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_ollama_llm():
    """
    Initialize and return an Ollama LLM instance configured for Arabic support.
    """
    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')
        # timeout = getattr(settings, 'OLLAMA_TIMEOUT', 120)

        return OllamaLLM(
            base_url=base_url,
            model=model,
            temperature= 0.2,
            top_p= 0.8,
            top_k= 40,
            num_ctx= 4096,
            num_predict= 2048,
            stop= ["```", "</s>"],
            repeat_penalty= 1.1,
        )
    except Exception as e:
        logger.error(f"Error initializing Ollama LLM: {str(e)}")
        return None


def create_prompt_analyzer_chain(language='ar'):
    """
    Create a LangChain for analyzing prompts in Arabic or English.
    """
    llm = get_ollama_llm()
    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