app.py

from utils.check_pydantic_version import use_pydantic_v1
use_pydantic_v1() #This function has to be run before importing haystack. as haystack requires pydantic v1 to run


from operator import index
import streamlit as st
import logging
import os

from annotated_text import annotation
from json import JSONDecodeError
from markdown import markdown
from utils.config import parser
from utils.haystack import start_document_store, query, initialize_pipeline, start_preprocessor_node, start_retriever, start_reader
from utils.ui import reset_results, set_initial_state
import pandas as pd
import haystack

from datetime import datetime
import streamlit.components.v1 as components
import streamlit_authenticator as stauth
import pickle

from streamlit_modal import Modal
import numpy as np



names = ['mlreply']
usernames = ['mlreply']
with open('hashed_password.pkl','rb') as f:
    hashed_passwords = pickle.load(f)



# Whether the file upload should be enabled or not
DISABLE_FILE_UPLOAD = bool(os.getenv("DISABLE_FILE_UPLOAD"))


def show_documents_list(retrieved_documents):
    data = []
    for i, document in enumerate(retrieved_documents):
        data.append([document.meta['name']])
    df = pd.DataFrame(data, columns=['Uploaded Document Name'])
    df.drop_duplicates(subset=['Uploaded Document Name'], inplace=True)
    df.index = np.arange(1, len(df) + 1)
    return df

# Define a function to handle file uploads
def upload_files():
    uploaded_files = upload_container.file_uploader(
            "upload", type=["pdf", "txt", "docx"], accept_multiple_files=True, label_visibility="hidden", key=1
        )
    return uploaded_files


# Define a function to process a single file
def process_file(data_file, preprocesor, document_store):
    # read file and add content
    file_contents = data_file.read().decode("utf-8")
    docs = [{
        'content': str(file_contents),
        'meta': {'name': str(data_file.name)}
    }]
    try:
        names = [item.meta.get('name') for item in document_store.get_all_documents()]
        #if args.store == 'inmemory':
        # doc = converter.convert(file_path=files, meta=None)
        if data_file.name in names:
            print(f"{data_file.name} already processed")
        else:
            print(f'preprocessing uploaded doc {data_file.name}.......')
            #print(data_file.read().decode("utf-8"))
            preprocessed_docs = preprocesor.process(docs)
            print('writing to document store.......')
            document_store.write_documents(preprocessed_docs)
            print('updating emebdding.......')
            document_store.update_embeddings(retriever)
    except Exception as e:
        print(e)


# Define a function to upload the documents to haystack document store
def upload_document():
    if data_files is not None:
        for data_file in data_files:
            # Upload file
            if data_file:
                try:
                    #raw_json = upload_doc(data_file)
                    # Call the process_file function for each uploaded file
                    if args.store == 'inmemory':
                        processed_data = process_file(data_file, preprocesor, document_store)
                    #upload_container.write(str(data_file.name) + "    ✅ ")
                except Exception as e:
                    upload_container.write(str(data_file.name) + "    ❌ ")
                    upload_container.write("_This file could not be parsed, see the logs for more information._")

# Define a function to reset the documents in haystack document store
def reset_documents():
    print('\nReseting documents list at ' + str(datetime.now()) + '\n')
    st.session_state.data_files = None
    document_store.delete_documents()

try:
    args = parser.parse_args()
    preprocesor = start_preprocessor_node()
    document_store = start_document_store(type=args.store)
    document_store.get_all_documents()
    retriever = start_retriever(document_store)
    reader = start_reader()
    st.set_page_config(
        page_title="MLReplySearch",
        layout="centered",
        page_icon=":shark:",
        menu_items={
            'Get Help': 'https://www.extremelycoolapp.com/help',
            'Report a bug': "https://www.extremelycoolapp.com/bug",
            'About': "# This is a header. This is an *extremely* cool app!"
        }
    )
    st.sidebar.image("ml_logo.png", use_column_width=True)

    authenticator = stauth.Authenticate(names, usernames, hashed_passwords, "document_search", "random_text", cookie_expiry_days=1)

    name, authentication_status, username = authenticator.login("Login", "main")

    if authentication_status == False:
        st.error("Username/Password is incorrect")

    if authentication_status == None:
        st.warning("Please enter your username and password")

    if authentication_status:

        # Sidebar for Task Selection
        st.sidebar.header('Options:')

        # OpenAI Key Input
        openai_key = st.sidebar.text_input("Enter LLM-authorization Key:", type="password")

        if openai_key:
            task_options = ['Extractive', 'Generative']
        else:
            task_options = ['Extractive']

        task_selection = st.sidebar.radio('Select the task:', task_options)

        # Check the task and initialize pipeline accordingly
        if task_selection == 'Extractive':
            pipeline_extractive = initialize_pipeline("extractive", document_store, retriever, reader)
        elif task_selection == 'Generative' and openai_key:  # Check for openai_key to ensure user has entered it
            pipeline_rag = initialize_pipeline("rag", document_store, retriever, reader, openai_key=openai_key)


        set_initial_state()

        modal = Modal("Manage Files", key="demo-modal")
        open_modal = st.sidebar.button("Manage Files", use_container_width=True)
        if open_modal:
            modal.open()

        st.write('# ' + args.name)
        if modal.is_open():
            with modal.container():
                if not DISABLE_FILE_UPLOAD:
                    upload_container = st.container()
                    data_files = upload_files()
                    upload_document()
                    st.session_state.sidebar_state = 'collapsed'
                st.table(show_documents_list(document_store.get_all_documents()))
        
        # File upload block
       # if not DISABLE_FILE_UPLOAD:
        #    upload_container = st.sidebar.container()
         #   upload_container.write("## File Upload:")
          #  data_files = upload_files()
            # Button to update files in the documentStore
           # upload_container.button('Upload Files', on_click=upload_document, args=())

        # Button to reset the documents in DocumentStore
        st.sidebar.button("Reset documents", on_click=reset_documents, args=(), use_container_width=True)

        if "question" not in st.session_state:
            st.session_state.question = ""
        # Search bar
        question = st.text_input("Question", value=st.session_state.question, max_chars=100, on_change=reset_results, label_visibility="hidden")

        run_pressed = st.button("Run")

        run_query = (
            run_pressed or question != st.session_state.question #or task_selection != st.session_state.task
        )

        # Get results for query
        if run_query and question:
            if task_selection == 'Extractive':
                reset_results()
                st.session_state.question = question
                with st.spinner("🔎    Running your pipeline"):
                    try:
                        st.session_state.results_extractive = query(pipeline_extractive, question)
                        st.session_state.task = task_selection
                    except JSONDecodeError as je:
                        st.error(
                            "👓    An error occurred reading the results. Is the document store working?"
                        )
                    except Exception as e:
                        logging.exception(e)
                        st.error("🐞    An error occurred during the request.")

            elif task_selection == 'Generative':
                reset_results()
                st.session_state.question = question
                with st.spinner("🔎    Running your pipeline"):
                    try:
                        st.session_state.results_generative = query(pipeline_rag, question)
                        st.session_state.task = task_selection
                    except JSONDecodeError as je:
                        st.error(
                            "👓    An error occurred reading the results. Is the document store working?"
                        )
                    except Exception as e:
                        if "API key is invalid" in str(e):
                            logging.exception(e)
                            st.error("🐞    incorrect API key provided. You can find your API key at https://platform.openai.com/account/api-keys.")
                        else:
                            logging.exception(e)
                            st.error("🐞    An error occurred during the request.")
        # Display results
        if (st.session_state.results_extractive or st.session_state.results_generative) and run_query:

            # Handle Extractive Answers
            if task_selection == 'Extractive':
                results = st.session_state.results_extractive

                st.subheader("Extracted Answers:")

                if 'answers' in results:
                    answers = results['answers']
                    treshold = 0.2
                    higher_then_treshold = any(ans.score > treshold for ans in answers)
                    if not higher_then_treshold:
                        st.markdown(f"<span style='color:red'>Please note none of the answers achieved a score higher then {int(treshold) * 100}%. Which probably means that the desired answer is not in the searched documents.</span>", unsafe_allow_html=True)
                    for count, answer in enumerate(answers):
                        if answer.answer:
                            text, context = answer.answer, answer.context
                            start_idx = context.find(text)
                            end_idx = start_idx + len(text)
                            score = round(answer.score, 3)
                            st.markdown(f"**Answer {count + 1}:**")
                            st.markdown(
                                context[:start_idx] + str(annotation(body=text, label=f'SCORE {score}', background='#964448', color='#ffffff')) + context[end_idx:],
                                unsafe_allow_html=True,
                            )
                        else:
                            st.info(
                                "🤔 &nbsp;&nbsp; Haystack is unsure whether any of the documents contain an answer to your question. Try to reformulate it!"
                            )

            # Handle Generative Answers
            elif task_selection == 'Generative':
                results = st.session_state.results_generative
                st.subheader("Generated Answer:")
                if 'results' in results:
                    st.markdown("**Answer:**")
                    st.write(results['results'][0])

            # Handle Retrieved Documents
            if 'documents' in results:
                retrieved_documents = results['documents']
                st.subheader("Retriever Results:")

                data = []
                for i, document in enumerate(retrieved_documents):
                    # Truncate the content
                    truncated_content = (document.content[:150] + '...') if len(document.content) > 150 else document.content
                    data.append([i + 1, document.meta['name'], truncated_content])

                # Convert data to DataFrame and display using Streamlit
                df = pd.DataFrame(data, columns=['Ranked Context', 'Document Name', 'Content'])
                st.table(df)
except SystemExit as e:
    os._exit(e.code)