from typing import Dict
from business_transaction_map.common.constants import DEVICE, DO_NORMALIZATION, COLUMN_TYPE_DOC_MAP
from business_transaction_map.components.faiss_vector_database import FaissVectorDatabase
from business_transaction_map.components.embedding_extraction import EmbeddingExtractor
import os
import requests
from prompts import BUSINESS_TRANSACTION_PROMPT


db_files_path = os.environ.get("GLOBAL_TRANSACTION_MAPS_DATA_PATH", "transaction_maps_search_data/csv/карта_проводок_new.pkl")

model_path = os.environ.get("GLOBAL_TRANSACTION_MAPS_MODEL_PATH", "")
llm_api_endpoint = os.environ.get("LLM_API_ENDPOINT", "")

class TransactionMapsSearch:
    
    def __init__(self,
                 model_name_or_path: str = model_path,
                 device: str = DEVICE):
        
        self.device = device
        self.model = self.load_model(
            model_name_or_path=model_name_or_path, 
            device=device
            )
        self.database = FaissVectorDatabase(str(db_files_path))
        
    @staticmethod
    def extract_business_transaction_with_llm(question: str) -> str:
        
        question = BUSINESS_TRANSACTION_PROMPT.replace('{{ЗАПРОС}}', question)

        response = requests.post(url=llm_api_endpoint,
                                 json={"prompt": f"[INST] {question} [/INST]", #пробелы внутри [INST], как оказалось, важны. Без них можно словить бесконечную генерацию бреда от ллм
                                       "temperature": 0.0})
        return response.json()['content']
        

    @staticmethod
    def load_model(model_name_or_path: str = None,
                   device: str = None):
        
        model = EmbeddingExtractor(model_name_or_path, device)
        return model
    

    @staticmethod
    def filter_answer(answer: Dict) -> Dict:
        """
        Функция фильтрует ответы.
        Args:
            answer: Словарь с ответом и дополнительной информацией.

        Returns:
            Словарь уникальных ответов.
        """
        list_ = []
        del_key = []
        for key in answer:
            if answer[key]["doc_name"] in list_:
                del_key.append(key)
            else:
                list_.append(answer[key]["doc_name"])
        for i in del_key:
            answer.pop(i)
        return answer


    def search_transaction_map(self, 
                               query: str = None,
                               find_transaction_maps_by_question: bool = False,
                               k_neighbours: int = 15):
        
        if find_transaction_maps_by_question:
            query = self.extract_business_transaction_with_llm(query)
        cleaned_text = query.replace("\n", " ")
        # cleaned_text = 'query: ' + cleaned_text  # only for e5
        query_tokens = self.model.query_tokenization(cleaned_text)
        query_embeds = self.model.query_embed_extraction(query_tokens.to(self.device), DO_NORMALIZATION)[0]
        query_embeds = query_embeds[None, :]

        # Предсказывает расстояние и индекс. ИНДЕКС == номерам строк в df
        answer = self.database.search_transaction_map(query_embeds, k_neighbours)
        answer = self.filter_answer(answer)
        final_docs = {}
        answers = []
        for value in list(answer.values()):
            final_docs[value["doc_name"] + '.xlsx'] = value[COLUMN_TYPE_DOC_MAP].upper() if 'sap' in value[
                COLUMN_TYPE_DOC_MAP] else value[COLUMN_TYPE_DOC_MAP]
            answers.append(answer)
        return final_docs, answers