import os
import logging
import time
from dotenv import load_dotenv
import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain_cohere import CohereEmbeddings
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain_groq import ChatGroq

# Load environment variables
load_dotenv()

# Set up logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(levelname)s - %(message)s"
)

# Function to extract text from PDF files
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

# Function to split the extracted text into chunks
def get_text_chunks(text):
    text_splitter = CharacterTextSplitter(
        separator="\n",
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len
    )
    chunks = text_splitter.split_text(text)
    return chunks

# Function to create a FAISS vectorstore with rate-limiting and retry logic
def get_vectorstore(text_chunks):
    cohere_api_key = os.getenv("COHERE_API_KEY")
    embeddings = CohereEmbeddings(model="embed-english-v3.0", cohere_api_key=cohere_api_key)
    
    vectorstore = None
    batch_size = 10  # Process chunks in batches of 10
    for i in range(0, len(text_chunks), batch_size):
        batch = text_chunks[i:i+batch_size]
        retry_count = 0
        
        while retry_count < 5:  # Retry up to 5 times
            try:
                if vectorstore is None:
                    vectorstore = FAISS.from_texts(texts=batch, embedding=embeddings)
                else:
                    vectorstore.add_texts(batch, embedding=embeddings)
                break  # Exit retry loop if successful
            except Exception as e:
                if "rate limit" in str(e).lower():
                    logging.warning(f"Rate limit exceeded. Retrying batch {i//batch_size + 1} in {2 ** retry_count} seconds...")
                    time.sleep(2 ** retry_count)  # Exponential backoff
                    retry_count += 1
                else:
                    raise e  # Raise other errors
    return vectorstore

# Function to set up the conversational retrieval chain
def get_conversation_chain(vectorstore):
    try:
        llm = ChatGroq(model="llama-3.1-70b-versatile", temperature=0.5)
        memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
        
        conversation_chain = ConversationalRetrievalChain.from_llm(
            llm=llm,
            retriever=vectorstore.as_retriever(),
            memory=memory
        )
        
        logging.info("Conversation chain created successfully.")
        return conversation_chain
    except Exception as e:
        logging.error(f"Error creating conversation chain: {e}")
        st.error("An error occurred while setting up the conversation chain.")

# Handle user input
def handle_userinput(user_question):
    if st.session_state.conversation is not None:
        response = st.session_state.conversation({"question": user_question})
        st.session_state.chat_history = response["chat_history"]

        for i, message in enumerate(st.session_state.chat_history):
            if i % 2 == 0:
                st.write(f"*User:* {message.content}")
            else:
                st.write(f"*Bot:* {message.content}")
    else:
        st.warning("Please process the documents first.")

# Main function to run the Streamlit app
def main():
    load_dotenv()
    st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:")

    if "conversation" not in st.session_state:
        st.session_state.conversation = None
    if "chat_history" not in st.session_state:
        st.session_state.chat_history = None

    st.header("Chat with multiple PDFs :books:")
    user_question = st.text_input("Ask a question about your documents:")
    if user_question:
        handle_userinput(user_question)

    with st.sidebar:
        st.subheader("Your documents")
        pdf_docs = st.file_uploader(
            "Upload your PDFs here and click on 'Process'", 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)
                vectorstore = get_vectorstore(text_chunks)
                st.session_state.conversation = get_conversation_chain(vectorstore)

if __name__ == "__main__":
    main()