import streamlit as st
import os
from langchain_groq import ChatGroq
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.embeddings import OllamaEmbeddings
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
import time

from dotenv import load_dotenv

load_dotenv()

## Load Groq API Key
groq_api_key = os.environ['GROQ_API_KEY']

if "vector" not in st.session_state:
    st.session_state.embeddings=OllamaEmbeddings()
    st.session_state.loader=WebBaseLoader("https://docs.smith.langchain.com/")
    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[:50])
    st.session_state.vectors=FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)


st.title("Chatgroq Demo")
llm=ChatGroq(groq_api_key=groq_api_key,
             model="gemma-7b-it")

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}
    """
)


document_chain = create_stuff_documents_chain(llm, prompt)
retriver = st.session_state.vectors.as_retriever()
retriver_chain = create_retrieval_chain(retriver, document_chain)

prompt=st.text_input("Input your prompt here")

if prompt:
    start=time.process_time()
    response = retriver_chain.invoke({"input": prompt})
    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("------------------------------------")