import streamlit as st
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 ExtractiveQAPipeline
from haystack.nodes import FARMReader, TfidfRetriever
import logging
from markdown import markdown
from annotated_text import annotation
import validators
import json

#Haystack Components
document_store = InMemoryDocumentStore()
retriever = TfidfRetriever(document_store=document_store)
reader = FARMReader(model_name_or_path="deepset/tinyroberta-squad2", use_gpu=True)
pipeline = ExtractiveQAPipeline(reader, retriever)

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


st.set_page_config(page_title="Game of Thrones QA with Haystack", page_icon="https://haystack.deepset.ai/img/HaystackIcon.png")

set_state_if_absent("question", "Who is Arya's father")
set_state_if_absent("results", None)


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

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

    document_store.write_documents(docs)


#Streamlit App

st.title('Game of Thrones QA with Haystack')
question = st.text_input("", value=st.session_state.question, max_chars=100, on_change=reset_results)

load_and_write_data()

def ask_question(question):
    prediction = pipeline.run(query=question, params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}})
    results = []
    for answer in prediction["answers"]:
        if answer.get("answer", None):
            results.append(
                {
                    "context": "..." + answer["context"] + "...",
                    "answer": answer.get("answer", None),
                    "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
    # st.write(prediction['answers'][0].to_dict())
    # st.write(prediction['answers'][1].to_dict())
    # st.write(prediction['answers'][2].to_dict())
    

if question:
    try:
        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(answer, "ANSWER", "#8ef")) + 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!"
            )