app.py

import os
from dotenv import load_dotenv
import streamlit as st
from PyPDF2 import PdfReader

import google.generativeai as genai

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import PromptTemplate

#Load the environment variables
load_dotenv()

#Configure the Google API key
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

#Read the uploaded PDFs one by one and each page in the pdf and extract the text
def get_pdf_text(pdf_docs):
    text = ""
    for pdf in pdf_docs:
        pdf_reader = PdfReader(pdf)
        for page in pdf_reader.pages:
            text += page.extract_text()
    return text

#Split the text into chunks of 10000 characters
def get_text_chunks(text):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
    chunks = text_splitter.split_text(text)
    return chunks

#Convert the text chunks into vectors
def get_vector_store(text_chunks):
    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
    vector_store = FAISS.from_texts(text_chunks, embedding = embeddings)
    vector_store.save_local("faiss_index")

#Create a conversational chain with a prompt template   
def get_conversational_chain():
    prompt_template = """
    Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
    provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
    Context:\n {context}?\n
    Question: \n{question}\n

    Answer:
    """
    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
    
    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
    
    return chain

#Get user input from the chatbox and get the response from the conversational chain
def user_input(user_question):
    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
    
    new_db = FAISS.load_local("faiss_index", embeddings,allow_dangerous_deserialization=True)
    docs = new_db.similarity_search(user_question)
    
    chain = get_conversational_chain()
    
    response = chain(
        {"input_documents": docs, "question": user_question},
        return_only_outputs=True
    )
    
    print(response)
    st.write("Reply: ", response["output_text"])

def main():
    st.set_page_config("Chat With PDFs")
    st.header("Chat with Multiple PDFs :speech_balloon:")

    user_question = st.text_input("Ask a Question from the PDF Files")

    if user_question:
        user_input(user_question)

    with st.sidebar:
        st.title("Upload PDF Files")
        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
        if st.button("Submit & Process"):
            with st.spinner("Processing..."):
                raw_text = get_pdf_text(pdf_docs)
                text_chunks = get_text_chunks(raw_text)
                get_vector_store(text_chunks)
                st.success("Done")


if __name__ == "__main__":
    main()