06FF' for c in text)



    def _prepare_context(self, search_results: List[Dict[str, Any]]) -> str:
        MAX_CHARS = 1500
        if not search_results:
            return "First aid protocol: "
        context_parts = []
        for result in search_results[:5]:  # Top 3 results only
            text = str(result.get('text', '')).strip()
            context_parts.append({
                'text': clean_ocr_artifacts(text),
                'source': result.get('source', 'unknown'),
                'score': result.get('score', 0.0)
            })
            return "\n\n".join(
                f"[Reference {i+1}]: {ctx['text']}" 
                for i, ctx in enumerate(context_parts)
            )[:MAX_CHARS]
            
       
        
        
            
    def _format_context_with_groq(self, context_parts: List[Dict[str, Any]]) -> str:
        """Format context using Groq with comprehensive error handling"""

        def _get_groq_client(self):
            from groq import Groq
            return Groq(api_key=os.getenv("GROQ_API_KEY"))
            
        logger.info(f"GROQ_API_KEY prefix-format context with groq: {os.getenv('GROQ_API_KEY')[:5]}****")
        if not hasattr(self, 'groq_client') or not self.groq_client:
            raise ValueError("Groq client not initialized")
        if not context_parts:
            return "No context available"
        try:
            # Prepare context string
            context_str = "\n\n".join(
                f"Source {i+1} ({ctx['source']}, relevance {ctx['score']:.2f}):\n{ctx['text']}"
                for i, ctx in enumerate(context_parts)
            )
            
            response = self.groq_client.chat.completions.create(
                model="deepseek-r1-distill-llama-70b",
                messages=[
                    {
                        "role": "system", 
                        "content": self.system_prompt
                    },
                    
                    {
                        "role": "user",
                        "content": f"Organize this medical context:\n\n{context_str}"
                    }
                ],
                
                temperature=0.3,
                max_tokens=2000,
                top_p=0.9
            )
            
            # Validate response structure
            
            if not response or not response.choices:
                raise ValueError("Empty Groq response")
                
            if not hasattr(response.choices[0], 'message') or not hasattr(response.choices[0].message, 'content'):
                raise ValueError("Invalid response format")
            
            formatted = response.choices[0].message.content
            
            if formatted is None:
                raise ValueError("Empty response content")
            # Post-processing safety checks
            
            if not isinstance(formatted, str):
                raise ValueError("Formatted context is not a string")
                
            if "syringe" in formatted.lower():
                formatted = "⚠️ SAFETY ALERT: Rejected dangerous suggestion\n\n" + formatted
                
            return formatted
        
        except Exception as e:
            logger.error(f"Groq formatting failed: {str(e)}")
            raise ValueError(f"Groq processing failed: {str(e)}")
        

            


    def _generate_with_groq(self, query: str, context: str = None, refined_prompt: str = None) -> str:
        """
        Generate medical response using Groq with two modes:
        1. Direct mode (query + context)
        2. Refined prompt mode (FLAN-processed prompt)
        
        Args:
        query: Original user query
        context: Optional RAG context
        refined_prompt: Optional FLAN-processed prompt
        """
        
        def _get_groq_client():
            from groq import Groq
            return Groq(api_key=os.getenv("GROQ_API_KEY"))
            
        # Verify Groq client
        if not self.groq_client:
            self.groq_client = _get_groq_client()
        if not self.groq_client:
            raise ValueError("Groq client not available")
            
        try:
            # Determine which mode to use
            if refined_prompt:
                # Refined prompt mode (RAG→FLAN→Groq pipeline)
                messages = [
                    {
                        "role": "system",
                        "content": (
                            f"""{self.system_prompt}\n
                            Your task is to expand the medical information into comprehensive, 
                             descriptive guidance while preserving all safety considerations.
                            You are a WHO medical advisor for Gaza. Provide 1) Extremely detailed step-by-step guidance
                            2) Multiple treatment options for different resource scenarios
                            3)Clear timeframes for each action
                            4)Islamic medical considerations
                            5)Format with emoji section headers
                            
                            """
                        )
                    },
                    {
                        "role": "user",
                        "content": f"""Expand this into comprehensive medical guidance:
                        {refined_prompt}
                        Include:
                        1. Detailed procedural steps
                        2. Pain management techniques
                        3. Infection prevention measures
                        4. When to seek emergency care"""
                        
                    }
                ]
                max_tokens = 2000  # Allow longer responses for descriptive answers
                temperature = 0.5  # Slightly higher for creativity
                
            else:
                # Direct mode (query + context)
                messages = [
                    {
                        "role": "system",
                        "content": (
                            f"{self.system_prompt}\n"
                            "Provide detailed 5-7 step guidance when applicable."
                        )
                    },
                    {
                        "role": "user",
                        "content": (
                            f"Query: {query}\n"
                            f"Context: {context if context else 'No additional context'}\n\n"
                            "Provide comprehensive guidance with:\n"
                            "1. Detailed steps\n2. Alternative methods\n3. Gaza-specific adaptations"
                        )
                    }
                ]
                max_tokens = 1500  # Slightly shorter for direct responses
                temperature = 0.4  # Balanced between creativity and accuracy
                
            # Make the API call
            response = self.groq_client.chat.completions.create(
                model="llama3-70b-8192",  # Using latest model
                messages=messages,
                temperature=temperature,
                max_tokens=max_tokens,
                top_p=0.9
            )
            
            result = response.choices[0].message.content
            if not result:
                raise ValueError("Empty response from Groq")
                
            # Post-processing steps
            if refined_prompt and "Context References:" in refined_prompt:
                # Preserve RAG references in refined prompt mode
                refs = refined_prompt.split("Context References:")[1]
                result = f"{result}\n\n📚 Source References:{refs}"
            elif context:
                # Add basic context reference in direct mode
                result += "\n\nℹ️ Context: Based on verified medical sources"
                
            # Safety checks (applied to both modes)
            if "syringe" in result.lower():
                result = "⚠️ SAFETY ALERT: Rejected dangerous suggestion\n\n" + result
            if "Gaza Red Crescent" not in result:
                result += "\n\n📞 Verify with Gaza Red Crescent (101) if condition worsens"
            return result
        
        except Exception as e:
            logger.error(f"Groq generation failed: {str(e)}")
            raise ValueError(f"Groq processing error: {str(e)}")
    
    def _generate_response(self, query: str, context: str) -> str:
        """Enhanced RAG → FLAN → Groq pipeline with fallbacks"""
        # FIXED: Removed early return that was bypassing FLAN→Groq pipeline

        def _get_groq_client():
            from groq import Groq
            return Groq(api_key=os.getenv("GROQ_API_KEY"))
            
        # 1. Get broader RAG context (3 results instead of 1)
        rag_results = self.knowledge_base.search(query, k=6)
        context = self._prepare_context(rag_results)  # ✅ FIXED: ensure context exists
        top_score = rag_results[0]['score'] if rag_results else 0
        
        logger.info(f"Found {len(rag_results)} RAG results (top score: {rag_results[0]['score'] if rag_results else 0:.2f})")


        
        # 2. Try RAG→FLAN→Groq pipeline
        try:

            # Create refined prompt using FLAN
            refined_prompt = self._create_prompt_from_rag(query, rag_results)
            logger.info(f"Refined prompt: {refined_prompt[:100]}...")
            
        # Generate with Groq if available
            if hasattr(self, 'groq_client') and self.groq_client:
                try:
                    groq_response = self._generate_with_groq(query=query, refined_prompt=refined_prompt)
                    if groq_response:
                        return groq_response
                except Exception as groq_error:
                    logger.warning(f"Groq generation failed: {str(groq_error)}")
        except Exception as pipe_error:
            logger.warning(f"RAG→FLAN→Groq pipeline failed: {str(pipe_error)}")

            # 3. Fallback to direct FLAN generation
            if self.llm and self.tokenizer:
                try:
                    # Use the original context (not refined prompt) for fallback
                    flan_response = self._generate_with_flan(query, context)
                    if flan_response:
                        return flan_response
                except Exception as flan_error:
                    logger.error(f"FLAN generation failed: {str(flan_error)}")
                    
            # 4. Ultimate fallback to cached knowledge
            if rag_results:
                return self._format_kb_response(rag_results[0]['text'])
                
            # 5. Final emergency fallback
            return self._generate_fallback_response(query, context)


    
    
    def _format_final_response(self, response: str) -> str:
        """Ensure response meets all Gaza-specific requirements"""
        clean_response = response.split("CONTEXT:")[0].strip()
        for icon in ["🩹", "💡", "⚠️"]:
            if icon not in clean_response:
                clean_response = clean_response.replace("Immediate Actions", f"Immediate Actions {icon}", 1)
                break
        if "📍 Gaza Context:" not in clean_response:
             clean_response += "\n\n📍 Gaza Context: This guidance considers resource limitations. Adapt based on available supplies and seek professional medical care when accessible."
        return clean_response
    
    def _get_error_response(self, query: str, error: Exception) -> str:
        """User-friendly error message with Gaza contacts"""
        return f"""⚠️ We're unable to process your query about "{query}"
        For immediate medical assistance:
        📞 Palestinian Red Crescent: 101
        📞 Civil Defense: 102
        (Technical issue: {str(error)}...)"""

    


    
    def _generate_fallback_response(self, query: str, context: str) -> str:
        """Enhanced fallback response with Gaza-specific guidance"""
        gaza_guidance = {
            "burn": "For burns: Use clean, cool water if available. If water is scarce, use clean cloth. Avoid ice. Seek medical help urgently.",
            "bleeding": "For bleeding: Apply direct pressure with clean cloth. Elevate if possible. If severe, seek immediate medical attention.",
            "wound": "For wounds: Clean hands if possible. Apply pressure to stop bleeding. Cover with clean material. Watch for infection signs.",
            "infection": "Signs of infection: Redness, warmth, swelling, pus, fever. Seek medical care immediately if available.",
            "pain": "For pain management: Rest, elevation, cold/warm compress as appropriate. Avoid aspirin in children."
        }
        
        query_lower = query.lower()
        for condition, guidance in gaza_guidance.items():
            if condition in query_lower:
                return f"{guidance}\n\nContext from medical sources:\n{context}..."
        
        return f"Medical guidance for: {query}\n\nGeneral advice: Prioritize safety, seek professional help when available, consider resource limitations in Gaza.\n\nRelevant information:\n{context[:600]}..."






    
    def _prepare_final_response(
        self, 
        response: str, 
        search_results: List[Dict[str, Any]], 
        safety_check: Dict[str, Any],
        response_time: float
    ) -> Dict[str, Any]:

        def _get_groq_client():
            from groq import Groq
            return Groq(api_key=os.getenv("GROQ_API_KEY"))
        
        # Ensure response is a string
        if response is None:
            response = "Unable to generate response. Please try again."
        elif not isinstance(response, str):
            response = str(response)


        """Enhanced final response preparation with more metadata"""
        # Ensure response is a string
        if not isinstance(response, str):
            response = "Unable to generate response. Please try again."
        # Ensure safety_check has required fields
        if not isinstance(safety_check, dict):
            safety_check = {
                "confidence_score": 0.5,
                "issues": [],
                "warnings": ["Response validation failed"],
                "is_safe": False
            }
            
        # Add safety warnings if needed
        if not safety_check["is_safe"]:
            response = f"⚠️ MEDICAL CAUTION: {response}\n\n🚨 Please verify this guidance with a medical professional when possible."


        if safety_check["is_safe"] and hasattr(self, 'groq_client') and self.groq_client:
            try:
                enhanced = self._enhance_response_with_groq(response, search_results)
                if enhanced:
                    response = enhanced
            except Exception as e:
                logger.warning(f"Response enhancement failed: {e}")

        # Add Gaza-specific disclaimer
        
        # Extract unique sources
        sources = list(set(res.get("source", "unknown") for res in search_results)) if search_results else []
        
        # Calculate confidence based on multiple factors
        base_confidence = safety_check.get("confidence_score", 0.5)
        context_bonus = 0.1 if search_results else 0.0
        safety_penalty = 0.2 if not safety_check.get("is_safe", True) else 0.0
        
        final_confidence = max(0.0, min(1.0, base_confidence + context_bonus - safety_penalty))
        
        return {
            "response": response,
            "confidence": final_confidence,
            "sources": sources,
            "search_results_count": len(search_results),
            "safety_issues": safety_check.get("issues", []),
            "safety_warnings": safety_check.get("warnings", []),
            "response_time": round(response_time, 2),
            "timestamp": datetime.now().isoformat()[:19],
            "cached": False
        }


        
        if not hasattr(self, 'groq_client') or not self.groq_client:
            return response
        try:
            groq_client = self._get_groq_client()
            models = groq_client.models.list()
            logger.info("🧪 Available Groq models:")
            for m in models.data:
                logger.info(f" - {m.id}")
            
            messages = [
                {"role": "system", "content": self.system_prompt},
                {"role": "user", "content": f"Enhance this medical response:\n\n{response}"}
            ]
            
            enhanced = self.groq_client.chat.completions.create(
                model="deepseek-r1-distill-llama-70b",  
                messages=messages,
                temperature=0.3,
                max_tokens=1000
            )
            
            if enhanced and enhanced.choices:
                return enhanced.choices[0].message.content
                return response
        except Exception as e:
            logger.warning(f"Response enhancement failed: {e}")
            return response

    def _enhance_response_with_groq(self, response: str, search_results: List[Dict[str, Any]]) -> str:
        """Enhance response using Groq's capabilities"""

        def _get_groq_client():
            from groq import Groq
            return Groq(api_key=os.getenv("GROQ_API_KEY"))

        if not hasattr(self, 'groq_client') or not self.groq_client:
            return response
        try:
            messages = [
                {"role": "system", "content": self.system_prompt},
                {"role": "user", "content": f"Enhance this medical response:\n\n{response}"}
            ]
            
            enhanced = self.groq_client.chat.completions.create(
                model="llama3-70b-8192",  # Updated model name
                messages=messages,
                temperature=0.3,
                max_tokens=2000
            )
            
            if enhanced and enhanced.choices:
                return enhanced.choices[0].message.content
                return response
        except Exception as e:
            logger.warning(f"Response enhancement failed: {e}")
            return response

      
    
    def _create_error_response(self, error_msg: str) -> Dict[str, Any]:
        """Enhanced error response with helpful information"""
        return {
            "response": f"⚠️ System Error: Unable to process your medical query at this time.\n\nError: {error_msg}\n\n🚨 For immediate medical emergencies, seek professional help directly.\n\n📞 Gaza Emergency Numbers:\n- Palestinian Red Crescent: 101\n- Civil Defense: 102",
            "confidence": 0.0,
            "sources": [],
            "search_results_count": 0,
            "safety_issues": ["System error occurred"],
            "safety_warnings": ["Unable to validate medical accuracy "],
            "response_time": 0.0,
            "timestamp": datetime.now().isoformat()[:19],
            "cached": False,
            "error": True
        }