import streamlit as st
# import fitz  # PyMuPDF for extracting text from PDFs
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.docstore.document import Document
from langchain.llms import HuggingFacePipeline
from langchain.chains import RetrievalQA
from transformers import AutoConfig, AutoTokenizer, pipeline, AutoModelForCausalLM
import torch
import re
import transformers
from torch import bfloat16
from langchain_community.document_loaders import DirectoryLoader

# Initialize embeddings and ChromaDB
model_name = "sentence-transformers/all-mpnet-base-v2"
device = "cpu"
model_kwargs = {"device": device}
embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)

loader = DirectoryLoader('./pdf', glob="**/*.pdf", use_multithreading=True)
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
all_splits = text_splitter.split_documents(docs)
vectordb = Chroma.from_documents(documents=all_splits, embedding=embeddings, persist_directory="pdf_db")
books_db = Chroma(persist_directory="./pdf_db", embedding_function=embeddings)

books_db_client = books_db.as_retriever()

# Initialize the model and tokenizer
model_name = "stabilityai/stablelm-zephyr-3b"

bnb_config = transformers.BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type='nf4',
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.bfloat16
)

model_config = transformers.AutoConfig.from_pretrained(model_name, max_new_tokens=1024)
model = transformers.AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    config=model_config,
    quantization_config=bnb_config,
    device_map=device,
)

tokenizer = AutoTokenizer.from_pretrained(model_name)

query_pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    return_full_text=True,
    torch_dtype=torch.float16,
    device_map=device,
    temperature=0.7,
    top_p=0.9,
    top_k=50,
    max_new_tokens=256
)

llm = HuggingFacePipeline(pipeline=query_pipeline)

books_db_client_retriever = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=books_db_client,
    verbose=True
)

st.title("RAG System with ChromaDB")

# Initialize session state for tracking previous questions and answers
if "history" not in st.session_state:
    st.session_state.history = []

# Function to retrieve answer using the RAG system
def test_rag(qa, query):
    return qa.run(query)

query = st.text_input("Enter your question:")

if st.button("Submit"):
    if query:
        # Get the answer from RAG
        books_retriever = test_rag(books_db_client_retriever, query)

        # Extracting the relevant answer using regex
        corrected_text_match = re.search(r"Helpful Answer:(.*)", books_retriever, re.DOTALL)
        
        if corrected_text_match:
            corrected_text_books = corrected_text_match.group(1).strip()
        else:
            corrected_text_books = "No helpful answer found."

        # Store the query and answer in session state
        st.session_state.history.append({"question": query, "answer": corrected_text_books})

# Display previous questions and answers
if st.session_state.history:
    # st.write("### Previous Questions and Answers")
    for idx, item in enumerate(st.session_state.history):
        st.write(f"**Question:** {item['question']}")
        st.write(f"**Answer:** {item['answer']}")
        st.write("---")