App.py

import os
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from htmlTemplates import css, bot_template, user_template

def get_pdf_text(pdf_path):
    text = ""
    pdf_reader = PdfReader(pdf_path)
    for page in pdf_reader.pages:
        text += page.extract_text()
    return text

def get_text_chunks(text):
    text_splitter = CharacterTextSplitter(
        separator="\n",
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len
    )
    chunks = text_splitter.split_text(text)
    return chunks

def get_vectorstore(text_chunks):
    embeddings = OpenAIEmbeddings()
    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
    return vectorstore

def get_conversation_chain(vectorstore):
    llm = ChatOpenAI()
    memory = ConversationBufferMemory(
        memory_key='chat_history', return_messages=True)
    conversation_chain = ConversationalRetrievalChain.from_llm(
        llm=llm,
        retriever=vectorstore.as_retriever(),
        memory=memory
    )
    return conversation_chain

def handle_userinput(user_question):
    response = st.session_state.conversation({'question': user_question})
    st.session_state.chat_history.extend(response['chat_history'])

    for i, message in enumerate(st.session_state.chat_history):
        if i % 2 == 0:
            st.write(user_template.replace(
                "{{MSG}}", message.content), unsafe_allow_html=True)
        else:
            st.write(bot_template.replace(
                "{{MSG}}", message.content), unsafe_allow_html=True)

def main():
    load_dotenv()
    st.set_page_config(page_title="EdGPT", page_icon=":books:")
    st.write(css, unsafe_allow_html=True)

    if "conversation" not in st.session_state:
        st.session_state.conversation = None
    if "chat_history" not in st.session_state:
        st.session_state.chat_history = []

    st.header("Competitive Exam Guide :books:")

    pdf_folder_path = "docs"
    pdfs = [os.path.join(pdf_folder_path, file) for file in os.listdir(pdf_folder_path) if file.endswith(".pdf")]

    if not pdfs:
        st.warning("No PDFs found in the specified folder.")
        return

    user_question = st.text_input("Ask a question about Indian Competitive Exams:")
    if user_question:
        handle_userinput(user_question)

    with st.spinner("Processing"):
        for pdf_path in pdfs:

            raw_text = get_pdf_text(pdf_path)
            text_chunks = get_text_chunks(raw_text)
            vectorstore = get_vectorstore(text_chunks)
            st.session_state.conversation = get_conversation_chain(vectorstore)

if __name__ == '__main__':
    main()