streamlit_app.py

import os
import re
from hashlib import blake2b
from tempfile import NamedTemporaryFile

import dotenv
from grobid_quantities.quantities import QuantitiesAPI
from langchain.llms.huggingface_hub import HuggingFaceHub

dotenv.load_dotenv(override=True)

import streamlit as st
from langchain.chat_models import PromptLayerChatOpenAI
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceEmbeddings

from document_qa.document_qa_engine import DocumentQAEngine
from document_qa.grobid_processors import GrobidAggregationProcessor, decorate_text_with_annotations
from grobid_client_generic import GrobidClientGeneric

if 'rqa' not in st.session_state:
    st.session_state['rqa'] = None

if 'api_key' not in st.session_state:
    st.session_state['api_key'] = False

if 'doc_id' not in st.session_state:
    st.session_state['doc_id'] = None

if 'loaded_embeddings' not in st.session_state:
    st.session_state['loaded_embeddings'] = None

if 'hash' not in st.session_state:
    st.session_state['hash'] = None

if 'git_rev' not in st.session_state:
    st.session_state['git_rev'] = "unknown"
    if os.path.exists("revision.txt"):
        with open("revision.txt", 'r') as fr:
            from_file = fr.read()
            st.session_state['git_rev'] = from_file if len(from_file) > 0 else "unknown"

if "messages" not in st.session_state:
    st.session_state.messages = []


def new_file():
    st.session_state['loaded_embeddings'] = None
    st.session_state['doc_id'] = None


@st.cache_resource
def init_qa(model):
    if model == 'chatgpt-3.5-turbo':
        chat = PromptLayerChatOpenAI(model_name="gpt-3.5-turbo",
                                     temperature=0,
                                     return_pl_id=True,
                                     pl_tags=["streamlit", "chatgpt"])
        embeddings = OpenAIEmbeddings()
    elif model == 'mistral-7b-instruct-v0.1':
        chat = HuggingFaceHub(repo_id="mistralai/Mistral-7B-Instruct-v0.1",
                              model_kwargs={"temperature": 0.01, "max_length": 4096, "max_new_tokens": 2048})
        embeddings = HuggingFaceEmbeddings(
            model_name="all-MiniLM-L6-v2")
    elif model == 'llama-2-70b-chat':
        chat = HuggingFaceHub(repo_id="meta-llama/Llama-2-70b-chat-hf",
                              model_kwargs={"temperature": 0.01, "max_length": 4096, "max_new_tokens": 2048})
        embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
    else:
        st.error("The model was not loaded properly. Try reloading. ")

    return DocumentQAEngine(chat, embeddings, grobid_url=os.environ['GROBID_URL'])


@st.cache_resource
def init_ner():
    quantities_client = QuantitiesAPI(os.environ['GROBID_QUANTITIES_URL'], check_server=True)

    materials_client = GrobidClientGeneric(ping=True)
    config_materials = {
        'grobid': {
            "server": os.environ['GROBID_MATERIALS_URL'],
            'sleep_time': 5,
            'timeout': 60,
            'url_mapping': {
                'processText_disable_linking': "/service/process/text?disableLinking=True",
                # 'processText_disable_linking': "/service/process/text"
            }
        }
    }

    materials_client.set_config(config_materials)

    gqa = GrobidAggregationProcessor(None,
                                     grobid_quantities_client=quantities_client,
                                     grobid_superconductors_client=materials_client
                                     )

    return gqa


gqa = init_ner()


def get_file_hash(fname):
    hash_md5 = blake2b()
    with open(fname, "rb") as f:
        for chunk in iter(lambda: f.read(4096), b""):
            hash_md5.update(chunk)
    return hash_md5.hexdigest()


def play_old_messages():
    if st.session_state['messages']:
        for message in st.session_state['messages']:
            if message['role'] == 'user':
                with st.chat_message("user"):
                    st.markdown(message['content'])
            elif message['role'] == 'assistant':
                with st.chat_message("assistant"):
                    if mode == "LLM":
                        st.markdown(message['content'], unsafe_allow_html=True)
                    else:
                        st.write(message['content'])


is_api_key_provided = st.session_state['api_key']

model = st.sidebar.radio("Model (cannot be changed after selection or upload)",
                         ("chatgpt-3.5-turbo", "mistral-7b-instruct-v0.1"),  # , "llama-2-70b-chat"),
                         index=1,
                         captions=[
                             "ChatGPT 3.5 Turbo + Ada-002-text (embeddings)",
                             "Mistral-7B-Instruct-V0.1 + Sentence BERT (embeddings)"
                             # "LLama2-70B-Chat + Sentence BERT (embeddings)",
                         ],
                         help="Select the model you want to use.",
                         disabled=is_api_key_provided)

if not st.session_state['api_key']:
    if model == 'mistral-7b-instruct-v0.1' or model == 'llama-2-70b-chat':
        api_key = st.sidebar.text_input('Huggingface API Key',
                                        type="password")  # if 'HUGGINGFACEHUB_API_TOKEN' not in os.environ else os.environ['HUGGINGFACEHUB_API_TOKEN']
        if api_key:
            st.session_state['api_key'] = is_api_key_provided = True
            os.environ["HUGGINGFACEHUB_API_TOKEN"] = api_key
            st.session_state['rqa'] = init_qa(model)
    elif model == 'chatgpt-3.5-turbo':
        api_key = st.sidebar.text_input('OpenAI API Key',
                                        type="password")  # if 'OPENAI_API_KEY' not in os.environ else os.environ['OPENAI_API_KEY']
        if api_key:
            st.session_state['api_key'] = is_api_key_provided = True
            os.environ['OPENAI_API_KEY'] = api_key
            st.session_state['rqa'] = init_qa(model)
else:
    is_api_key_provided = st.session_state['api_key']

st.title("📝 Scientific Document Insight Q&A")
st.subheader("Upload a scientific article in PDF, ask questions, get insights.")

upload_col, radio_col, context_col = st.columns([7, 2, 2])
with upload_col:
    uploaded_file = st.file_uploader("Upload an article", type=("pdf", "txt"), on_change=new_file,
                                     disabled=not is_api_key_provided,
                                     help="The full-text is extracted using Grobid. ")
with radio_col:
    mode = st.radio("Query mode", ("LLM", "Embeddings"), disabled=not uploaded_file, index=0,
                    help="LLM will respond the question, Embedding will show the "
                         "paragraphs relevant to the question in the paper.")
with context_col:
    context_size = st.slider("Context size", 3, 10, value=4,
                             help="Number of paragraphs to consider when answering a question",
                             disabled=not uploaded_file)

question = st.chat_input(
    "Ask something about the article",
    # placeholder="Can you give me a short summary?",
    disabled=not uploaded_file
)

with st.sidebar:
    st.header("Documentation")
    st.markdown("https://github.com/lfoppiano/document-qa")
    st.markdown(
        """After entering your API Key (Open AI or Huggingface). Upload a scientific article as PDF document. You will see a spinner or loading indicator while the processing is in progress. Once the spinner stops, you can proceed to ask your questions.""")

    st.markdown(
        '**NER on LLM responses**: The responses from the LLMs are post-processed to extract <span style="color:orange">physical quantities, measurements</span> and <span style="color:green">materials</span> mentions.',
        unsafe_allow_html=True)
    if st.session_state['git_rev'] != "unknown":
        st.markdown("**Revision number**: [" + st.session_state[
            'git_rev'] + "](https://github.com/lfoppiano/document-qa/commit/" + st.session_state['git_rev'] + ")")

    st.header("Query mode (Advanced use)")
    st.markdown(
        """By default, the mode is set to LLM (Language Model) which enables question/answering. You can directly ask questions related to the document content, and the system will answer the question using content from the document.""")

    st.markdown(
        """If you switch the mode to "Embedding," the system will return specific chunks from the document that are semantically related to your query. This mode helps to test why sometimes the answers are not satisfying or incomplete. """)

if uploaded_file and not st.session_state.loaded_embeddings:
    with st.spinner('Reading file, calling Grobid, and creating memory embeddings...'):
        binary = uploaded_file.getvalue()
        tmp_file = NamedTemporaryFile()
        tmp_file.write(bytearray(binary))
        # hash = get_file_hash(tmp_file.name)[:10]
        st.session_state['doc_id'] = hash = st.session_state['rqa'].create_memory_embeddings(tmp_file.name,
                                                                                             chunk_size=250,
                                                                                             perc_overlap=0.1)
        st.session_state['loaded_embeddings'] = True
        st.session_state.messages = []

    # timestamp = datetime.utcnow()

if st.session_state.loaded_embeddings and question and len(question) > 0 and st.session_state.doc_id:
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            if message['mode'] == "LLM":
                st.markdown(message["content"], unsafe_allow_html=True)
            elif message['mode'] == "Embeddings":
                st.write(message["content"])

    with st.chat_message("user"):
        st.markdown(question)
        st.session_state.messages.append({"role": "user", "mode": mode, "content": question})

    text_response = None
    if mode == "Embeddings":
        with st.spinner("Generating LLM response..."):
            text_response = st.session_state['rqa'].query_storage(question, st.session_state.doc_id,
                                                                  context_size=context_size)
    elif mode == "LLM":
        with st.spinner("Generating response..."):
            _, text_response = st.session_state['rqa'].query_document(question, st.session_state.doc_id,
                                                                      context_size=context_size)

    if not text_response:
        st.error("Something went wrong. Contact Luca Foppiano (Foppiano.Luca@nims.co.jp) to report the issue.")

    with st.chat_message("assistant"):
        if mode == "LLM":
            with st.spinner("Processing NER on LLM response..."):
                entities = gqa.process_single_text(text_response)
                # for entity in entities:
                #     entity
                decorated_text = decorate_text_with_annotations(text_response.strip(), entities)
                decorated_text = decorated_text.replace('class="label material"', 'style="color:green"')
                decorated_text = re.sub(r'class="label[^"]+"', 'style="color:orange"', decorated_text)
                st.markdown(decorated_text, unsafe_allow_html=True)
                text_response = decorated_text
        else:
            st.write(text_response)
        st.session_state.messages.append({"role": "assistant", "mode": mode, "content": text_response})

elif st.session_state.loaded_embeddings and st.session_state.doc_id:
    play_old_messages()