app.py

import dill
import json
import streamlit as st
import os
from haystack.utils import 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
from streamlit_lottie import st_lottie

st.set_page_config(page_title="QA-project", page_icon="📇")
os.environ['TOKENIZERS_PARALLELISM'] = "false"
DATA_DIR = './dataset'
DOCS_PATH = os.path.join(DATA_DIR, 'all_docs_36838.pkl')
LOTTIE_PATH = './img/108423-search-for-documents.json'
PROG_TITLE = "QA project Demo"
# Adjust to a question that you would like users to see in the search bar when they load the UI:
DEFAULT_QUESTION_AT_STARTUP = os.getenv("DEFAULT_QUESTION_AT_STARTUP", "What's the capital of France?")
DEFAULT_ANSWER_AT_STARTUP = os.getenv("DEFAULT_ANSWER_AT_STARTUP", "Paris")

def place_header_center(text, lottie_data):
    img, title= st.columns([1,3])
    with img:
        st_lottie(lottie_data, height=150)
    with title:
        st.title(text)


@st.experimental_memo
def get_lottie(path):
    with open(path, 'r', errors='ignore') as f:
        lottie_data = json.load(f)
        return lottie_data


def load_and_write_data(document_store):

    with open(DOCS_PATH, "rb") as f:
        docs = dill.load(f)

    document_store.write_documents(docs)


def get_backlink(result):
    if result.get("document", None):
        doc = result["document"]
        if isinstance(doc, dict):
            if doc.get("meta", None):
                if isinstance(doc["meta"], dict):
                    if doc["meta"].get("url", None):
                        return doc["meta"]["url"]
    return None


def get_doc_name(result):
    if result.get("document", None):
        doc = result["document"]
        if isinstance(doc, dict):
            if doc.get("meta", None):
                if isinstance(doc["meta"], dict):
                    if doc["meta"].get("name", None):
                        return doc["meta"]["name"]
    return None

def get_doc_reg_id(result):
    if result.get("document", None):
        doc = result["document"]
        if isinstance(doc, dict):
            if doc.get("meta", None):
                if isinstance(doc["meta"], dict):
                    if doc["meta"].get("reg_id", None):
                        return doc["meta"]["reg_id"]
    return None
# Haystack Components
# @st.cache(allow_output_mutation=True)
# def start_haystack():
document_store = InMemoryDocumentStore() # use_bm25=True
load_and_write_data(document_store)
retriever = TfidfRetriever(document_store=document_store)
reader = FARMReader(model_name_or_path="mrm8488/RuPERTa-base-finetuned-squadv1",
                    use_gpu=False,
                    num_processes=1)
pipeline = ExtractiveQAPipeline(reader, retriever)


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

set_state_if_absent("question", DEFAULT_QUESTION_AT_STARTUP)
set_state_if_absent("answer", DEFAULT_ANSWER_AT_STARTUP)
set_state_if_absent("results", None)


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

# Streamlit App
lottie_data = get_lottie(LOTTIE_PATH)
place_header_center(PROG_TITLE, lottie_data)

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 different program modules
Go ahead and ask questions about the program modules functionality!
""", 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}})
    results = []
    for answer in prediction["answers"]:
        answer = answer.to_dict()
        if answer["answer"]:
            results.append(
                {
                    "context": "..." + answer["context"] + "...",
                    "answer": answer["answer"],
                    "relevance": round(answer["score"] * 100, 2),
                    "document": [doc for doc in prediction["documents"] if doc["id"] == answer["document_id"]][0],
                    # "_raw": answer,
                    "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 program modules..."):
        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="#ff700f", color='#ffffff')) + context[end_idx:]),
                unsafe_allow_html=True,
            )
            source = ""
            url = get_backlink(result)
            name = get_doc_name(result)
            reg_id = get_doc_reg_id(result)
            if name:
                source += f"[{result['document']['meta']['name']}]"
            
            if url:
                source += f"({result['document']['meta']['url']})"

            if reg_id:
                source += f"({result['document']['meta']['reg_id']})"

            st.markdown(f"**Relevance:** {result['relevance']} -  **Source:** {source}")
            
        else:
            st.info(
                "🤔    Haystack is unsure whether any of the documents contain an answer to your question. Try to reformulate it!"
            )