import streamlit as st
import os

from haystack import Pipeline
from haystack.utils import fetch_archive_from_http, clean_wiki_text, convert_files_to_docs
from haystack.schema import Answer
from haystack.document_stores import InMemoryDocumentStore
from haystack.pipelines import DocumentSearchPipeline, ExtractiveQAPipeline, GenerativeQAPipeline
from haystack.nodes import (DensePassageRetriever, EmbeddingRetriever, FARMReader,
                            OpenAIAnswerGenerator, Seq2SeqGenerator,
                            TfidfRetriever)
from haystack.nodes import RAGenerator
import logging
from markdown import markdown
from annotated_text import annotation
from PIL import Image
logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
logging.getLogger("haystack").setLevel(logging.INFO)

os.environ['TOKENIZERS_PARALLELISM'] = "false"
MY_API_KEY = os.environ.get("MY_API_KEY")


# Haystack Components
@st.cache(hash_funcs={"builtins.SwigPyObject": lambda _: None}, allow_output_mutation=True)
# @st.cache_data
def start_haystack():
    # document_store = InMemoryDocumentStore()
    # For dense retriever
    document_store = InMemoryDocumentStore(embedding_dim=128)
    # For OPEN AI retriever
    # document_store = InMemoryDocumentStore(embedding_dim=1024)
    load_and_write_data(document_store)
    # retriever = TfidfRetriever(document_store=document_store)
    retriever = DensePassageRetriever(
        document_store=document_store,
        query_embedding_model="vblagoje/dpr-question_encoder-single-lfqa-wiki",
        passage_embedding_model="vblagoje/dpr-ctx_encoder-single-lfqa-wiki",
    )

    # retriever = EmbeddingRetriever(
    #     document_store=document_store,
    #     embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1",
    #     model_format="sentence_transformers",
    # )
    document_store.update_embeddings(retriever)

    # OPEN AI
    # retriever = EmbeddingRetriever(
    #     document_store=document_store,
    #     batch_size=8,
    #     embedding_model="ada",
    #     api_key=MY_API_KEY,
    #     max_seq_len=1024
    # )
    # document_store.update_embeddings(retriever)

    # reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
    # pipeline = ExtractiveQAPipeline(reader, retriever)

    generator = Seq2SeqGenerator(model_name_or_path="vblagoje/bart_lfqa")
    # generator = OpenAIAnswerGenerator(
    #     api_key=MY_API_KEY,
    #     model="text-davinci-003",
    #     max_tokens=50,
    #     presence_penalty=0.1,
    #     frequency_penalty=0.1,
    #     top_k=3,
    #     temperature=0.9
    # )
    # pipe.add_node(component=retriever, name="Retriever", inputs=["Query"])
    # pipe.add_node(component=generator, name="prompt_node", inputs=["Query"])
    pipe = GenerativeQAPipeline(generator=generator, retriever=retriever)

    return pipe


def load_and_write_data(document_store):
    doc_dir = './dao_data'
    docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text,
                                 split_paragraphs=True)

    document_store.write_documents(docs)


pipeline = start_haystack()


def set_state_if_absent(key, value):
    if key not in st.session_state:
        st.session_state[key] = value


set_state_if_absent("question", "What is the goal of VitaDAO?")
set_state_if_absent("results", None)


def reset_results(*args):
    st.session_state.results = None


# Streamlit App

image = Image.open('got-haystack.png')
st.image(image)

st.markdown("""
This QA demo uses a [Haystack Extractive QA Pipeline](
https://haystack.deepset.ai/components/ready-made-pipelines#extractiveqapipeline) with 
an [InMemoryDocumentStore](https://haystack.deepset.ai/components/document-store) which contains 
documents about Game of Thrones 👑
Go ahead and ask questions about the marvellous kingdom!
""", unsafe_allow_html=True)

question = st.text_input("", value=st.session_state.question, max_chars=100,
                         on_change=reset_results)


def ask_question(question):
    # prediction = pipeline.run(query=question, params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}})
    prediction = pipeline.run(query=question, params={"Retriever": {"top_k": 10}, "Generator": {"top_k": 1}})
    results = []
    for answer in prediction["answers"]:
        answer = answer.to_dict()
        if answer["answer"]:
            print(answer)
            results.append(
                {
                    "context": "..." + str(answer["context"]) + "...",
                    "answer": answer["answer"],
                    # "relevance": round(answer["score"] * 100, 2),
                    # "offset_start_in_doc": answer["offsets_in_document"][0]["start"],
                }
            )
        else:
            results.append(
                {
                    "context": None,
                    "answer": None,
                    # "relevance": round(answer["score"] * 100, 2),
                }
            )
    return results


if question:
    with st.spinner("👑    Performing semantic search on royal scripts..."):
        try:
            msg = 'Asked ' + question
            logging.info(msg)
            st.session_state.results = ask_question(question)
        except Exception as e:
            logging.exception(e)

if st.session_state.results:
    st.write('## Top Results')
    for count, result in enumerate(st.session_state.results):
        if result["answer"]:
            answer, context = result["answer"], result["context"]
            start_idx = context.find(answer)
            end_idx = start_idx + len(answer)
            st.write(
                markdown(context[:start_idx] + str(
                    annotation(body=answer, label="ANSWER", background="#964448",
                               color='#ffffff')) + context[end_idx:]),
                unsafe_allow_html=True,
            )
            # st.markdown(f"**Relevance:** {result['relevance']}")
        else:
            st.info(
                "🤔    Haystack is unsure whether any of the documents contain an "
                "answer to your question. Try to reformulate it!"
            )