app.py

import streamlit as st
from pypdf import PdfReader
# import replicate
import os
from pathlib import Path
from dotenv import load_dotenv
import pickle
import timeit
from PIL import Image
import datetime
import base64

from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import PyPDFLoader, DirectoryLoader
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts.prompt import PromptTemplate
from langchain.llms import LlamaCpp
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.vectorstores import Chroma
from langchain.document_loaders import PyPDFDirectoryLoader
from langchain.retrievers import BM25Retriever, EnsembleRetriever
from langchain.chat_models import ChatOpenAI
from langchain.agents.agent_toolkits import create_retriever_tool
from langchain.agents.agent_toolkits import create_conversational_retrieval_agent
from langchain.utilities import SerpAPIWrapper

from utils import build_embedding_model, build_llm
from utils import load_retriver,load_vectorstore, load_conversational_retrievel_chain

load_dotenv()
# Getting current timestamp to keep track of historical conversations
current_timestamp = datetime.datetime.now()
timestamp_string = current_timestamp.strftime("%Y-%m-%d %H:%M:%S")

#Directories path
persist_directory= "vector_db_gsa"
all_docs_pkl_directory= 'Database/text_chunks_html_pdf.pkl'

# Initliazing sesstion states in Streamlit to cache different stuffs like model iniitialization and there by avoid re-running of alredy initialized stuffs over and again.
if "llm" not in st.session_state:
    st.session_state["llm"] = build_llm()

if "embeddings" not in st.session_state:
    st.session_state["embeddings"] = build_embedding_model()
    
if "vector_db" not in st.session_state:
    st.session_state["vector_db"] = load_vectorstore(persist_directory=persist_directory, embeddings=st.session_state["embeddings"])
 
# if "text_chunks" not in st.session_state:
#     st.session_state["text_chunks"] = load_text_chunks(text_chunks_pkl_dir=all_docs_pkl_directory)

if "load_retriver" not in st.session_state:
    st.session_state["load_retriver"] = load_retriver(chroma_vectorstore=st.session_state["vector_db"] )

if "conversation_chain" not in st.session_state:
    st.session_state["conversation_chain"] = load_conversational_retrievel_chain(retriever=st.session_state["load_retriver"], llm=st.session_state["llm"])    



# App title
st.set_page_config(
    page_title="OMP Search Bot",
    layout="wide",
    initial_sidebar_state="expanded",
)

st.markdown("""
        <style>
               .block-container {
                    padding-top: 2.2rem}
        </style>
        """, unsafe_allow_html=True)
# To get header in the App
col1, col2= st.columns(2)

title1 = """
<p style="font-size: 26px;text-align: right; color: #0C3453; font-weight: bold">GSA Procurement Services Assistant</p>
"""

def clear_chat_history():
        st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]

file_ = open("logo.png", "rb")
contents = file_.read()
data_url = base64.b64encode(contents).decode("utf-8")
file_.close()

st.markdown(
    f"""
    <div style="background-color: white; padding: 15px; border-radius: 10px;">
        <div style="display: flex; justify-content: space-between;">
            <div>
                <img src="data:image/png;base64,{data_url}" style="max-width: 100%;" alt="OPM Logo" />
            </div>
            <div style="flex: 1; padding: 15px;">
                {title1}
    """,
    unsafe_allow_html=True
)
st.write("")


st.write('<p style="color: #B0B0B0;margin: 0;">The Procurement Services Digital AI Assistant is a quantum leap in GSA’s strategic goal of delivering better services to the public using modern technology. This AI enabled assistant makes it easy for citizens to get the information they need from the government by answering questions and providing assistance 24/7. It\'s designed to be user-friendly, making government services more accessible and reliable for all citizens. Just ask away.</p>', unsafe_allow_html=True)

st.markdown("""---""")

text_html = """
    <p style="font-size: 14px; text-align: center; color: #727477; margin: 0;">
        Type your question in conversational style
    </p>
    <p style="font-size: 14px; text-align: center; color: #727477; margin: 0;">
        Example: what is Electronic Protest Docketing System?
    </p>
"""

st.write(text_html, unsafe_allow_html=True)


with st.sidebar:
    st.subheader("")       
        
if st.session_state["vector_db"] and st.session_state["llm"]:
        # Store LLM generated responses
    if "messages" not in st.session_state.keys():
        st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?", "Source":""}]

    # Display or clear chat messages
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.write(message["content"])
            if message["Source"]=="":
                st.write("")
            else:
                with st.expander("source"):
                    for idx, item in enumerate(message["Source"]):
                        st.markdown(item["Page"])
                        st.markdown(item["Source"])
                        st.markdown(item["page_content"])
                        st.write("---")


    # Initialize the session state to store chat history
    if "stored_session" not in st.session_state:
        st.session_state["stored_session"] = []

    # Create a list to store expanders
    if "expanders" not in st.session_state:
        st.session_state["expanders"] = []
    
        # Define a function to add a new chat expander
    def add_chat_expander(chat_history):
        current_timestamp = datetime.datetime.now()
        timestamp_string = current_timestamp.strftime("%Y-%m-%d %H:%M:%S")
        st.session_state["expanders"].append({"timestamp": timestamp_string, "chat_history": chat_history})
                
    def clear_chat_history():
        """
        To remove existing chat history and start new conversation
        """
        stored_session = []
        for dict_message in st.session_state.messages:
            if dict_message["role"] == "user":
                string_dialogue = "User: " + dict_message["content"] + "\n\n"
                st.session_state["stored_session"].append(string_dialogue)

            else:
                string_dialogue = "Assistant: " + dict_message["content"] + "\n\n"
                st.session_state["stored_session"].append(string_dialogue)
            stored_session.append(string_dialogue)
        
            # Add a new chat expander
        add_chat_expander(stored_session)
        st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?", "Source":""}]
        
    st.sidebar.button('New chat', on_click=clear_chat_history, use_container_width=True)
    st.sidebar.text("")
    st.sidebar.write('<p style="font-size: 16px;text-align: center; color: #727477; font-weight: bold">Chat history</p>', unsafe_allow_html=True)
    # Display existing chat expanders
    for expander_info in st.session_state["expanders"]:
        with st.sidebar.expander("Conversation ended at:"+"\n\n"+expander_info["timestamp"]):
            for message in expander_info["chat_history"]:
                if message.startswith("User:"):
                    st.write(f'<span style="color: #EF6A6A;">{message}</span>', unsafe_allow_html=True)
                elif message.startswith("Assistant:"):
                    st.write(f'<span style="color: #F7BD45;">{message}</span>', unsafe_allow_html=True)
                else:
                    st.write(message)


    def generate_llm_response(conversation_chain, prompt_input):
        # output= conversation_chain({'question': prompt_input})
        res = conversation_chain(prompt_input)
        return res['result']


    # User-provided prompt
    if prompt := st.chat_input(disabled= not st.session_state["vector_db"]):
        st.session_state.messages.append({"role": "user", "content": prompt, "Source":""})
        with st.chat_message("user"):
            st.write(prompt)

    # Generate a new response if last message is not from assistant
    if st.session_state.messages[-1]["role"] != "assistant":
        with st.chat_message("assistant"):
            with st.spinner("Searching..."):
                start = timeit.default_timer()
                response = generate_llm_response(conversation_chain=st.session_state["conversation_chain"], prompt_input=prompt)
                placeholder = st.empty()
                full_response = ''
                for item in response:
                    full_response += item
                placeholder.markdown(full_response)
                # The following logic will work in the way given below.
                # -- Check if intermediary steps are present in the output of the given prompt.
                # -- If not, we can conclude that, agent has used internet search as tool.
                # -- Check if intermediary steps are present in the output of the prompt.
                # -- If intermediary steps are present, it means agent has used exising custom knowledge base for iformation retrival and therefore we need to give souce docs as output along with LLM's reponse.
                if response:
                    st.text("-------------------------------------")
                    docs= st.session_state["load_retriver"].get_relevant_documents(prompt)   
                    source_doc_list= []  
                    for doc in docs:
                        source_doc_list.append(doc.dict())                   
                    merged_source_doc= []   
                    with st.expander("source"): 
                        for idx, item in enumerate(source_doc_list):
                            source_doc = {"Page": f"Source {idx + 1}", "Source": f"**Source:** {item['metadata']['source'].split('/')[-1]}",
                                        "page_content":item["page_content"]}
                            merged_source_doc.append(source_doc)
                            st.markdown(f"Source {idx + 1}")
                            st.markdown(f"**Source:** {item['metadata']['source'].split('/')[-1]}")
                            st.markdown(item["page_content"])
                            st.write("---")  # Add a separator between entries
                    message = {"role": "assistant", "content": full_response, "Source":merged_source_doc}
                    st.session_state.messages.append(message)
                    st.markdown("👍  👎  Create Ticket")
                # else:
                    # with st.expander("source"):
                    #     message = {"role": "assistant", "content": full_response, "Source":""}
                    #     st.session_state.messages.append(message)
        end = timeit.default_timer()
        print(f"Time to retrieve response: {end - start}")