import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import os
from langchain.vectorstores import FAISS
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory

# import google.generativeai as palm
# from langchain.embeddings import GooglePalmEmbeddings
# from langchain.llms import GooglePalm
# from langchain_google_genai import GoogleGenerativeAI


from langchain.embeddings import HuggingFaceInstructEmbeddings
# from langchain.llms import HuggingFaceHub
from langchain_huggingface import HuggingFaceEndpoint

os.getenv('HUGGINGFACEHUB_API_TOKEN')
# 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=20)
    chunks = text_splitter.split_text(text)
    return chunks

def get_vector_store(text_chunks):
    embeddings = HuggingFaceInstructEmbeddings(model_name="BAAI/bge-large-zh-v1.5")
    # embeddings = GooglePalmEmbeddings()
    
    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
    return vector_store

def get_conversational_chain(vector_store):
    # llm = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={"temperature":0.5, "max_length":512})
    # llm = GoogleGenerativeAI(model="models/text-bison-001", temperature=0.5)
    llm = HuggingFaceEndpoint(
    repo_id="HuggingFaceH4/zephyr-7b-beta",
    task="text-generation",
    max_new_tokens=512)
    
    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("Human: ", message.content)
        else:
            st.write("Bot: ", message.content)
def main():
    st.set_page_config("Chat with Multiple PDFs")
    st.header("Chat with Multiple 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()