"""Multi-LLM Provider Support

Unified interface for OpenAI, Ollama, and HuggingFace Inference API.
Enables seamless switching between LLM backends with parameterized configuration.
"""

import os
from abc import ABC, abstractmethod
from typing import Optional, List, Dict, Any
from dataclasses import dataclass


@dataclass
class LLMResponse:
    """Standardized LLM response"""
    text: str
    model: str
    provider: str
    tokens_used: Optional[int] = None
    metadata: Optional[Dict[str, Any]] = None


class LLMProvider(ABC):
    """Abstract base class for LLM providers"""
    
    def __init__(self, model_name: str, **kwargs):
        self.model_name = model_name
        self.config = kwargs
    
    @abstractmethod
    def generate(self, prompt: str, **kwargs) -> LLMResponse:
        """Generate text from prompt"""
        pass
    
    @abstractmethod
    def score_response(self, prompt: str, response: str) -> float:
        """Score quality of a response (0-1)"""
        pass


class OpenAIProvider(LLMProvider):
    """OpenAI API provider"""
    
    def __init__(self, model_name: str = "gpt-4", api_key: Optional[str] = None, **kwargs):
        super().__init__(model_name, **kwargs)
        self.api_key = api_key or os.getenv("OPENAI_API_KEY")
        
        try:
            from openai import OpenAI
            self.client = OpenAI(api_key=self.api_key)
            self.available = True
        except ImportError:
            print("Warning: openai package not installed. Install with: pip install openai")
            self.available = False
        except Exception as e:
            print(f"Warning: Could not initialize OpenAI client: {e}")
            self.available = False
    
    def generate(self, prompt: str, temperature: float = 0.7, max_tokens: int = 1000, **kwargs) -> LLMResponse:
        if not self.available:
            return LLMResponse(
                text="OpenAI provider not available. Please install openai package and set API key.",
                model=self.model_name,
                provider="openai"
            )
        
        try:
            response = self.client.chat.completions.create(
                model=self.model_name,
                messages=[{"role": "user", "content": prompt}],
                temperature=temperature,
                max_tokens=max_tokens,
                **kwargs
            )
            
            return LLMResponse(
                text=response.choices[0].message.content,
                model=self.model_name,
                provider="openai",
                tokens_used=response.usage.total_tokens if hasattr(response, 'usage') else None,
                metadata={"finish_reason": response.choices[0].finish_reason}
            )
        except Exception as e:
            return LLMResponse(
                text=f"Error generating response: {str(e)}",
                model=self.model_name,
                provider="openai"
            )
    
    def score_response(self, prompt: str, response: str) -> float:
        """Score response quality using a simple prompt"""
        if not self.available:
            return 0.5
        
        try:
            score_prompt = f"""Rate the quality of this response on a scale of 0.0 to 1.0.

Prompt: {prompt}

Response: {response}

Provide only a number between 0.0 and 1.0:"""
            
            result = self.generate(score_prompt, temperature=0.1, max_tokens=10)
            
            # Extract number from response
            try:
                score = float(result.text.strip())
                return max(0.0, min(1.0, score))
            except ValueError:
                return 0.5
        except Exception:
            return 0.5


class OllamaProvider(LLMProvider):
    """Ollama local LLM provider"""
    
    def __init__(self, model_name: str = "llama2", base_url: str = "http://localhost:11434", **kwargs):
        super().__init__(model_name, **kwargs)
        self.base_url = base_url
        
        try:
            import requests
            self.requests = requests
            # Test connection
            response = requests.get(f"{base_url}/api/tags", timeout=2)
            self.available = response.status_code == 200
        except ImportError:
            print("Warning: requests package not installed. Install with: pip install requests")
            self.available = False
        except Exception as e:
            print(f"Warning: Could not connect to Ollama at {base_url}: {e}")
            self.available = False
    
    def generate(self, prompt: str, temperature: float = 0.7, max_tokens: int = 1000, **kwargs) -> LLMResponse:
        if not self.available:
            return LLMResponse(
                text="Ollama provider not available. Please ensure Ollama is running at " + self.base_url,
                model=self.model_name,
                provider="ollama"
            )
        
        try:
            response = self.requests.post(
                f"{self.base_url}/api/generate",
                json={
                    "model": self.model_name,
                    "prompt": prompt,
                    "temperature": temperature,
                    "num_predict": max_tokens,
                    "stream": False
                },
                timeout=60
            )
            
            if response.status_code == 200:
                data = response.json()
                return LLMResponse(
                    text=data.get("response", ""),
                    model=self.model_name,
                    provider="ollama",
                    metadata=data
                )
            else:
                return LLMResponse(
                    text=f"Ollama error: {response.status_code}",
                    model=self.model_name,
                    provider="ollama"
                )
        except Exception as e:
            return LLMResponse(
                text=f"Error generating response: {str(e)}",
                model=self.model_name,
                provider="ollama"
            )
    
    def score_response(self, prompt: str, response: str) -> float:
        """Simple heuristic scoring for Ollama"""
        # Length-based heuristic (can be improved)
        if len(response) < 10:
            return 0.2
        elif len(response) < 50:
            return 0.5
        elif len(response) < 200:
            return 0.7
        else:
            return 0.9


class HuggingFaceProvider(LLMProvider):
    """HuggingFace Inference API provider"""
    
    def __init__(self, model_name: str = "HuggingFaceH4/zephyr-7b-beta", api_token: Optional[str] = None, **kwargs):
        super().__init__(model_name, **kwargs)
        self.api_token = api_token or os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACE_TOKEN")
        
        try:
            from huggingface_hub import InferenceClient
            self.client = InferenceClient(token=self.api_token)
            self.available = True
        except ImportError:
            print("Warning: huggingface_hub not installed. Install with: pip install huggingface_hub")
            self.available = False
        except Exception as e:
            print(f"Warning: Could not initialize HuggingFace client: {e}")
            self.available = False
    
    def generate(self, prompt: str, temperature: float = 0.7, max_tokens: int = 1000, **kwargs) -> LLMResponse:
        if not self.available:
            return LLMResponse(
                text="HuggingFace provider not available. Please install huggingface_hub.",
                model=self.model_name,
                provider="huggingface"
            )
        
        try:
            response = self.client.text_generation(
                prompt,
                model=self.model_name,
                max_new_tokens=max_tokens,
                temperature=temperature,
                **kwargs
            )
            
            return LLMResponse(
                text=response,
                model=self.model_name,
                provider="huggingface"
            )
        except Exception as e:
            return LLMResponse(
                text=f"Error generating response: {str(e)}",
                model=self.model_name,
                provider="huggingface"
            )
    
    def score_response(self, prompt: str, response: str) -> float:
        """Simple heuristic scoring"""
        # Basic quality metrics
        score = 0.5
        
        # Length check
        if 50 <= len(response) <= 2000:
            score += 0.2
        
        # Contains relevant terms from prompt
        prompt_words = set(prompt.lower().split())
        response_words = set(response.lower().split())
        overlap = len(prompt_words & response_words) / max(len(prompt_words), 1)
        score += overlap * 0.3
        
        return min(1.0, score)


class LLMManager:
    """Unified LLM manager for seamless provider switching"""
    
    def __init__(self, default_provider: str = "huggingface"):
        self.providers: Dict[str, LLMProvider] = {}
        self.default_provider = default_provider
        self._initialize_providers()
    
    def _initialize_providers(self):
        """Initialize all available providers"""
        # OpenAI
        openai_model = os.getenv("OPENAI_MODEL", "gpt-4")
        self.providers["openai"] = OpenAIProvider(model_name=openai_model)
        
        # Ollama
        ollama_model = os.getenv("OLLAMA_MODEL", "llama2")
        ollama_url = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
        self.providers["ollama"] = OllamaProvider(model_name=ollama_model, base_url=ollama_url)
        
        # HuggingFace
        hf_model = os.getenv("HF_MODEL", "HuggingFaceH4/zephyr-7b-beta")
        self.providers["huggingface"] = HuggingFaceProvider(model_name=hf_model)
    
    def generate(
        self,
        prompt: str,
        provider: Optional[str] = None,
        **kwargs
    ) -> LLMResponse:
        """Generate text using specified or default provider"""
        provider_name = provider or self.default_provider
        
        if provider_name not in self.providers:
            return LLMResponse(
                text=f"Provider '{provider_name}' not found. Available: {list(self.providers.keys())}",
                model="unknown",
                provider="unknown"
            )
        
        return self.providers[provider_name].generate(prompt, **kwargs)
    
    def score_response(self, prompt: str, response: str, provider: Optional[str] = None) -> float:
        """Score response quality"""
        provider_name = provider or self.default_provider
        
        if provider_name not in self.providers:
            return 0.5
        
        return self.providers[provider_name].score_response(prompt, response)
    
    def get_available_providers(self) -> List[str]:
        """Get list of available providers"""
        return [
            name for name, provider in self.providers.items()
            if getattr(provider, 'available', True)
        ]
    
    def set_default_provider(self, provider: str):
        """Set default provider"""
        if provider in self.providers:
            self.default_provider = provider
        else:
            raise ValueError(f"Provider '{provider}' not found")


# Singleton instance
_llm_manager = None

def get_llm_manager(default_provider: Optional[str] = None) -> LLMManager:
    """Get or create LLM manager singleton"""
    global _llm_manager
    
    if _llm_manager is None:
        provider = default_provider or os.getenv("LLM_PROVIDER", "huggingface")
        _llm_manager = LLMManager(default_provider=provider)
    elif default_provider:
        _llm_manager.set_default_provider(default_provider)
    
    return _llm_manager