app.py

import streamlit as st
from langchain_openai import ChatOpenAI
import os
import dotenv
from langchain_community.document_loaders import WebBaseLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.messages import HumanMessage, AIMessage
from langchain.memory import ConversationBufferMemory
from langchain.document_loaders import PyPDFLoader

# Set page config
st.set_page_config(page_title="Tbank Assistant", layout="wide")

# Streamlit app header
st.title("Tbank Customer Support Chatbot")

# Sidebar for API Key input
with st.sidebar:
    st.header("Configuration")
    api_key = st.text_input("Enter your OpenAI API Key:", type="password")
    if api_key:
        os.environ["OPENAI_API_KEY"] = api_key

# Main app logic
if "OPENAI_API_KEY" in os.environ:
    # Initialize components
    @st.cache_resource
    def initialize_components():
        dotenv.load_dotenv()
        chat = ChatOpenAI(model="gpt-3.5-turbo-1106", temperature=0.2)

        #loader1 = WebBaseLoader("https://www.tbankltd.com/")
        loader1 = PyPDFLoader("Tbank resources.pdf")
        loader2 = PyPDFLoader("International Banking Services.pdf")
        data1 = loader1.load()
        data2 = loader2.load()
        data = data1 + data2
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
        all_splits = text_splitter.split_documents(data)
        vectorstore = Chroma.from_documents(documents=all_splits, embedding=OpenAIEmbeddings())
        retriever = vectorstore.as_retriever(k=4)

        SYSTEM_TEMPLATE = """
        You are Tbank's AI assistant, a chatbot whose knowledge comes exclusively from Tbank's provided PDF documents. Follow these guidelines:

        Guidelines:
        Identity Confirmation:
        If asked, state: "I am Tbank's AI assistant. How can I help you today?"
        Scope of Information:
        Use only information from Tbank's website content and provided PDF documents.
        Do not infer or provide information from outside these sources.
        Response Style:
        Provide clear, concise responses.
        Keep answers brief and relevant to the user's query.
        Maintain a friendly and professional tone.
        Unknown Information:
        If a query is outside your knowledge base, respond: "I apologize, but I don't have information about that. My knowledge is limited to Tbank's products/services and our website/document content. Is there anything specific about Tbank I can help with?"
        If unsure about an answer, say: "I'm not certain about that. For accurate information, please check our website or contact our customer support team."
        Factual Information:
        Remind users that you provide only factual information from Tbank sources.
        End Interaction:
        Always end by asking: "Is there anything else you can help with regarding Tbank?"
        Examples:
        General Greeting:
        
        "Hello! Welcome to Tbank. How can I assist you today?"
        Identity Query:
        
        "I am Tbank's AI assistant. How can I help you today?"
        Out of Scope Query:
        
        "I apologize, but I don't have information about that. My knowledge is limited to Tbank's products/services and our website/document content. Is there anything specific about Tbank I can help with?"
        Uncertainty:
        
        "I'm not certain about that. For accurate information, please check our website or contact our customer support team."
        Closing:
        
        "Is there anything else you can help with regarding Tbank?"

        <context>
        {context}
        </context>

        Chat History:
        {chat_history}
        """

        question_answering_prompt = ChatPromptTemplate.from_messages(
            [
                (
                    "system",
                    SYSTEM_TEMPLATE,
                ),
                MessagesPlaceholder(variable_name="chat_history"),
                MessagesPlaceholder(variable_name="messages"),
            ]
        )

        document_chain = create_stuff_documents_chain(chat, question_answering_prompt)

        return retriever, document_chain

    # Load components
    with st.spinner("Initializing Tbank Assistant..."):
        retriever, document_chain = initialize_components()

    # Initialize memory for each session
    if "memory" not in st.session_state:
        st.session_state.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

    # Chat interface
    st.subheader("Chat with Tbank Assistant")

    # Initialize chat history
    if "messages" not in st.session_state:
        st.session_state.messages = []

    # Display chat messages from history on app rerun
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

    # React to user input
    if prompt := st.chat_input("What would you like to know about Tbank?"):
        # Display user message in chat message container
        st.chat_message("user").markdown(prompt)
        # Add user message to chat history
        st.session_state.messages.append({"role": "user", "content": prompt})

        with st.chat_message("assistant"):
            message_placeholder = st.empty()

            # Retrieve relevant documents
            docs = retriever.get_relevant_documents(prompt)

            # Generate response
            response = document_chain.invoke(
                {
                    "context": docs,
                    "chat_history": st.session_state.memory.load_memory_variables({})["chat_history"],
                    "messages": [
                        HumanMessage(content=prompt)
                    ],
                }
            )

            # The response is already a string, so we can use it directly
            full_response = response
            message_placeholder.markdown(full_response)

        # Add assistant response to chat history
        st.session_state.messages.append({"role": "assistant", "content": full_response})

        # Update memory
        st.session_state.memory.save_context({"input": prompt}, {"output": full_response})

else:
    st.warning("Please enter your OpenAI API Key in the sidebar to start the chatbot.")

# Add a footer
st.markdown("---")
st.markdown("By AI Planet")