import streamlit as st
import os
from langchain_groq import ChatGroq
from langchain_openai import OpenAIEmbeddings
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
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 GROQ API KEY 
groq_api_key=os.getenv('GROQ_API_KEY')

st.title("Chatgroq With Llama3 Demo")

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

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

"""
)

def vector_embedding():
    if "vectors" not in st.session_state:
        st.session_state.embeddings=HuggingFaceBgeEmbeddings(model_name="BAAI/bge-small-en-v1.5",
                                                            model_kwargs={'device':'cpu'},
                                                            encode_kwargs={'normalize_embeddings':True})
        st.session_state.loader=PyPDFDirectoryLoader("./us_census") ## Data Ingestion
        st.session_state.docs=st.session_state.loader.load() ## Document Loading
        st.session_state.text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=200) ## Chunk Creation
        st.session_state.final_documents=st.session_state.text_splitter.split_documents(st.session_state.docs[:20]) #splitting
        st.session_state.vectors=FAISS.from_documents(st.session_state.final_documents,st.session_state.embeddings) #vector Huggingface embeddings


prompt1=st.text_input("Enter Your Question From Documents")


if st.button("Documents Embedding"):
    vector_embedding()
    st.write("Vector Store DB Is Ready")


import time
if prompt1:
    document_chain=create_stuff_documents_chain(llm,prompt)
    retriever=st.session_state.vectors.as_retriever()
    retrieval_chain=create_retrieval_chain(retriever,document_chain)
    start=time.process_time()
    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("--------------------------------")