1_🏠Home.py

import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import os
from langchain_google_genai import GoogleGenerativeAIEmbeddings
import google.generativeai as genai
from langchain.vectorstores import FAISS
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv

load_dotenv()
os.getenv("GOOGLE_API_KEY")
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

# Initialize session state
if 'pdf_docs' not in st.session_state:
    st.session_state.pdf_docs = None

if 'user_question' not in st.session_state:
    st.session_state.user_question = ""

if 'output_text' not in st.session_state:
    st.session_state.output_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

def get_text_chunks(text):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    chunks = text_splitter.split_text(text)
    return chunks

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")

def get_conversational_chain():
    prompt_template = """
    1. Answer the question as detailed as possible from the provided context 
    2. (if not in context search on Internet), 
    3. make sure to provide all the details, 
    4. use pointers and tables to make context more readable. 
    5. If information not found then search on google and then provide reply.
    6. (but then mention the reference name)
    7. If 'Summerize' word is used in input then Summerize the context.
    8. i input is: Hello reply: Hey hi Suraj.\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

def user_input(user_question):
    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")

    new_db = FAISS.load_local("faiss_index", embeddings)
    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.session_state.output_text = response["output_text"]
    st.write("Reply: ", st.session_state.output_text)

def main():
    st.set_page_config("PDF.Gemini","6183004.png")
    st.header("🖥️PDF.Gemini Start chat")
    
    user_question = st.text_input("Ask a Question from the PDF Files")

    if st.session_state.user_question != user_question:
        st.session_state.user_question = user_question
        st.session_state.output_text = ""  # Reset output text when input changes

    if user_question:
        user_input(user_question)

    with st.sidebar:
        st.title("Menu:")
        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)

        if pdf_docs:
            st.session_state.pdf_docs = pdf_docs

        if st.button("Submit & Process"):
            if st.session_state.pdf_docs:
                with st.spinner("Processing..."):
                    raw_text = get_pdf_text(st.session_state.pdf_docs)
                    text_chunks = get_text_chunks(raw_text)
                    get_vector_store(text_chunks)
                    st.success("Done")
        st.write("\n\nProject by Suraj Sanap")

if __name__ == "__main__":
    main()