from abc import ABC, abstractmethod
from haystack.nodes import BM25Retriever, FARMReader
from haystack.document_stores import ElasticsearchDocumentStore
from haystack.pipelines import ExtractiveQAPipeline, DocumentSearchPipeline
from haystack.document_stores import PineconeDocumentStore
from haystack.nodes import EmbeddingRetriever

import json
import logging
import os
import shutil
import sys
import uuid
from json import JSONDecodeError
from pathlib import Path
from typing import List, Optional

import pandas as pd
import pinecone
import streamlit as st

from haystack import BaseComponent, Document
from haystack.document_stores import PineconeDocumentStore
from haystack.nodes import (
    EmbeddingRetriever,
    FARMReader
)
from haystack.pipelines import ExtractiveQAPipeline, Pipeline
from sentence_transformers import SentenceTransformer

import certifi
import datetime
import requests
from base64 import b64encode

ca_certs=certifi.where()        

class PineconeRetriever(BaseComponent):
    outgoing_edges = 1

    def __init__(self, sentence_transformer_name: str, api_key:str, environment: str, index_name: str): 
        # a small subset of the component's parameters is sent in an event after applying filters defined in haystack.telemetry.NonPrivateParameters
        self.sts_model = SentenceTransformer(sentence_transformer_name)
        pinecone.init(api_key = api_key, environment=environment)
        self.index = pinecone.Index(index_name)

    def run(self, query: str, top_k: Optional[int]):
        # process the inputs
        vector_embeddings = self.sts_model.encode(query).tolist()
        response = self.index.query([vector_embeddings], top_k=top_k, include_metadata=True)
        docs = [
            Document(
                content=d["metadata"]['content'],
                meta={'title': d["metadata"]['title'], 
                      'page': d["metadata"]['page'],
                      'source': d["metadata"]['source']
                      }
                )
                for d in response["matches"]
        ]
        output = {"documents": docs, "query": query}
        return output, "output_1"

    def run_batch(self, queries: List[str], top_k: Optional[int]):
        return {}, "output_1"


class DocumentQueries(ABC):
    
    @abstractmethod
    def search_by_query(self, query : str, retriever_top_k: int, reader_top_k: int, es_index: str):
        pass

class PinecodeProposalQueries(DocumentQueries):
    
    def __init__(self, es_host: str, es_index: str, es_user, es_password, reader_name_or_path: str, use_gpu = True) -> None:
        reader = FARMReader(model_name_or_path = reader_name_or_path, use_gpu = use_gpu, num_processes=1, context_window_size=200)
        self._initialize_pipeline(es_host, es_index, es_user, es_password, reader = reader)
        #self.log = Log(es_host= es_host, es_index="log", es_user = es_user, es_password= es_password)

    def _initialize_pipeline(self, es_host, es_index, es_user, es_password, reader = None):
        if reader is not None:
            self.reader = reader
        
        #pinecone.init(api_key=es_password, environment="us-east1-gcp")
        index_name = "semantic-text-search"
        
        self.document_store = PineconeDocumentStore(
            api_key=es_password,
            environment = "us-east1-gcp",
            index=index_name,
            similarity="cosine",
            embedding_dim=384
        )
        self.pipe = Pipeline()
        pinecone_retriever = PineconeRetriever("sentence-transformers/multi-qa-MiniLM-L6-cos-v1", 
                                               es_password, "us-east1-gcp", 
                                               index_name)
        self.pipe.add_node(component=pinecone_retriever, name="Retriever", inputs=["Query"])
        self.pipe.add_node(component=self.reader, name="Reader", inputs=["Retriever"])
               
        # #self.retriever = BM25Retriever(document_store = self.document_store)
        # self.retriever = EmbeddingRetriever(
        #     document_store=self.document_store,
        #     #embedding_model="multi-qa-distilbert-dot-v1",
        #     embedding_model = "sentence-transformers/msmarco-MiniLM-L6-cos-v5",
        #     model_format="sentence_transformers"
        # )
        
        # retriever_model = "sentence-transformers/multi-qa-mpnet-base-dot-v1"


        
        #self.document_store.update_embeddings(self.retriever, update_existing_embeddings=False)
        #self.pipe = ExtractiveQAPipeline (reader = self.reader, retriever = self.retriever)
        #self.pipe = DocumentSearchPipeline(self.retriever)

    def search_by_query(self, query : str, retriever_top_k: int, reader_top_k: int, es_index: str = None) :
        #self.document_store.update_embeddings(self.retriever, update_existing_embeddings=False)

        #if es_index is not None:
            #self._initialize_pipeline(self.es_host, es_index, self.es_user, self.es_password)
        params = {"Retriever": {"top_k": retriever_top_k}, "Reader": {"top_k": reader_top_k}}
        #params = {"Retriever": {"top_k": retriever_top_k}}
        prediction = self.pipe.run( query = query, params = params)      
        return prediction["answers"]

class Log():
    
    def __init__(self, es_host: str, es_index: str, es_user, es_password) -> None:
        self.elastic_endpoint = f"https://{es_host}:443/{es_index}/_doc"
        self.credentials = b64encode(b"3pvrzh9tl:4yl4vk9ijr").decode("ascii")
        self.auth_header = { 'Authorization' : 'Basic %s' %  self.credentials }
                      
    def write_log(self, message: str, source: str) -> None:
        created_date = datetime.datetime.now().strftime('%Y-%m-%dT%H:%M:%SZ')
        post_data  = {
            "message" : message,
            "createdDate": {
                "date" : created_date
            },
            "source": source
        }
        r = requests.post(self.elastic_endpoint, json = post_data, headers = self.auth_header)
        print(r.text)