from langchain_ollama import OllamaLLM
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from django.conf import settings
import logging

logger = logging.getLogger(__name__)


def get_llm_instance():
    """
    Initialize and return an Ollama LLM instance configured for Arabic support.
    
    This function creates a new LLM instance with optimized parameters for
    both Arabic and English language processing. It reads configuration from
    Django settings or uses sensible defaults.
    
    :return: Configured OllamaLLM instance or None if initialization fails
    :rtype: OllamaLLM or None
    """
    try:
        # Get settings from Django settings or use defaults
        base_url = getattr(settings, 'OLLAMA_BASE_URL', 'http://localhost:11434')
        model = getattr(settings, 'OLLAMA_MODEL', 'qwen3:8b')
        temperature = getattr(settings, 'OLLAMA_TEMPERATURE', 0.2)
        top_p = getattr(settings, 'OLLAMA_TOP_P', 0.8)
        top_k = getattr(settings, 'OLLAMA_TOP_K', 40)
        num_ctx = getattr(settings, 'OLLAMA_NUM_CTX', 4096)
        num_predict = getattr(settings, 'OLLAMA_NUM_PREDICT', 2048)
        
        return OllamaLLM(
            base_url=base_url,
            model=model,
            temperature=temperature,
            top_p=top_p,
            top_k=top_k,
            num_ctx=num_ctx,
            num_predict=num_predict,
            stop=["```", "</s>"],
            repeat_penalty=1.1,
        )
    except Exception as e:
        logger.error(f"Error initializing Ollama LLM: {str(e)}")
        return None


def get_llm_chain(language='en'):
    """
    Create a LangChain for analyzing prompts in Arabic or English.
    
    This function creates a chain that processes user prompts and extracts
    structured information about the analysis request. It supports both
    Arabic and English languages.
    
    :param language: Language code ('en' or 'ar')
    :type language: str
    :return: LangChain for prompt analysis or None if initialization fails
    :rtype: LLMChain or None
    """
    llm = get_llm_instance()
    if not llm:
        return None

    # Define the prompt template based on language
    if language == 'ar':
        template = """
        قم بتحليل الاستعلام التالي وتحديد نوع التحليل المطلوب ونماذج البيانات المستهدفة وأي معلمات استعلام.

        الاستعلام: {prompt}

        قم بتقديم إجابتك بتنسيق JSON كما يلي:
        {{
            "analysis_type": "count" أو "relationship" أو "performance" أو "statistics" أو "general",
            "target_models": ["ModelName1", "ModelName2"],
            "query_params": {{"field1": "value1", "field2": "value2"}}
        }}
        """
    else:
        template = """
        Analyze the following prompt and determine the type of analysis required, target data models, and any query parameters.

        Prompt: {prompt}

        Provide your answer in JSON format as follows:
        {
            "analysis_type": "count" or "relationship" or "performance" or "statistics" or "general",
            "target_models": ["ModelName1", "ModelName2"],
            "query_params": {"field1": "value1", "field2": "value2"}
        }
        """

    # Create the prompt template
    prompt_template = PromptTemplate(
        input_variables=["prompt"],
        template=template
    )

    # Create and return the LLM chain
    return prompt_template | llm