import streamlit as st
import os
from langchain_groq import ChatGroq
from langchain_openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain.chains import create_retrieval_chain
from langchain_community.vectorstores import FAISS

from langchain_community.document_loaders import PyPDFDirectoryLoader

from dotenv import load_dotenv

load_dotenv()

## load the GroqAPI Key
os.environ['OPENAI_API_KEY']=os.getenv("OPENAI_API_KEY")
groq_api_key = os.getenv('GROQ_API_KEY')

st.title("ChatBot Demo for Error Codes")

llm=ChatGroq(groq_api_key=groq_api_key,
             model="Llama3-8b-8192")


prompt = ChatPromptTemplate.from_template(
    """
Answer the question based on the provided context only.
Please provide the most accurate response based on the question.
<context>
{context}
<context>
Question: {input}
    """
)


def vector_embedding():

    if "vectors" not in st.session_state:

        st.session_state.embeddings = OpenAIEmbeddings()
        st.session_state.loader = PyPDFDirectoryLoader("./data")
        st.session_state.docs = st.session_state.loader.load()
        st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
        st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs[:20])
        st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings )





prompt1=st.text_input("Enter your question from Documents")

if st.button("Documents Embedding"):
    vector_embedding()
    st.write("VectorStore DB is ready")

import time




if prompt1:
    start = time.process_time()
    document_chain = create_stuff_documents_chain(llm, prompt)
    retriever = st.session_state.vectors.as_retriever()
    retrieval_chain = create_retrieval_chain(retriever, document_chain)
    response = retrieval_chain.invoke({'input': prompt1})
    print("Response time : ", time.process_time() - start)
    st.write(response['answer'])

    # With a Streamlit expander
    with st.expander("Document Similarity Search"):
        # Find the relevant chunks
        for i, doc in enumerate(response["context"]):
            st.write(doc.page_content)
            st.write("------------------------------------")