"""Model chaining logic with Groq fallback."""

import re
from typing import Optional, Dict, Any, List
from enum import Enum

from llama_index.llms.ollama import Ollama
from llama_index.llms.groq import Groq
from llama_index.core.llms import LLM
from litellm import completion
import httpx

from src.config import config
from src.harry_personality import get_harry_prompt


class ModelType(Enum):
    """Model types for routing."""
    LOCAL_SMALL = "local_small"
    LOCAL_LARGE = "local_large"
    GROQ_API = "groq"


class QueryComplexity(Enum):
    """Query complexity levels."""
    SIMPLE = "simple"  # Factual questions, definitions
    MODERATE = "moderate"  # Analysis, reasoning
    COMPLEX = "complex"  # Creative, multi-step reasoning


class ModelChain:
    """Intelligent model routing with fallback to Groq."""

    def __init__(self):
        self.models = {}
        self.groq_available = config.has_groq_api()

        # Initialize models lazily
        self._ollama_available = None

    def check_ollama_available(self) -> bool:
        """Check if Ollama is running and available."""
        if self._ollama_available is not None:
            return self._ollama_available

        try:
            # Try to connect to Ollama
            response = httpx.get(f"{config.ollama_host}/api/tags", timeout=2.0)
            self._ollama_available = response.status_code == 200
            if self._ollama_available:
                print("Ollama is available")
            else:
                print("Ollama is not responding correctly")
        except Exception as e:
            print(f"Ollama not available: {e}")
            self._ollama_available = False

        return self._ollama_available

    def get_model(self, model_type: ModelType) -> Optional[LLM]:
        """Get or initialize a model."""
        if model_type in self.models:
            return self.models[model_type]

        if model_type == ModelType.GROQ_API:
            if not self.groq_available:
                print("Groq API key not configured")
                return None

            try:
                # For groq/compound model, we'll use litellm
                # Return a wrapper that uses litellm
                return "groq"  # Special marker for litellm usage
            except Exception as e:
                print(f"Failed to initialize Groq: {e}")
                return None

        elif model_type in [ModelType.LOCAL_SMALL, ModelType.LOCAL_LARGE]:
            if not self.check_ollama_available():
                print("Ollama not available, falling back to Groq")
                return None

            model_config = config.get_model_config(model_type.value)
            try:
                model = Ollama(
                    model=model_config["model"],
                    base_url=config.ollama_host,
                    temperature=model_config["temperature"],
                    request_timeout=120.0,
                )
                self.models[model_type] = model
                print(f"Initialized {model_type.value} model: {model_config['model']}")
                return model
            except Exception as e:
                print(f"Failed to initialize Ollama model: {e}")
                return None

        return None

    def analyze_query_complexity(self, query: str, context_size: int = 0) -> QueryComplexity:
        """Analyze query complexity to determine which model to use."""
        query_lower = query.lower()

        # Simple queries - factual questions
        simple_patterns = [
            r"what is",
            r"who is",
            r"when did",
            r"where is",
            r"define",
            r"list",
            r"name",
            r"how many",
            r"yes or no",
        ]

        # Complex queries - requiring reasoning or creativity
        complex_patterns = [
            r"explain why",
            r"analyze",
            r"compare and contrast",
            r"what would happen if",
            r"imagine",
            r"create",
            r"write a",
            r"develop",
            r"design",
            r"evaluate",
            r"critique",
            r"synthesize",
        ]

        # Check for simple patterns
        for pattern in simple_patterns:
            if re.search(pattern, query_lower):
                return QueryComplexity.SIMPLE

        # Check for complex patterns
        for pattern in complex_patterns:
            if re.search(pattern, query_lower):
                return QueryComplexity.COMPLEX

        # Check query length and context size
        if len(query.split()) > 50 or context_size > config.max_local_context_size:
            return QueryComplexity.COMPLEX

        # Default to moderate
        return QueryComplexity.MODERATE

    def route_query(
        self,
        query: str,
        context: Optional[str] = None,
        force_model: Optional[ModelType] = None
    ) -> ModelType:
        """Determine which model to use for the query."""
        if force_model:
            return force_model

        context_size = len(context) if context else 0
        complexity = self.analyze_query_complexity(query, context_size)

        # Check Ollama availability
        ollama_available = self.check_ollama_available()

        # Routing logic
        if complexity == QueryComplexity.SIMPLE:
            if ollama_available:
                return ModelType.LOCAL_SMALL
            elif self.groq_available:
                return ModelType.GROQ_API
        elif complexity == QueryComplexity.MODERATE:
            if ollama_available:
                return ModelType.LOCAL_LARGE
            elif self.groq_available:
                return ModelType.GROQ_API
        else:  # COMPLEX
            if self.groq_available:
                return ModelType.GROQ_API
            elif ollama_available:
                return ModelType.LOCAL_LARGE

        # Final fallback
        if self.groq_available:
            return ModelType.GROQ_API
        elif ollama_available:
            return ModelType.LOCAL_SMALL
        else:
            raise RuntimeError("No models available! Please check Ollama or configure Groq API key.")

    def generate_response(
        self,
        query: str,
        context: Optional[str] = None,
        force_model: Optional[ModelType] = None,
        stream: bool = False
    ) -> Dict[str, Any]:
        """Generate response using appropriate model."""
        # Determine which model to use
        model_type = self.route_query(query, context, force_model)
        print(f"Using model: {model_type.value}")

        # Prepare prompt with Harry's personality
        if context:
            prompt = get_harry_prompt(query, context)
        else:
            # Even without context, use Harry's voice
            prompt = get_harry_prompt(query, "No specific context available - respond based on your general knowledge of HPMOR.")

        # Try primary model
        try:
            model = self.get_model(model_type)

            if model == "groq":  # Special handling for Groq via litellm
                # Use litellm for Groq
                response = completion(
                    model=f"groq/{config.groq_model}",
                    messages=[{"role": "user", "content": prompt}],
                    api_key=config.groq_api_key,
                    temperature=0.7,
                    max_tokens=2048,
                    stream=stream
                )

                if stream:
                    return {
                        "response": response,
                        "model_used": model_type.value,
                        "streaming": True
                    }
                else:
                    return {
                        "response": response.choices[0].message.content,
                        "model_used": model_type.value,
                        "tokens_used": response.usage.total_tokens if hasattr(response, 'usage') else None
                    }

            elif model:
                # Use LlamaIndex model
                if stream:
                    response = model.stream_complete(prompt)
                else:
                    response = model.complete(prompt)

                return {
                    "response": response,
                    "model_used": model_type.value,
                    "streaming": stream
                }

        except Exception as e:
            print(f"Error with {model_type.value}: {e}")

            # Try fallback
            if model_type != ModelType.GROQ_API and self.groq_available:
                print("Falling back to Groq API...")
                model_type = ModelType.GROQ_API
                try:
                    response = completion(
                        model=f"groq/{config.groq_model}",
                        messages=[{"role": "user", "content": prompt}],
                        api_key=config.groq_api_key,
                        temperature=0.7,
                        max_tokens=2048,
                        stream=stream
                    )

                    if stream:
                        return {
                            "response": response,
                            "model_used": model_type.value,
                            "streaming": True,
                            "fallback": True
                        }
                    else:
                        return {
                            "response": response.choices[0].message.content,
                            "model_used": model_type.value,
                            "tokens_used": response.usage.total_tokens if hasattr(response, 'usage') else None,
                            "fallback": True
                        }
                except Exception as e2:
                    print(f"Fallback to Groq also failed: {e2}")
                    raise RuntimeError(f"All models failed. Last error: {e2}")

        raise RuntimeError("No models available for response generation")


def main():
    """Test model chaining."""
    chain = ModelChain()

    # Test queries of different complexities
    test_queries = [
        ("What is Harry's full name?", QueryComplexity.SIMPLE),
        ("Explain Harry's reasoning about magic", QueryComplexity.MODERATE),
        ("Analyze the philosophical implications of Harry's scientific approach to magic", QueryComplexity.COMPLEX),
    ]

    for query, expected_complexity in test_queries:
        print(f"\nQuery: {query}")
        complexity = chain.analyze_query_complexity(query)
        print(f"Detected complexity: {complexity}")
        print(f"Expected complexity: {expected_complexity}")

        try:
            model_type = chain.route_query(query)
            print(f"Selected model: {model_type.value}")

            # Generate response
            result = chain.generate_response(query)
            print(f"Model used: {result['model_used']}")
            print(f"Response preview: {str(result['response'])[:200]}...")
        except Exception as e:
            print(f"Error: {e}")


if __name__ == "__main__":
    main()