app.py

import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import os
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from google import genai
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import PromptTemplate
from langchain_community.vectorstores import FAISS
from langchain_core.documents import Document

from dotenv import load_dotenv

load_dotenv()
os.getenv("GOOGLE_API_KEY")

# Create Gen AI Client
client = genai.Client(api_key=os.getenv("GOOGLE_API_KEY"))


def process_pdf_docs(pdf_docs: list[str]) -> list[Document]:
    docs = []
    for pdf in pdf_docs:
        text = ""
        pdf_reader = PdfReader(pdf)
        for page in pdf_reader.pages:
            text += page.extract_text()
        docs.append(
            Document(
                page_content=text, metadata={"file_name": pdf, "char_len": len(text)}
            )
        )
    return docs


def create_chunks(docs: list[Document]) -> list[Document]:
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    chunks = text_splitter.split_documents(docs)
    return chunks


def create_embeddings(chunks: list[Document]) -> None:
    embeddings = GoogleGenerativeAIEmbeddings(
        client=client, model="models/embedding-001"
    )
    vector_store = FAISS.from_documents(chunks, embedding=embeddings)
    print(vector_store.index.ntotal)
    vector_store.save_local("faiss_index")


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-1.5-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, 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 PDF")
    st.header("Chat with PDF using Gemini👀")

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

    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 st.button("Submit & Process"):
            with st.spinner("Processing..."):
                raw_documents = process_pdf_docs(pdf_docs)
                chunks = create_chunks(raw_documents)
                create_embeddings(chunks)
                st.success("Done")


if __name__ == "__main__":
    main()