app.py

import os  
import streamlit as st  
import google.generativeai as genai  
from langchain_google_genai import GoogleGenerativeAIEmbeddings  
from langchain_google_genai import ChatGoogleGenerativeAI  
from langchain_community.document_loaders import PyPDFLoader   
from langchain.text_splitter import RecursiveCharacterTextSplitter  
from langchain_community.vectorstores import Chroma  
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder  
from langchain_core.messages import HumanMessage, SystemMessage  
from langchain.chains import create_history_aware_retriever, create_retrieval_chain 
from langchain.chains.combine_documents import create_stuff_documents_chain 
from dotenv import load_dotenv  
from langchain.embeddings import HuggingFaceEmbeddings

from sentence_transformers import SentenceTransformer

import pysqlite3
import sys
sys.modules['sqlite3'] = pysqlite3

import os

# Retrieve Google API key
GOOGLE_API_KEY = str(os.getenv('GOOGLE_API_KEY'))
HF_TOKEN = str(os.getenv("HF_TOKEN"))

if not GOOGLE_API_KEY:
    raise ValueError("Gemini API key not found. Please set it in the .env file.")

os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
os.environ["HF_TOKEN"] = HF_TOKEN
# Streamlit app configuration
st.set_page_config(page_title="English Chatbot", layout="centered")
st.title("English Tutor Bot")

# Initialize Google Generative AI LLM 
llm = ChatGoogleGenerativeAI(
    model="gemini-1.5-pro-latest",
    temperature=0.2,
    max_tokens=500,
    timeout=None,
    max_retries=2,
)

# Initialize embeddings using HuggingFace
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")

def load_preprocessed_vectorstore():
    try:
        loader = PyPDFLoader("sound.pdf")
        documents = loader.load()

        text_splitter = RecursiveCharacterTextSplitter(
            separators=["\n\n", "\n", ". ", " ", ""],
            chunk_size=500, 
            chunk_overlap=150
        )

        document_chunks = text_splitter.split_documents(documents)

        vector_store = Chroma.from_documents(
            embedding=embeddings,
            documents=document_chunks,
            persist_directory="./data32"
        )
        return vector_store
    except Exception as e:
        st.error(f"Error creating vector store: {e}")
        return None

def get_context_retriever_chain(vector_store):
    retriever = vector_store.as_retriever()

    prompt = ChatPromptTemplate.from_messages([
        MessagesPlaceholder(variable_name="chat_history"),
        ("human", "{input}"),
        ("system", """You are an expert english tutor, your task is to help users to learn english. Given the chat history and the latest user question, which might reference context in the chat history, Answer the question
        by taking reference from the document.
If the question is directly addressed within the provided document, provide a relevant answer. 
If the question is not explicitly addressed in the document, return the following message: 
'This question is beyond the scope of the available information. Please contact your mentor for further assistance.'
Do NOT answer the question directly, just reformulate it if needed and otherwise return it as is.""")
    ])

    retriever_chain = create_history_aware_retriever(llm, retriever, prompt)
    return retriever_chain

def get_conversational_chain(retriever_chain):
    prompt = ChatPromptTemplate.from_messages([
        ("system", """Hello! I'm your English Tutor, I am here to help you with learning english and can also take quiz to test your skills.
Note: I will only provide information that is available within our database to ensure accuracy. Let's get started!
"""
         "\n\n"
         "{context}"),
        MessagesPlaceholder(variable_name="chat_history"),
        ("human", "{input}")
    ])

    stuff_documents_chain = create_stuff_documents_chain(llm, prompt)
    return create_retrieval_chain(retriever_chain, stuff_documents_chain)

def get_response(user_query):
    retriever_chain = get_context_retriever_chain(st.session_state.vector_store)
    conversation_rag_chain = get_conversational_chain(retriever_chain)

    formatted_chat_history = []
    for message in st.session_state.chat_history:
        if isinstance(message, HumanMessage):
            formatted_chat_history.append({"author": "user", "content": message.content})
        elif isinstance(message, SystemMessage):
            formatted_chat_history.append({"author": "assistant", "content": message.content})

    response = conversation_rag_chain.invoke({
        "chat_history": formatted_chat_history,
        "input": user_query
    })

    return response['answer']

# Load the preprocessed vector store from the local directory
st.session_state.vector_store = load_preprocessed_vectorstore()

# Initialize chat history if not present
if "chat_history" not in st.session_state:
    st.session_state.chat_history = [
        {"author": "assistant", "content": "Hello, I am a English Tutor Bot. How can I help you?"}
    ]

# Main app logic
if st.session_state.get("vector_store") is None:
    st.error("Failed to load preprocessed data. Please ensure the data exists in './data' directory.")
else:
    # Display chat history
    with st.container():
        for message in st.session_state.chat_history:
            if message["author"] == "assistant":
                with st.chat_message("system"):
                    st.write(message["content"])
            elif message["author"] == "user":
                with st.chat_message("human"):
                    st.write(message["content"])

    # Add user input box below the chat
    with st.container():
        with st.form(key="chat_form", clear_on_submit=True):
            user_query = st.text_input("Type your message here...", key="user_input")
            submit_button = st.form_submit_button("Send")

        if submit_button and user_query:
            # Get bot response
            response = get_response(user_query)
            st.session_state.chat_history.append({"author": "user", "content": user_query})
            st.session_state.chat_history.append({"author": "assistant", "content": response})

            # Rerun the app to refresh the chat display
            st.rerun()