import os
import chromadb
from dotenv import load_dotenv
from langchain_core.documents import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.llms import OpenAI
from langchain.chains.question_answering import load_qa_chain
from langchain_community.vectorstores import Chroma
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.chat_models import ChatOpenAI
from config import Config


load_dotenv()

# ChromaDB configuration
CHROMA_HOST = Config.RAG_CHROMA_HOST
CHROMA_PORT = Config.RAG_CHROMA_PORT
COLLECTION_NAME = Config.RAG_COLLECTION_NAME

# LLM Provider Configuration
LLM_PROVIDER = Config.RAG_LLM_PROVIDER
LLM_API_KEY = Config.RAG_LLM_API_KEY
LLM_MODEL = Config.RAG_LLM_MODEL
LLM_TEMPERATURE = Config.RAG_LLM_TEMPERATURE
LLM_MAX_TOKENS = Config.RAG_LLM_MAX_TOKENS

# Provider-specific configurations
PROVIDER_CONFIGS = {
    "openai": {
        "api_base": "https://api.openai.com/v1",
        "default_model": "gpt-3.5-turbo"
    },
    "groq": {
        "api_base": "https://api.groq.com/openai/v1",
        "default_model": "llama-3.3-70b-versatile"
    },
    "openrouter": {
        "api_base": "https://openrouter.ai/api/v1",
        "default_model": "mistralai/mistral-small-3.2-24b-instruct:free"
    }
}

vector_store = None
company_qa_chain = None
query_router_chain = None
cybersecurity_chain = None
llm = None

def get_llm_config():
    """Get the appropriate LLM configuration based on the provider."""
    if LLM_PROVIDER not in PROVIDER_CONFIGS:
        raise ValueError(f"Unsupported LLM provider: {LLM_PROVIDER}. Supported: {list(PROVIDER_CONFIGS.keys())}")
    
    config = PROVIDER_CONFIGS[LLM_PROVIDER].copy()
    
    # Use provided model or fall back to default
    model = LLM_MODEL if LLM_MODEL != "gpt-3.5-turbo" else config["default_model"]
    
    return {
        "model": model,
        "openai_api_key": LLM_API_KEY,
        "openai_api_base": config["api_base"],
        "temperature": LLM_TEMPERATURE,
        "max_tokens": LLM_MAX_TOKENS,
    }

def initialize_llm():
    """Initialize the LLM based on the configured provider."""
    if not LLM_API_KEY:
        raise ValueError(f"LLM_API_KEY environment variable is required for {LLM_PROVIDER}")
    
    config = get_llm_config()
    
    print(f"Initializing {LLM_PROVIDER.upper()} with model: {config['model']}")
    
    return ChatOpenAI(**config)

def initialize_pipelines():
    """Initializes all required models, chains, and the vector store."""
    global vector_store, company_qa_chain, query_router_chain, cybersecurity_chain, llm
    
    try:
        # Initialize LLM
        llm = initialize_llm()

        # Initialize embeddings
        embeddings = HuggingFaceEmbeddings(
            model_name="all-MiniLM-L6-v2",  
            model_kwargs={'device': 'cpu'}, 
            encode_kwargs={'normalize_embeddings': True}
        )
        
        # Initialize ChromaDB client 
        try:
            chroma_client = chromadb.HttpClient(host=CHROMA_HOST, port=CHROMA_PORT)
            chroma_client.heartbeat()
        except Exception as e:
            raise ConnectionError("Failed to connect to ChromaDB.") from e
        
        # Initialize vector store
        vector_store = Chroma(
            client=chroma_client,
            collection_name=COLLECTION_NAME,
            embedding_function=embeddings,
        )

        # Query Router Chain
        router_template = """You are a query classifier. Classify the following query into one of these categories:
- COMPANY: Questions about our company, its products, services, or general information
- CYBERSECURITY: Questions about cybersecurity, security threats, best practices, or vulnerabilities
- OFF_TOPIC: Questions that don't fit the above categories

Query: {query}

Respond with only the category name (COMPANY, CYBERSECURITY, or OFF_TOPIC):"""
        
        router_prompt = PromptTemplate(
            input_variables=["query"],
            template=router_template
        )
        
        query_router_chain = LLMChain(
            llm=llm,
            prompt=router_prompt
        )
        
        # Custom Company QA Chain
        company_qa_template = """You are a helpful assistant for CyberAlertNepal. Answer the following question about our company using the information provided and links if only available. Give a natural, direct and polite response.

Question: {question}

Information:
{context}

Answer:"""
        
        company_qa_prompt = PromptTemplate(
            input_variables=["question", "context"],
            template=company_qa_template
        )
        
        company_qa_chain = LLMChain(
            llm=llm,
            prompt=company_qa_prompt
        )
        
        # Cybersecurity Chain
        cybersecurity_template = """You are a cybersecurity professional. Answer the following question truthfully and concisely. 
If you are not 100% sure about the answer, simply respond with: "I am not sure about the answer." 
Do not add extra explanations or assumptions. Do not provide false or speculative information.

Question: {question}

Provide a comprehensive and accurate answer about cybersecurity:"""
        
        cybersecurity_prompt = PromptTemplate(
            input_variables=["question"],
            template=cybersecurity_template
        )
        
        cybersecurity_chain = LLMChain(
            llm=llm,
            prompt=cybersecurity_prompt
        )
        
        print(f"Successfully initialized pipelines with {LLM_PROVIDER.upper()}")

    except Exception as e:
        print(f"Error initializing pipelines: {e}")
        raise

def add_document_to_rag(text: str, metadata: dict):
    """Splits a document and adds it to the ChromaDB index."""
    global vector_store
    
    if not vector_store:
        initialize_pipelines()
        
    try:
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000, 
            chunk_overlap=200
        )
        docs = text_splitter.create_documents([text], metadatas=[metadata])
        
        if not docs:
            print("Document was empty after splitting, not adding to ChromaDB.")
            return False

        vector_store.add_documents(docs)
        print("Successfully added documents.")
        return True
        
    except Exception as e:
        print(f"Error adding document to RAG: {e}")
        return False

def route_and_process_query(query: str):
    """Routes the query and processes it using the appropriate pipeline."""
    global query_router_chain, vector_store, company_qa_chain, cybersecurity_chain
    
    if not all([query_router_chain, vector_store, company_qa_chain, cybersecurity_chain]):
        initialize_pipelines()

    try:
        # 1. Classify the query
        route_result = query_router_chain.run(query)
        route = route_result.strip().upper()
        

        # 2. Route to appropriate logic
        if "CYBERSECURITY" in route:
            answer = cybersecurity_chain.run(question=query)
            return {
                "answer": answer, 
                "source": "Cybersecurity Knowledge Base",
                "route": "CYBERSECURITY",
                "provider": LLM_PROVIDER.upper(),
                "model": get_llm_config()["model"]
            }
            
        elif "COMPANY" in route:
            # Perform similarity search on ChromaDB
            docs = vector_store.similarity_search(query, k=3)

            if not docs:
                return {
                    "answer": "I could not find any relevant information to answer your question.",
                    "source": "Company Documents",
                    "route": "COMPANY",
                    "provider": LLM_PROVIDER.upper(),
                    "model": get_llm_config()["model"]
                }
            
            # Combine document content for context
            context = "\n\n".join([doc.page_content for doc in docs])
            
            # Run the custom QA chain
            answer = company_qa_chain.run(question=query, context=context)
            sources = list(set([doc.metadata.get("source", "Unknown") for doc in docs]))
            
            return {
                "answer": answer,
                "source": "Company Documents",
                "documents": sources,
                "route": "COMPANY",
                "provider": LLM_PROVIDER.upper(),
                "model": get_llm_config()["model"]
            }

        else:  # OFF_TOPIC
            return {
                "answer": "I am a specialized assistant of CyberAlertNepal. I cannot answer questions outside of cybersecurity topics.",
                "source": "N/A",
                "route": "OFF_TOPIC",
                "provider": LLM_PROVIDER.upper(),
                "model": get_llm_config()["model"]
            }
            
    except Exception as e:
        print(f"Error processing query: {e}")
        return {
            "answer": "I encountered an error while processing your query. Please try again.",
            "source": "Error",
            "route": None,
            "documents": None,
            "provider": LLM_PROVIDER.upper(),
            "error": str(e)
        }

def check_system_health():
    """Check if all components are properly initialized."""
    try:
        # Test ChromaDB connection
        if vector_store:
            vector_store._client.heartbeat()
        
        # Test if all chains are initialized
        components = {
            "vector_store": vector_store is not None,
            "company_qa_chain": company_qa_chain is not None,
            "query_router_chain": query_router_chain is not None,
            "cybersecurity_chain": cybersecurity_chain is not None,
            "llm": llm is not None
        }
        
        return {
            "status": "healthy" if all(components.values()) else "unhealthy",
            "components": components,
            "provider": LLM_PROVIDER.upper(),
            "model": get_llm_config()["model"] if llm else "Not initialized"
        }
        
    except Exception as e:
        return {
            "status": "unhealthy",
            "error": str(e),
            "provider": LLM_PROVIDER.upper()
        }

def test_llm_connection():
    """Test the LLM API connection."""
    try:
        if not llm:
            initialize_pipelines()
        
        # Simple test query
        test_response = llm("Say 'Hello, LLM is working!'")
        return {
            "success": True,
            "provider": LLM_PROVIDER.upper(),
            "model": get_llm_config()["model"],
            "response": str(test_response)
        }
    except Exception as e:
        return {
            "success": False,
            "provider": LLM_PROVIDER.upper(),
            "error": str(e)
        }

# Initialize pipelines on module import
try:
    initialize_pipelines()
except Exception as e:
    print(f"Failed to initialize pipelines on startup: {e}")