from haystack import Finder
from haystack.document_store.elasticsearch import ElasticsearchDocumentStore

from haystack.retriever.dense import EmbeddingRetriever
from haystack.utils import print_answers
import pandas as pd
import requests
import logging
import subprocess
import time


def tutorial4_faq_style_qa():
    ## "FAQ-Style QA": Utilizing existing FAQs for Question Answering

    # While *extractive Question Answering* works on pure texts and is therefore more generalizable, there's also a common alternative that utilizes existing FAQ data.
    #
    # Pros:
    # - Very fast at inference time
    # - Utilize existing FAQ data
    # - Quite good control over answers
    #
    # Cons:
    # - Generalizability: We can only answer questions that are similar to existing ones in FAQ
    #
    # In some use cases, a combination of extractive QA and FAQ-style can also be an interesting option.
    LAUNCH_ELASTICSEARCH=True

    if LAUNCH_ELASTICSEARCH:
        logging.info("Starting Elasticsearch ...")
        status = subprocess.run(
            ['docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2'], shell=True
        )
        if status.returncode:
            raise Exception("Failed to launch Elasticsearch. If you want to connect to an existing Elasticsearch instance"
                            "then set LAUNCH_ELASTICSEARCH in the script to False.")
        time.sleep(15)

    ### Init the DocumentStore
    # In contrast to Tutorial 1 (extractive QA), we:
    #
    # * specify the name of our `text_field` in Elasticsearch that we want to return as an answer
    # * specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question
    # * set `excluded_meta_data=["question_emb"]` so that we don't return the huge embedding vectors in our search results

    document_store = ElasticsearchDocumentStore(host="localhost", username="", password="",
                                                index="document",
                                                embedding_field="question_emb",
                                                embedding_dim=768,
                                                excluded_meta_data=["question_emb"],
                                                similarity="cosine")

    ### Create a Retriever using embeddings
    # Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).
    # We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings.
    #
    retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=True)

    # Download a csv containing some FAQ data
    # Here: Some question-answer pairs related to COVID-19
    temp = requests.get("https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/faqs/faq_covidbert.csv")
    open('small_faq_covid.csv', 'wb').write(temp.content)

    # Get dataframe with columns "question", "answer" and some custom metadata
    df = pd.read_csv("small_faq_covid.csv")
    # Minimal cleaning
    df.fillna(value="", inplace=True)
    df["question"] = df["question"].apply(lambda x: x.strip())
    print(df.head())

    # Get embeddings for our questions from the FAQs
    questions = list(df["question"].values)
    df["question_emb"] = retriever.embed_queries(texts=questions)
    df = df.rename(columns={"answer": "text"})

    # Convert Dataframe to list of dicts and index them in our DocumentStore
    docs_to_index = df.to_dict(orient="records")
    document_store.write_documents(docs_to_index)


    # Init reader & and use Finder to get answer (same as in Tutorial 1)
    finder = Finder(reader=None, retriever=retriever)
    prediction = finder.get_answers_via_similar_questions(question="How is the virus spreading?", top_k_retriever=10)
    print_answers(prediction, details="all")


if __name__ == "__main__":
    tutorial4_faq_style_qa()