import re
import json
import time
import torch
import logging
from threading import Lock
from config import app_config, load_config
from model_manager import safe_get_config_value  # Added import to fix error on line 458
from typing import Dict, List, Optional, Union, Any, Tuple
# Import ModelManager as a type hint only to avoid circular imports
from typing import TYPE_CHECKING
if TYPE_CHECKING:
    from model_manager import ModelManager

# Import service registry for dependencies
from service_registry import registry, MODEL, TOKENIZER, MODEL_MANAGER, COMMUNICATOR

# Then import other dependencies
from utils.sentence_transformer_utils import get_sentence_transformer
from utils.output_formatter import OutputFormatter
from sklearn.metrics.pairwise import cosine_similarity
# Import base interfaces
from base_interfaces.common_types import *
from base_interfaces.communicator_interface import AbstractCommunicator
# Import hybrid attention utils - update this import
from utils.smartHybridAttention import get_hybrid_attention_config

# Conditional imports for SNN/STDP functionality
try:
    from snntorch._neurons.lapicque import LIF
    from snntorch import spikegen
    from snntorch._neurons import Synaptic
    from communicator_STDP import Communicator_STDP
    SNNTORCH_AVAILABLE = True
except ImportError:
    SNNTORCH_AVAILABLE = False
    logger.warning("SNN/STDP functionality not available - some features will be disabled")

# Configure logging for the module
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

# Gracefully handle psutil import - only do this once
try:
    import psutil
    PSUTIL_AVAILABLE = True
except ImportError:
    logger.warning("psutil not available - cannot monitor system resources")
    PSUTIL_AVAILABLE = False
    # Create a minimal psutil-like interface for compatibility
    class DummyProcess:
        def __init__(self, pid=None):
            self.pid = pid or 1
        
        def memory_info(self):
            class MemInfo:
                def __init__(self):
                    self.rss = 1000000  # 1 MB
                    self.vms = 1000000  # 1 MB
            return MemInfo()
        def memory_percent(self):
            return 1.0  # 1%
    
    class DummyPsutil:
        @staticmethod
        def Process(pid=None):
            return DummyProcess(pid)
    psutil = DummyPsutil()

# The Communicator class implementation 
class Communicator(AbstractCommunicator):
    def __init__(self, models: Dict[str, torch.nn.Module] = None, model_manager=None):
        """Initialize the Communicator with a model manager and necessary components."""
        self.lock = Lock()
        self.config = load_config()
        self.similarity_threshold = app_config.SIMILARITY_THRESHOLD
        self.top_k = app_config.TOP_K
        self.conversation_history = []
        self.shared_layers = [
            'encoder.layer.0',  # Often early layers capture general language features
            'encoder.layer.1',
            'embeddings'        # Embeddings are often beneficial to share
        ]
        
        # Initialize model manager - fixed to avoid circular imports
        self._init_model_manager(model_manager)
        
        # Initialize components
        self.output_formatter = OutputFormatter()
        self.embedding_model = get_sentence_transformer("Wildnerve-tlm01-0.05Bx12")
        
        # Get models and compute specialization embeddings
        self._init_models_and_embeddings()
        
        # Initialize SNN/STDP components if enabled
        self._init_snn_components()
        
        # Initialize with attention configuration
        self.attention_config = get_hybrid_attention_config()
        
        # Update attention config from app_config
        if hasattr(app_config, 'TRANSFORMER_CONFIG') and hasattr(app_config.TRANSFORMER_CONFIG, 'ATTENTION_MECHANISM'):
            attn_mech = app_config.TRANSFORMER_CONFIG.ATTENTION_MECHANISM
            if isinstance(attn_mech, dict):
                for key, value in attn_mech.items():
                    if key in self.attention_config:
                        self.attention_config[key] = value
        
        # Initialize tokenizer - set this directly to avoid attribute errors later
        self.tokenizer = self._init_tokenizer()
        
        logger.info("Communicator initialized successfully")
    
    def _init_tokenizer(self):
        """Initialize the tokenizer with proper error handling"""
        try:
            if registry.has(TOKENIZER):
                return registry.get(TOKENIZER)
            from transformers import AutoTokenizer
            tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
            logger.info("Tokenizer initialized in communicator")
            registry.register(TOKENIZER, tokenizer)  # Register only if not present
            return tokenizer
        except Exception as e:
            logger.error(f"Tokenizer initialization failed: {e}")
            return None
        
    def _init_model_manager(self, model_manager):
        """Helper method to initialize model manager"""
        if model_manager is None:
            # Delayed import to avoid circular reference
            from model_manager import ModelManager
            
            try:
                max_active_models = getattr(app_config, 'MAX_ACTIVE_MODELS', 5)
                self.model_manager = ModelManager(max_active_models=max_active_models)
                logger.info(f"Created ModelManager with max_active_models={max_active_models}")
            except Exception as e:
                logger.error(f"Error creating ModelManager: {e}")
                self.model_manager = None
        else:
            self.model_manager = model_manager
            
    def _init_models_and_embeddings(self):
        """Initialize models and compute embeddings for specializations"""
        # Always force primary sentence transformer usage.
        self.embedding_model = get_sentence_transformer("Wildnerve-tlm01-0.05Bx12")
        self.models = self.model_manager.get_available_models() if self.model_manager else {}
        if not self.models:
            logger.warning("No models available in model manager")
            
        # Create embeddings for each specialization
        self.specialization_embeddings = {}
        
        if self.model_manager:
            # Access specializations through models dictionary keys
            specializations = []
            if hasattr(self.model_manager, 'models'):
                specializations = list(self.model_manager.models.keys())
            elif hasattr(self.model_manager, 'get_available_models'):
                specializations = list(self.model_manager.get_available_models().keys())
            
            for spec in specializations:
                self.specialization_embeddings[spec] = self.embedding_model.encode(spec, convert_to_numpy=True)
        
        # Compute weight sharing groups based on cosine similarity
        self.weight_sharing_groups = self.create_weight_sharing_groups(self.similarity_threshold)
        logger.info("Computed weight sharing groups: %s", self.weight_sharing_groups)
        
    def _init_snn_components(self):
        """Initialize SNN/STDP components if enabled"""
        # Check if SNN should be used
        use_snn = self._get_config_value('STDP_CONFIG', 'USE_SNN', False)
                
        if use_snn:
            # Determine device (CPU/GPU)
            self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
            
            # Get configuration values safely
            alpha = self._get_config_value('STDP_CONFIG', 'ALPHA', 0.1)
            beta = self._get_config_value('STDP_CONFIG', 'BETA', 0.2)
            spike_threshold = self._get_config_value('STDP_CONFIG', 'SpikeThreshold', 0.5)
                
            # Initialize components
            self.synapse_weights = Synaptic(alpha=alpha, beta=beta)
            self.spike_threshold = spike_threshold
            self.spike_generator = spikegen.rate
            self.beta = beta
            self.snn_layer = LIF(beta=self.beta)
            self.snn_comm = Communicator_STDP(self.models, device=self.device)
            self.mem = torch.zeros(1, 1)
            self.spk = torch.zeros(1, 1)
            logger.info("SNN/STDP components initialized successfully")
        else:
            self.device = None
            self.snn_comm = None
            logger.info("SNN/STDP components not enabled")
            
    def _get_config_value(self, config_name, attribute, default=None):
        """Safely retrieve configuration values handling both dict and object access"""
        if not hasattr(app_config, config_name):
            return default
            
        config_obj = getattr(app_config, config_name)
        
        if isinstance(config_obj, dict):
            return config_obj.get(attribute, default)
        else:
            return getattr(config_obj, attribute, default)

    def create_weight_sharing_groups(self, similarity_threshold: float) -> Dict[str, set]:
        """Computes cosine similarities among specialization embeddings and groups
        specializations that exceed the similarity threshold to enable weight sharing. Returns:
        Dictionary of groups: {specialization: [other_specializations_exceeding_threshold]}"""
        groups = {}
        for spec1, emb1 in self.specialization_embeddings.items():
            for spec2, emb2 in self.specialization_embeddings.items():
                if spec1 != spec2:
                    # Compute similarity
                    similarity = cosine_similarity(
                        emb1.reshape(1, -1), 
                        emb2.reshape(1, -1)
                    )[0][0]
                
                    if similarity > similarity_threshold:
                        if spec1 not in groups:
                            groups[spec1] = set()
                        groups[spec1].add(spec2)
        return groups

    def share_weights(self):
        """Share weights between models based on their computed similarity groups."""
        with self.lock:
            for primary_spec, related_specs in self.weight_sharing_groups.items():
                primary_model = self.model_manager.get_model(primary_spec)
                if not primary_model:
                    continue
                    
                # Share weights from primary model to all related models in the group
                for related_spec in related_specs:
                    related_model = self.model_manager.get_model(related_spec)
                    if not related_model:
                        continue
                        
                    # Share weights for similar layers
                    for p_layer, r_layer in zip(primary_model.parameters(), related_model.parameters()):
                        r_layer.data.copy_(p_layer.data)    
            logger.info("Completed weight sharing across model groups")

    def process_with_snn(self, input_tensor: torch.Tensor) -> torch.Tensor:
        """Process input through SNN components if enabled."""
        # Check if SNN is enabled and components are available
        if not hasattr(self, 'snn_comm') or self.snn_comm is None:
            return input_tensor

        # Reset states before processing new input
        self.reset_snn_state()
        
        # Ensure input is properly shaped
        if input_tensor.dim() == 1:
            input_tensor = input_tensor.unsqueeze(0)
        
        try:
            # Use communicator_STDP for processing if available
            if hasattr(self, 'snn_comm') and self.snn_comm is not None:
                # Pass to dedicated STDP communicator - enabling parallel processing
                return self.snn_comm.process_input(input_tensor)
            else:
                # Generate spikes from input
                spikes = self.spike_generator(input_tensor, num_steps=1)
                
                # Process through synaptic layer
                syn_out_result = self.synapse_weights(spikes)
                syn_out = syn_out_result[0] if isinstance(syn_out_result, tuple) else syn_out_result
                    
                # Handle LIF neuron processing
                batch_size = syn_out.shape[0]
                if self.mem.shape[0] != batch_size:
                    self.mem = torch.zeros(batch_size, syn_out.shape[1], device=syn_out.device)
                    
                # Process through SNN layer
                mem_next = self.beta * self.mem + syn_out
                spk_next = (mem_next > self.spike_threshold).float()
                self.mem = mem_next * (1 - spk_next)  # Reset membrane if spiked
                self.spk = spk_next
                    
                return self.spk
        
        except Exception as e:
            logger.error(f"Error in SNN processing: {e}", exc_info=True)
            return input_tensor
    def reset_snn_state(self):
        """Reset the SNN neuron states"""
        if hasattr(self, 'mem'):
            self.mem = torch.zeros_like(self.mem)
        if hasattr(self, 'spk'):
            self.spk = torch.zeros_like(self.spk)
    
    def route_input(self, input_text: str, query: Optional[str] = None) -> List[tuple]:
        """Route input to most relevant specializations, returning top-k matches. Returns:
           List of (specialization, similarity_score) tuples"""
        with self.lock:
            text_to_analyze = query if query else input_text
        
            if not self.specialization_embeddings:
                logger.warning("No specialization embeddings available for routing")
                return [("default", 1.0)]
        
            try:
                # Calculate text embedding
                text_embedding = self.embedding_model.encode(text_to_analyze, convert_to_numpy=True)
                # Apply SNN processing if enabled
                use_snn = self._get_config_value('STDP_CONFIG', 'USE_SNN', False)
                        
                if use_snn:
                    text_embedding = torch.from_numpy(text_embedding).float()
                    text_embedding = self.process_with_snn(text_embedding)
                    text_embedding = text_embedding.detach().numpy()
                
                # Calculate similarities
                text_embedding = text_embedding.reshape(1, -1)
                similarities = {}
                
                for spec, spec_embedding in self.specialization_embeddings.items():
                    spec_embedding = spec_embedding.reshape(1, -1)
                    similarity = cosine_similarity(text_embedding, spec_embedding)[0][0]
                    similarities[spec] = float(similarity)
            
                # Get top-k most similar specializations
                sorted_specs = sorted(similarities.items(), key=lambda x: x[1], reverse=True)
                top_k_specs = sorted_specs[:self.top_k]
            
                logger.debug("Routing similarities: %s", similarities)
                logger.info("Selected top %d specializations: %s", self.top_k, top_k_specs)
                
                # Check if prompt is long enough to use sliding window
                prompt_length = len(input_text.split())
                use_sliding_window = prompt_length > self.attention_config['WINDOW_SIZE'] // 2
                
                if use_sliding_window:
                    logger.info(f"Using sliding window attention for long input (length: {prompt_length})")
                return top_k_specs if top_k_specs else [("default", 1.0)]
            except Exception as e:
                logger.error(f"Error in route_input: {str(e)}")
                return [("default", 1.0)]

    def process_input(self, input_text: str, context: Optional[Dict] = None) -> Dict[str, Any]:
        """Process user input through the appropriate model(s) and generate response. Returns:
            Dictionary containing response and metadata"""
        start_time = time.time()
        logger.info(f"Processing input: {input_text[:50]}...")
        try:
            # Add input to conversation history
            self.conversation_history.append({"role": "user", "content": input_text})
            
            # Route input to determine specialization
            specializations = self.route_input(input_text)
            primary_spec, confidence = specializations[0] if specializations else ("default", 0.0)
            
            # Get the model for primary specialization
            model = None
            if hasattr(self.model_manager, 'get_model'):
                model = self.model_manager.get_model(primary_spec)
            elif primary_spec in self.models:
                model = self.models[primary_spec]
                
            if not model:
                logger.warning(f"No model found for {primary_spec}, using default")
                # Try to get any available model
                if hasattr(self.model_manager, 'get_available_models'):
                    models = self.model_manager.get_available_models()
                    if models:
                        model = next(iter(models.values()), None)
                elif self.models:
                    model = next(iter(self.models.values()), None)    
                if not model:
                    return {
                        "response": "No models available to process your request.",
                        "specialization": "none",
                        "processing_time": time.time() - start_time
                    }
            
            # Check if STDP/SNN should be used
            use_snn = self._get_config_value('STDP_CONFIG', 'USE_SNN', False)
            
            # Process input with standard pipeline
            model_inputs = self.prepare_model_input(input_text, model)
            
            # Generate response
            response = self.process_request(input_text, model)
            
            # If SNN is enabled, also process with STDP - potentially in parallel
            stdp_response = None
            if use_snn and hasattr(self, 'snn_comm') and self.snn_comm:
                try:
                    # Process simultaneously with STDP
                    stdp_response = self.snn_comm.process_request(input_text, model)
                    logger.info("STDP processing completed successfully")
                except Exception as e:
                    logger.error(f"STDP processing failed: {e}")
            
            # Format response - prefer standard response but use STDP if standard fails
            formatted_response = None
            if response:
                formatted_response = self.output_formatter.format_response(response, primary_spec)
            elif stdp_response:
                formatted_response = self.output_formatter.format_response(stdp_response, primary_spec)
                response = stdp_response
            else:
                formatted_response = "I'm having trouble generating a response."
            
            # Add to conversation history
            self.conversation_history.append({"role": "assistant", "content": formatted_response})
            
            # Share weights if needed and more than one specialization
            if len(specializations) > 1:
                self.share_weights()
            # Calculate processing time
            processing_time = time.time() - start_time
            
            result = {
                "response": formatted_response,
                "specialization": primary_spec,
                "similarity_score": confidence,
                "processing_time": processing_time,
                "alternative_specializations": [s[0] for s in specializations[1:]] if len(specializations) > 1 else []
            }
            # Add STDP information if available
            if stdp_response:
                result["stdp_processed"] = True
                result["parallel_response"] = stdp_response
            return result
            
        except Exception as e:
            logger.error(f"Error processing input: {str(e)}", exc_info=True)
            return {
                "response": f"An error occurred while processing your request: {str(e)}",
                "error": str(e),
                "processing_time": time.time() - start_time
            }
    
    def prepare_model_input(self, text: str, model) -> Dict:
        """Prepare input text for model processing. Returns: Dictionary of model inputs"""
        device = next(model.parameters()).device
        try:
            # Get tokenizer from model
            tokenizer = getattr(model, 'tokenizer', None)
            
            if tokenizer:
                # Tokenize the input
                inputs = tokenizer(
                    text,
                    return_tensors="pt",
                    padding=True,
                    truncation=True,
                    max_length=safe_get_config_value(app_config, "MAX_SEQ_LENGTH", 512)
                )
                # Move inputs to the same device as model
                input_ids = inputs["input_ids"].to(device)
                
                return {
                    "input_ids": input_ids,
                    "max_length": app_config.MAX_SEQ_LENGTH,
                    "device": device,
                    "temperature": getattr(self, 'generation_config', {}).get('temperature', 0.7)
                }
            else:
                # Fallback if tokenizer not available
                logger.warning("Model has no tokenizer attribute, using basic input")
                return {
                    "input_text": text,
                    "max_length": app_config.MAX_SEQ_LENGTH
                }
        except Exception as e:
            logger.error(f"Error preparing model input: {str(e)}")
            # Return minimal inputs
            return {"input_text": text}
    
    def clear_conversation_history(self):
        """Clear the conversation history"""
        self.conversation_history = []
        
    def get_conversation_history(self) -> List[Dict]:
        """Get the current conversation history"""
        return self.conversation_history.copy()

    def process_request(self, prompt: str, model: Any) -> str:
        """Process a user request through the selected model"""
        try:
            logger.info(f"Processing request with model")
            
            # Get the tokenizer - reuse existing tokenizer or initialize if needed
            if not self.tokenizer:
                self.tokenizer = self._init_tokenizer()
                
            # Tokenize input
            inputs = self.tokenizer(
                prompt,
                return_tensors="pt",
                truncation=True,
                max_length=128
            )
            # Generate response with the model
            with torch.no_grad():
                try:
                    # Try using generate method with compatible parameters
                    if hasattr(model, 'generate_with_decoding'):
                        # Use the most direct generation method if available
                        return model.generate_with_decoding(
                            inputs["input_ids"],
                            max_length=256,
                            temperature=0.7
                        )
                    elif hasattr(model, 'generate'):
                        # Check what parameters the generate method accepts
                        import inspect
                        sig_params = inspect.signature(model.generate).parameters
                        generate_kwargs = {'input_ids': inputs["input_ids"]}
                        
                        # Only add parameters the function accepts
                        if 'max_length' in sig_params:
                            generate_kwargs['max_length'] = 256
                            
                        if 'temperature' in sig_params:
                            generate_kwargs['temperature'] = 0.7
                        
                        # Call generate with compatible parameters
                        outputs = model.generate(**generate_kwargs)
                        
                        # Decode the output
                        response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
                        
                        # Clean up and return
                        return response.strip()
                except Exception as e:
                    logger.warning(f"Error in model.generate: {e}")
                    
                    # Check for shape errors which is the common issue we're encountering
                    if "shape" in str(e):
                        # Extract the specific shape mentioned in the error
                        shape_match = re.search(r'shape \'\[(.*?)\]\'', str(e))
                        if shape_match:
                            # Special handling for shape error - use alternative models
                            logger.info("Detected shape error, trying alternative model inference methods")
                            
                            # Try to get a response using a different model specialization
                            alternative_response = self._get_response_from_alternative_model(prompt)
                            if alternative_response:
                                return alternative_response
                            
                            # If that fails, try using a more dynamic topic detection approach
                            topic, subtopics = self._analyze_prompt_for_topics(prompt)
                            logger.info(f"Detected topic: {topic}, subtopics: {subtopics}")
                            
                            return self._get_topic_response(topic, prompt, subtopics)
                    
                    # If not a shape error, try direct model inference
                    try:
                        # Use only input_ids to minimize potential shape issues
                        outputs = model(inputs["input_ids"])
                        
                        # Check if we can extract anything meaningful from the outputs
                        if isinstance(outputs, dict) and "logits" in outputs:
                            logits = outputs["logits"]
                            # Extract top tokens for a coherent response
                            response = self._generate_response_from_logits(logits, prompt)
                            if response:
                                return response
                        elif isinstance(outputs, torch.Tensor) and outputs.dim() >= 2:
                            # For tensor outputs, extract useful information
                            response = self._generate_response_from_tensor(outputs, prompt)
                            if response:
                                return response        
                    except Exception as fw_error:
                        logger.error(f"Forward pass error: {fw_error}")
                    
                    # Last resort: check if other models can handle this prompt better
                    return self._get_fallback_response(prompt)
        except Exception as e:
            logger.error(f"Error in process_request: {e}")
            return "I encountered an error processing your request. Could you try asking your question differently?"

    def _get_response_from_alternative_model(self, prompt: str) -> Optional[str]:
        """Try to get a response using a different model from the model manager"""
        try:
            if not self.model_manager:
                return None
            # Get the top 3 alternative models
            specializations = self.route_input(prompt)
            # Skip the first one (which is the one that just failed)
            for spec, _ in specializations[1:]:
                alt_model = self.model_manager.get_model(spec)
                if alt_model:
                    logger.info(f"Trying alternative model for specialization: {spec}")
                    try:
                        # Prepare inputs for this model
                        if hasattr(alt_model, 'tokenizer'):
                            tokenizer = alt_model.tokenizer
                        else:
                            tokenizer = self.tokenizer    
                        inputs = tokenizer(
                            prompt,
                            return_tensors="pt",
                            truncation=True,
                            max_length=128
                        ) 
                        # Try generation with this model
                        if hasattr(alt_model, 'generate_with_decoding'):
                            response = alt_model.generate_with_decoding(
                                inputs["input_ids"],
                                max_length=256,
                                temperature=0.7
                            )
                            if response and isinstance(response, str) and len(response) > 10:
                                return response
                    except Exception as alt_error:
                        logger.warning(f"Alternative model {spec} also failed: {alt_error}")
                        continue
            return None
        except Exception as e:
            logger.error(f"Error getting response from alternative model: {e}")
            return None
    def _analyze_prompt_for_topics(self, score, prompt: str) -> Tuple[str, List[str]]:
        """Analyze prompt to dynamically determine the topic and subtopics"""
        # First try to use the embedding model if available
        primary_topic = "general"
        subtopics = []
        try:
            # Option 1: Use embedding similarity to predefined topics
            if hasattr(self, 'embedding_model'):
                # Define a broad range of topics
                candidate_topics = [
                    "programming", "math", "science", "history", "art", 
                    "literature", "music", "politics", "economics", "philosophy",
                    "technology", "health", "sports", "entertainment", "education",
                    "business", "psychology", "sociology", "linguistics", "physics",
                    "chemistry", "biology", "medicine", "engineering", "computer science",
                    "artificial intelligence", "data science", "web development", "finance",
                    "law", "ethics", "religion", "geography", "astronomy", "environment"
                ]
                # Get embedding for the prompt
                prompt_embedding = self.embedding_model.encode(prompt, convert_to_numpy=True)
                
                # Get embeddings for topics
                topic_embeddings = {
                    topic: self.embedding_model.encode(f"This text is about {topic}.", convert_to_numpy=True)
                    for topic in candidate_topics
                }
                # Calculate similarities
                similarities = {
                    topic: float(cosine_similarity(
                        prompt_embedding.reshape(1, -1), 
                        emb.reshape(1, -1)
                    )[0][0])
                    for topic, emb in topic_embeddings.items()
                }
                # Sort by similarity score
                sorted_topics = sorted(similarities.items(), key=lambda x: x[1], reverse=True)
                # Get primary topic and subtopics
                if sorted_topics:
                    primary_topic = sorted_topics[0][0]
                    # Get subtopics with similarity score at least 80% of the top score
                    threshold = sorted_topics[0][1] * 0.8
                    subtopics = [topic for topic, score in sorted_topics[1:6] if score > threshold]
            
            # Option 2: Use frequency analysis as fallback
            if primary_topic == "general" or not subtopics:
                # Define topic keywords
                topic_keywords = {
                    "programming": ["code", "programming", "python", "java", "javascript", "function", "algorithm", "developer", "software"],
                    "math": ["math", "mathematics", "algebra", "calculus", "equation", "geometry", "statistics", "theorem"],
                    "science": ["science", "physics", "chemistry", "biology", "scientific", "experiment", "theory"],
                    "history": ["history", "historical", "ancient", "century", "civilization", "war", "empire"],
                    "technology": ["technology", "tech", "computer", "digital", "internet", "device", "hardware", "software"],
                    "ai": ["ai", "artificial intelligence", "machine learning", "neural network", "deep learning", "nlp", "algorithm"],
                    "health": ["health", "medical", "medicine", "disease", "treatment", "doctor", "patient", "healthcare"],
                    "business": ["business", "company", "market", "industry", "finance", "economic", "management", "strategy"],
                    "general": [] # Fallback
                }
                # Clean and tokenize prompt
                words = re.findall(r'\b[a-zA-Z]{3,}\b', prompt.lower())
                
                # Count matches for each topic
                topic_scores = {topic: 0 for topic in topic_keywords.keys()}
                for word in words:
                    for topic, keywords in topic_keywords.items():
                        if word in keywords or any(keyword in word for keyword in keywords):
                            topic_scores[topic] += 1
                
                # Get top topics by score
                sorted_topics = sorted(topic_scores.items(), key=lambda x: x[1], reverse=True)
                if sorted_topics[0][1] > 0:
                    primary_topic = sorted_topics[0][0]
                    # Get subtopics with score > 0
                    subtopics = [topic for topic in sorted_topics[1:4] if score > 0]
                
            # If we still don't have subtopics, add some based on primary topic
            if not subtopics:
                # Define related subtopics for common topics
                related_topics = {
                    "programming": ["software development", "algorithms", "data structures"],
                    "math": ["algebra", "geometry", "statistics"],
                    "science": ["physics", "chemistry", "biology"],
                    "history": ["ancient history", "modern history", "world wars"],
                    "technology": ["computers", "internet", "gadgets"],
                    "ai": ["machine learning", "neural networks", "natural language processing"],
                    "health": ["medicine", "wellness", "nutrition"],
                    "business": ["economics", "finance", "management"]
                }
                subtopics = related_topics.get(primary_topic, ["information", "knowledge", "details"])
            
            return primary_topic, subtopics
        except Exception as e:
            logger.error(f"Error analyzing prompt for topics: {e}")
            return "general", ["information"]
    def _generate_response_from_logits(self, logits: torch.Tensor, prompt: str) -> Optional[str]:
        """Generate a coherent response from model output logits"""
        try:
            # Extract the top tokens from the logits
            if logits.dim() >= 2:
                # Get the last position's logits
                last_logits = logits[:, -1, :] if logits.dim() > 2 else logits
                
                # Get top tokens
                top_k = min(5, last_logits.size(-1))
                top_values, top_indices = torch.topk(last_logits, top_k, dim=-1)
                
                # Decode top tokens
                if hasattr(self, 'tokenizer') and self.tokenizer is not None:
                    top_tokens = [self.tokenizer.decode([idx.item()]) for idx in top_indices[0]]
                    
                    # Create a coherent response using the tokens and context from the prompt
                    topic_tokens = [token for token in top_tokens if len(token) > 1 and not token.startswith('[')]
                    if topic_tokens:
                        # Extract topic from prompt
                        topic = self._extract_topic_from_prompt(prompt)
                        context = ", ".join(topic_tokens[:3])
                        return f"Based on my understanding of {topic}, the key concepts include {context}. Would you like more specific information about any of these aspects?"
            return None
        except Exception as e:
            logger.error(f"Error generating response from logits: {e}")
            return None
    def _generate_response_from_tensor(self, tensor: torch.Tensor, prompt: str) -> Optional[str]:
        """Generate a response from a tensor output"""
        try:
            # For sequence outputs, try to find the most relevant position
            if tensor.dim() >= 2:
                # If it's a sequence, use the mean or the last position
                if tensor.dim() == 3:  # [batch, seq, hidden]
                    features = tensor[0, -1, :]  # Last position of first batch
                else:  # [batch, hidden]
                    features = tensor[0, :]  # First batch
                    
                # Use these features to generate a meaningful response
                # (Simplified approach - in reality we'd want to use these features more effectively)
                topic = self._extract_topic_from_prompt(prompt)
                
                # If the tensor is small enough, we can include some values
                if features.numel() < 10:
                    values = [f"{val:.2f}" for val in features[:5].tolist()]
                    value_str = ", ".join(values)
                    return f"I analyzed your question about {topic}. My analysis indicates values of {value_str}, which suggests this topic involves multiple factors."
                else:
                    # Generic response using the tensor shape
                    shape_str = "x".join(str(dim) for dim in tensor.size())
                    return f"I analyzed your question about {topic}. This is a complex topic with many dimensions (tensor shape: {shape_str}). Could you specify which aspect you'd like me to focus on?"
            return None
        except Exception as e:
            logger.error(f"Error generating response from tensor: {e}")
            return None
    def _extract_topic_from_prompt(self, prompt: str) -> str:
        """Extract a topic phrase from the prompt"""
        # Simple extraction of the main subject using first few words
        words = prompt.strip().split()
        
        if not words:
            return "this topic"
            
        # Check for common question patterns
        if words[0].lower() in ['what', 'how', 'why', 'when', 'where', 'who', 'which']:
            # For questions, look for the subject after the question word
            # E.g., "What is quantum physics?" -> "quantum physics"
            if len(words) > 1:
                if words[1].lower() in ['is', 'are', 'was', 'were', 'will', 'did', 'does', 'do']:
                    if len(words) > 2:
                        return ' '.join(words[2:min(5, len(words))])
                    return words[1]
                return ' '.join(words[1:min(4, len(words))])
        # For non-questions, use the first few words
        return ' '.join(words[:min(3, len(words))])

    def _extract_subject(self, text: str) -> str:
        """Extract the primary subject from a text prompt
           This method uses basic NLP techniques to identify the main
           subject or topic of a text, which can be used for routing to specialized models."""
        try:
            # For more advanced implementations, we'd use proper NLP here
            # For now, a simple keyword extraction approach:
            
            # Convert to lowercase for easier matching
            text = text.lower()
            
            # Define some subject categories and their keywords
            subject_keywords = {
                "programming": ["code", "program", "programming", "function", "algorithm", "software", "developer"],
                "mathematics": ["math", "equation", "calculation", "formula", "number", "geometry"],
                "science": ["science", "physics", "chemistry", "biology", "scientific"],
                "history": ["history", "historical", "past", "ancient", "century"]
            }
            # Find which subject has the most matching keywords
            subject_scores = {}
            for subject, keywords in subject_keywords.items():
                score = sum(1 for keyword in keywords if keyword in text)
                if score > 0:
                    subject_scores[subject] = score
            
            # Return the subject with the highest score, or empty string if none found
            if subject_scores:
                return max(subject_scores.items(), key=lambda x: x[1])[0]
            return ""
        except Exception as e:
            logger.error(f"Error extracting subject: {e}")
            return ""
            
    # Add conversation context methods to enhance chatbot capabilities
    def add_to_conversation_history(self, role: str, content: str, metadata: Optional[Dict] = None):
        """Add an entry to conversation history with optional metadata"""
        entry = {
            "role": role,
            "content": content,
            "timestamp": time.time()
        }
        if metadata:
            entry["metadata"] = metadata
        self.conversation_history.append(entry)
        # Maintain a reasonable history size
        max_history = getattr(app_config, "MAX_CONVERSATION_HISTORY", 10)
        if len(self.conversation_history) > max_history:
            self.conversation_history = self.conversation_history[-max_history:]

    def get_conversation_context(self, window_size: int = 3) -> str:
        """Get recent conversation context formatted as a single string"""
        if not self.conversation_history:
            return ""
            
        # Get the most recent exchanges
        recent_history = self.conversation_history[-window_size*2:]
        
        # Format as a string
        context_parts = []
        for entry in recent_history:
            role_prefix = "User: " if entry["role"] == "user" else "Assistant: "
            context_parts.append(f"{role_prefix}{entry['content']}")
        
        return "\n".join(context_parts)
            
    def process_with_context(self, input_text: str, context: Optional[Dict] = None) -> Dict[str, Any]:
        """Process input with conversation context for better continuity"""
        # Get recent conversation context
        conversation_context = self.get_conversation_context(window_size=3)
        
        # Combine context with current prompt if context exists
        contextualized_prompt = input_text
        if conversation_context:
            # Create a prompt that includes conversation history 
            # but doesn't exceed token limits
            # Get MAX_SEQ_LENGTH safely
            max_seq_length = getattr(app_config, 'MAX_SEQ_LENGTH', 512)
            if isinstance(max_seq_length, dict):
                max_seq_length = 512
                logger.warning(f"MAX_SEQ_LENGTH is a dictionary, using default: {max_seq_length}")
            elif not isinstance(max_seq_length, (int, float)):
                max_seq_length = 512
                logger.warning(f"MAX_SEQ_LENGTH is not a number, using default: {max_seq_length}")
            else:
                max_seq_length = int(max_seq_length)
                
            max_context_length = max_seq_length // 2  # Now safe to use integer division
            
            contextualized_prompt = f"Previous conversation:\n{conversation_context}\n\nCurrent question: {input_text}"
        
        # Process using enhanced prompt
        result = self.process_input(contextualized_prompt, context)
        
        # Store original query in result
        if isinstance(result, dict):
            result["original_query"] = input_text
        return result

    def _get_fallback_response(self, prompt: str) -> str:
        """Get a fallback response when primary model processing fails"""
        try:
            # Extract topic from prompt
            topic, subtopics = self._analyze_prompt_for_topics(prompt)
            # Try to use any available model for generating a response
            if hasattr(self, 'model_manager') and self.model_manager:
                # Try multiple strategies to get a working model 
                # Strategy 1: Try the built-in alternative model getter
                if hasattr(self.model_manager, 'get_alternative_model_for_prompt'):
                    alt_model = self.model_manager.get_alternative_model_for_prompt(prompt)
                    if alt_model:
                        logger.info(f"Using alternative model for fallback response")
                        try:
                            inputs = self.tokenizer(prompt, return_tensors="pt", truncation=True, max_length=128)
                            if hasattr(alt_model, 'generate_with_decoding'):
                                response = alt_model.generate_with_decoding(
                                    inputs["input_ids"],
                                    max_length=256,
                                    temperature=0.7
                                )
                                if response and isinstance(response, str) and len(response) > 10:
                                    return response
                        except Exception as alt_error:
                            logger.warning(f"Alternative model also failed: {alt_error}")
                
                # Strategy 2: Try any other available model from the manager
                try:
                    available_models = self.model_manager.get_available_models()
                    for spec_name, model in available_models.items():
                        if spec_name != topic:  # Skip the model that likely failed already
                            logger.info(f"Trying model from specialization: {spec_name}")
                            inputs = self.tokenizer(prompt, return_tensors="pt", truncation=True, max_length=128)
                            if hasattr(model, 'generate_with_decoding'):
                                response = model.generate_with_decoding(
                                    inputs["input_ids"],
                                    max_length=256,
                                    temperature=0.9  # Higher temperature for diversity
                                )
                                if response and isinstance(response, str) and len(response) > 10:
                                    return response
                except Exception as e:
                    logger.warning(f"Failed to use alternative models: {e}")
                    
            # If no model worked, build a dynamic response based on topic analysis
            return self._build_dynamic_response(topic, prompt, subtopics)
        except Exception as e:
            logger.error(f"Error getting fallback response: {e}")
            # Absolute last resort generic response
            return "I'm having trouble understanding that request. Could you rephrase it or try asking something else?"

    def _build_dynamic_response(self, topic: str, prompt: str, subtopics: List[str] = None) -> str:
        """Build a dynamic response based on topic analysis without hardcoded templates"""
        try:
            # Extract subject if possible
            subject = self._extract_subject(prompt)
            # Ensure we have subtopics list
            subtopics = subtopics or []
            # Build a response that acknowledges the topic but doesn't contain hardcoded knowledge
            topic_str = subject if subject else topic
            
            # Construct a dynamic response prompt for a model
            meta_prompt = f"""
Topic: {topic_str}
Related areas: {', '.join(subtopics[:3]) if subtopics else 'various fields'}
Request: {prompt}

Create a brief response that acknowledges the topic but asks for clarification.
Do not provide specific information about the topic, just acknowledge understanding and ask for more details."""
            # Try to use a lightweight model for this meta-generation if possible
            try:
                if hasattr(self, 'model_manager') and self.model_manager:
                    # Try to find any working model
                    models = self.model_manager.get_available_models()
                    if models:
                        model = next(iter(models.values()))
                        inputs = self.tokenizer(meta_prompt, return_tensors="pt", truncation=True, max_length=256)
                        meta_response = model.generate_with_decoding(
                            inputs["input_ids"],
                            max_length=256,
                            temperature=0.7
                        )
                        if meta_response and len(meta_response) > 20:
                            return meta_response
            except Exception as e:
                logger.warning(f"Meta-generation failed: {e}")
            
            # Fallback to a very simple dynamic response if all else fails
            subtopic_str = ", ".join(subtopics[:3]) if subtopics else "related areas"
            
            return f"""I understand you're asking about {topic_str}. This relates to {subtopic_str}.
To provide a helpful response, I'd need more specific details about what aspect you're interested in learning about.
Could you please clarify what specific information you're looking for?"""
        except Exception as e:
            logger.error(f"Error building dynamic response: {e}")
            return "I need more information to help you with that topic. Could you provide more details about what you'd like to know?"
    def _get_topic_response(self, topic: str, prompt: str, subtopics: List[str] = None) -> str:
        """Get a response for a specific topic using model-driven approach"""
        return self._build_dynamic_response(topic, prompt, subtopics)

    def process_input(self, prompt, **kwargs):
        # First try using a real model if available
        if self.model and not (hasattr(self.model, '_is_minimal') and self.model._is_minimal) and self.tokenizer:
            try:
                logger.info("Attempting model inference with actual model")
                inputs = self.tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
                
                # Add timeout protection
                max_inference_time = 30  # seconds
                start_time = time.time()
                
                if hasattr(self.model, "generate_with_decoding"):
                    response = self.model.generate_with_decoding(inputs.input_ids)
                elif hasattr(self.model, "generate"):
                    output_ids = self.model.generate(inputs.input_ids)
                    response = self.tokenizer.decode(output_ids[0], skip_special_tokens=True)
                else:
                    # Forward pass
                    outputs = self.model(inputs.input_ids)
                    response = self.tokenizer.decode(torch.argmax(outputs, dim=-1)[0], skip_special_tokens=True)
                
                elapsed_time = time.time() - start_time
                
                if elapsed_time > max_inference_time:
                    logger.warning(f"Model inference took too long: {elapsed_time:.2f} seconds")
                
                if response and len(response) > 10:  # Require reasonably long response
                    logger.info("Generated model response successfully")
                    return {"response": response, "minimal_mode": False}
            except Exception as e:
                logger.warning(f"Model inference failed: {e}")
        elif self.model and hasattr(self.model, '_is_minimal') and self.model._is_minimal:
            logger.warning("Using minimal model - full model unavailable")
        
        # Check if prompt contains keywords we can respond to meaningfully
        logger.debug(f"Minimal communicator processing: {prompt[:30]}...")
        response = self._get_knowledge_response(prompt)
        if response:
            return {"response": response, "minimal_mode": True}  # Flag as minimal mode
        
        return {"response": f"I'm operating in minimal mode. Your query was about {prompt.split()[0] if prompt.split() else 'this topic'}...", 
                "minimal_mode": True}  # Flag as minimal mode

# Add factory function for producing & registering the main Communicator
def create_communicator(model_manager=None):
    from communicator import Communicator
    comm = Communicator(model_manager=model_manager)
    registry.register(COMMUNICATOR, comm)
    return comm

from service_registry import registry, COMMUNICATOR
from adapter_layer import WildnerveModelAdapter

class Communicator:
    def __init__(self):
        self.adapter = WildnerveModelAdapter()

    def process_request(self, prompt: str, **kwargs):
        return self.adapter.generate(prompt, **kwargs)

# Register
comm = Communicator()
registry.register(COMMUNICATOR, comm, overwrite=True)