Gemini App LLM

app.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_community.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
from googletrans import Translator
import pickle
from pathlib import Path

import streamlit_authenticator as stauth
# import pandas as pd

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

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 = """
    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

def user_input(user_question):
    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
    translator = Translator()
    new_db = FAISS.load_local("faiss_index", embeddings)
    docs = new_db.similarity_search(user_question)

    chain = get_conversational_chain()

    
    response = chain.invoke(
        {"input_documents":docs, "question": user_question})
    
    # print(response)
    # st.write("Reply: ", response["output_text"])
    response_text_english = response["output_text"]
    response_text_original_language = translator.translate (response_text_english,
                                                           dest=translator.detect(response_text_english).lang).text
    print(response_text_original_language)
    st.write("Reply: ", response_text_original_language)
    
def main():
    st.write("Debug Point 1")

    st.set_page_config(page_title="Chat PDF", page_icon=':male-technologist:', initial_sidebar_state='collapsed')
    # --- USER AUTHENTICATION ---
    names = ["Peter Parker", "Rebecca Miller"]
    usernames = ["pparker", "rmiller"]

    # load hashed passwords
    file_path = Path(__file__).parent / "hashed_dw.pkl"
    with file_path.open("rb") as file:
        hashed_passwords = pickle.load(file)

    authenticator = stauth.Authenticate(names, usernames, hashed_passwords, "CHATBOT", "abcdef", cookie_expiry_days=30)

    name, authentication_status, usernames = authenticator.login("Login", "main")

    if authentication_status == False:
        st.error("Username/password is incorrect")

    if authentication_status == None:
        st.warning("Please enter your username and password")

    if authentication_status:
        st.write("Debug Point 4")

        st.header("Chat with PDF in Any Language you want💁")
        user_question = st.text_input("Ask a Question from the PDF Files")
        st.write("Debug Point 5")

        if user_question:
            user_input(user_question)
        with st.sidebar:
            authenticator.logout("Logout Now", "sidebar")
            st.sidebar.title(f"Welcome {name} to my chatbot")
            pdf_docs = st.file_uploader("Upload your PDF Files to chat with me and Click on the Submit & Process Button", accept_multiple_files=True)
            if st.button("Submit & Process, Thinking..."):
                with st.spinner("Taking time for thinking..."):
                    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()