import streamlit as st

from utils.config import document_store_configs, model_configs
from haystack import Pipeline
from haystack.schema import Answer
from haystack.document_stores import BaseDocumentStore
from haystack.document_stores import InMemoryDocumentStore, OpenSearchDocumentStore, WeaviateDocumentStore
from haystack.nodes import EmbeddingRetriever, FARMReader, PromptNode, PreProcessor
#from haystack.nodes import TextConverter, FileTypeClassifier, PDFToTextConverter
from milvus_haystack import MilvusDocumentStore
#Use this file to set up your Haystack pipeline and querying

@st.cache_resource(show_spinner=False)
def start_preprocessor_node():
    print('initializing preprocessor node')
    processor = PreProcessor(
        clean_empty_lines= True,
        clean_whitespace=True,
        clean_header_footer=True,
        #remove_substrings=None,
        split_by="word",
        split_length=100,
        split_respect_sentence_boundary=True,
        #split_overlap=0,
        #max_chars_check= 10_000
    )
    return processor
    #return docs

@st.cache_resource(show_spinner=False)
def start_document_store(type: str):
    #This function starts the documents store of your choice based on your command line preference
    print('initializing document store')
    if type == 'inmemory':
        document_store = InMemoryDocumentStore(use_bm25=True, embedding_dim=384)
        '''
        documents = [
            {
                'content': "Pi is a super dog",
                'meta': {'name': "pi.txt"}
            },
            {
                'content': "The revenue of siemens is 5 milion Euro",
                'meta': {'name': "siemens.txt"}
            },
        ]
        document_store.write_documents(documents)
        '''
    elif type == 'opensearch':
        document_store = OpenSearchDocumentStore(scheme = document_store_configs['OPENSEARCH_SCHEME'], 
                                                 username = document_store_configs['OPENSEARCH_USERNAME'], 
                                                 password = document_store_configs['OPENSEARCH_PASSWORD'],
                                                 host = document_store_configs['OPENSEARCH_HOST'],
                                                 port = document_store_configs['OPENSEARCH_PORT'],
                                                 index = document_store_configs['OPENSEARCH_INDEX'],
                                                 embedding_dim = document_store_configs['OPENSEARCH_EMBEDDING_DIM'])
    elif type == 'weaviate':
        document_store = WeaviateDocumentStore(host = document_store_configs['WEAVIATE_HOST'],
                                                port = document_store_configs['WEAVIATE_PORT'],
                                                index = document_store_configs['WEAVIATE_INDEX'],
                                                embedding_dim = document_store_configs['WEAVIATE_EMBEDDING_DIM'])
    elif type == 'milvus':
        document_store = MilvusDocumentStore(uri = document_store_configs['MILVUS_URI'],
                                            index = document_store_configs['MILVUS_INDEX'],
                                            embedding_dim = document_store_configs['MILVUS_EMBEDDING_DIM'],
                                            return_embedding=True)
    return document_store

# cached to make index and models load only at start
@st.cache_resource(show_spinner=False)
def start_retriever(_document_store: BaseDocumentStore):
    print('initializing retriever')
    retriever = EmbeddingRetriever(document_store=_document_store,
                                   embedding_model=model_configs['EMBEDDING_MODEL'],
                                   top_k=5)
    #

    #_document_store.update_embeddings(retriever)
    return retriever


@st.cache_resource(show_spinner=False)
def start_reader():
    print('initializing reader')
    reader = FARMReader(model_name_or_path=model_configs['EXTRACTIVE_MODEL'])
    return reader



# cached to make index and models load only at start
@st.cache_resource(show_spinner=False)
def start_haystack_extractive(_document_store: BaseDocumentStore, _retriever: EmbeddingRetriever, _reader: FARMReader):
    print('initializing pipeline')
    pipe = Pipeline()
    pipe.add_node(component=_retriever, name="Retriever", inputs=["Query"])
    pipe.add_node(component= _reader, name="Reader", inputs=["Retriever"])
    return pipe

@st.cache_resource(show_spinner=False)
def start_haystack_rag(_document_store: BaseDocumentStore, _retriever: EmbeddingRetriever, openai_key):
    prompt_node = PromptNode(default_prompt_template="deepset/question-answering", 
                             model_name_or_path=model_configs['GENERATIVE_MODEL'],
                             api_key=openai_key,
                             max_length=500)
    pipe = Pipeline()

    pipe.add_node(component=_retriever, name="Retriever", inputs=["Query"])
    pipe.add_node(component=prompt_node, name="PromptNode", inputs=["Retriever"])

    return pipe

#@st.cache_data(show_spinner=True)
def query(_pipeline, question):
    params = {}
    results = _pipeline.run(question, params=params)
    return results

def initialize_pipeline(task, document_store, retriever, reader, openai_key = ""):
    if task == 'extractive':
        return start_haystack_extractive(document_store, retriever, reader)
    elif task == 'rag':
        return start_haystack_rag(document_store, retriever, openai_key)