from datetime import datetime as dt, timezone as tz
from flask import current_app
from sqlalchemy.exc import SQLAlchemyError
from celery import states
from celery.exceptions import Ignore
import os

# Unstructured commercial client imports
from unstructured_client import UnstructuredClient
from unstructured_client.models import shared
from unstructured_client.models.errors import SDKError

# OpenAI imports
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain.chains.summarize import load_summarize_chain
from langchain.text_splitter import CharacterTextSplitter
from langchain_core.exceptions import LangChainException

from common.utils.database import Database
from common.models.document import DocumentVersion, EmbeddingMistral, EmbeddingSmallOpenAI
from common.models.user import Tenant
from common.extensions import db
from common.utils.celery_utils import current_celery

from bs4 import BeautifulSoup


@current_celery.task(name='ask_question', queue='llm_interactions')
def ask_question(tenant_id, question):
    current_app.logger.debug('In ask_question')
    current_app.logger.debug(f'ask_question: Received question for tenant {tenant_id}: {question}. Processing...')

    try:
        # Retrieve the tenant
        tenant = Tenant.query.get(tenant_id)
        if not tenant:
            raise Exception(f'Tenant {tenant_id} not found.')

        # Ensure we are working in the correct database schema
        Database(tenant_id).switch_schema()

        # Select variables to work with depending on tenant model
        model_variables = select_model_variables(tenant)

        # create embedding for the query
        embedded_question = create_embedding(model_variables, question)

        # Search the database for relevant embeddings
        relevant_embeddings = search_embeddings(model_variables, embedded_question)

        response = ""
        for embed in relevant_embeddings:
            response += relevant_embeddings.chunk + '\n'

        return response
    except Exception as e:
        current_app.logger.error(f'ask_question: Error processing question: {e}')
        raise Ignore


def select_model_variables(tenant):
    embedding_provider = tenant.embedding_model.rsplit('.', 1)[0]
    embedding_model = tenant.embedding_model.rsplit('.', 1)[1]

    llm_provider = tenant.llm_model.rsplit('.', 1)[0]
    llm_model = tenant.llm_model.rsplit('.', 1)[1]

    # Set model variables
    model_variables = {}
    if tenant.es_k:
        model_variables['k'] = tenant.es_k
    else:
        model_variables['k'] = 5

    if tenant.es_similarity_threshold:
        model_variables['similarity_threshold'] = tenant.es_similarity_threshold
    else:
        model_variables['similarity_threshold'] = 0.7

    if tenant.chat_RAG_temperature:
        model_variables['RAG_temperature'] = tenant.chat_RAG_temperature
    else:
        model_variables['RAG_temperature'] = 0.3

    if tenant.chat_no_RAG_temperature:
        model_variables['no_RAG_temperature'] = tenant.chat_no_RAG_temperature
    else:
        model_variables['no_RAG_temperature'] = 0.5

    # Set Embedding variables
    match embedding_provider:
        case 'openai':
            match embedding_model:
                case 'text-embedding-3-small':
                    api_key = current_app.config.get('OPENAI_API_KEY')
                    model_variables['embedding'] = OpenAIEmbeddings(api_key=api_key,
                                                                    model='text-embedding-3-small')
                    model_variables['embedding_db_model'] = EmbeddingSmallOpenAI
                case _:
                    raise Exception(f'Error setting model variables for tenant {tenant.id} '
                                    f'error: Invalid embedding model')
        case _:
            raise Exception(f'Error setting model variables for tenant {tenant.id} '
                            f'error: Invalid embedding provider')

    # Set Chat model variables
    match llm_provider:
        case 'openai':
            api_key = current_app.config.get('OPENAI_API_KEY')
            model_variables['llm'] = ChatOpenAI(api_key=api_key,
                                                model=llm_model,
                                                temperature=model_variables['RAG_temperature'])
            match llm_model:
                case 'gpt-4-turbo' | 'gpt-4-o':
                    rag_template = current_app.config.get('GPT4_RAG_TEMPLATE')
                case 'gpt-3-5-turbo':
                    rag_template = current_app.config.get('GPT3_5_RAG_TEMPLATE')
                case _:
                    raise Exception(f'Error setting model variables for tenant {tenant.id} '
                                    f'error: Invalid chat model')
            model_variables['prompt'] = ChatPromptTemplate.from_template(rag_template)
        case _:
            raise Exception(f'Error setting model variables for tenant {tenant.id} '
                            f'error: Invalid chat provider')

    return model_variables


def create_embedding(model_variables, question):
    try:
        embeddings = model_variables['embedding'].embed_documents(question)
    except LangChainException as e:
        raise Exception(f'Error creating embedding for question (LangChain): {e}')

    return embeddings[0]


def search_embeddings(model_variables, embedded_query):
    current_app.logger.debug(f'In search_embeddings searching for {embedded_query}')
    db_class = model_variables['embedding_db_model']
    try:
        res = (
            db.session.query(db_class, db_class.embedding.cosine_distance(embedded_query).label('distance'))
            .filter(db_class.embedding.cosine_distance(embedded_query) < model_variables['similarity_threshold'])
            .order_by("distance")
            .limit(model_variables['k'])
            .all()
        )
    except SQLAlchemyError as e:
        raise Exception(f'Error searching embeddings (SQLAlchemy): {e}')

    current_app.logger.debug(f'Results from embedding search: {res}')
    return res


def tasks_ping():
    return 'pong'