import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import google.generativeai as palm
from langchain.embeddings import GooglePalmEmbeddings
from langchain.llms import GooglePalm
from langchain.vectorstores import FAISS
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
import os

os.environ['GOOGLE_API_KEY'] =   'AIzaSyAO1uqCO_1CTZV1zgIlUhk5Mv4Ey08cjzI'

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=20)
    chunks = text_splitter.split_text(text)
    return chunks

def get_vector_store(text_chunks):
    embeddings = GooglePalmEmbeddings()
    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
    return vector_store

def get_conversational_chain(vector_store):
    llm=GooglePalm()
    memory = ConversationBufferMemory(memory_key = "chat_history", return_messages=True)
    conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=vector_store.as_retriever(), memory=memory)
    return conversation_chain

def user_input(user_question):
    response = st.session_state.conversation({'question': user_question})
    st.session_state.chatHistory = response['chat_history']
    for i, message in enumerate(st.session_state.chatHistory):
        if i%2 == 0:
            st.write("Me: ", message.content)
        else:
            st.write("mGPT: ", message.content)
def main():
    st.set_page_config("palm2 pdf ")
    st.header("Hi , ask me anything from your pdf 😎 ")
    user_question = st.text_input("Ask a Question from the PDF Files")
    if "conversation" not in st.session_state:
        st.session_state.conversation = None
    if "chatHistory" not in st.session_state:
        st.session_state.chatHistory = None
    if user_question:
        user_input(user_question)
    with st.sidebar:
        st.title("Settings")
        st.subheader("Upload your Documents")
        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Process Button", accept_multiple_files=True)
        if st.button("Process"):
            with st.spinner("Processing"):
                raw_text = get_pdf_text(pdf_docs)
                text_chunks = get_text_chunks(raw_text)
                vector_store = get_vector_store(text_chunks)
                st.session_state.conversation = get_conversational_chain(vector_store)
                st.success("Done")



if __name__ == "__main__":
    main()


#M