import streamlit as st
import os
from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings, ChatNVIDIA
from langchain_community.document_loaders import WebBaseLoader
from langchain.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_core.output_parsers import StrOutputParser
from langchain.chains import create_retrieval_chain
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyPDFDirectoryLoader
import time
import requests
import os
from dotenv import load_dotenv

load_dotenv()

## load the Groq API key
os.environ['NVIDIA_API_KEY'] = os.environ.get('api_key')

def vector_embedding():
    if "vectors" not in st.session_state:
        st.session_state.embeddings = NVIDIAEmbeddings()
        st.session_state.loader = PyPDFDirectoryLoader("./documents")  # Data Ingestion
        st.session_state.docs = st.session_state.loader.load()  # Document Loading
        st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=700, chunk_overlap=50)  # Chunk Creation
        st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs)  # Splitting
        print("hEllo")
        st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)  # Vector OpenAI embeddings

st.title("Ayurvedic Chatbot using Nvidia NIM")
llm = ChatNVIDIA(model="meta/llama3-70b-instruct")

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

prompt1 = st.text_input("Enter Your Question From related to Ayurvedic Herbs?")

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

if prompt1:
    # Ensure vectors are initialized before proceeding
    if "vectors" not in st.session_state:
        st.warning("Please embed the documents first by clicking the 'Documents Embedding' button.")
    else:
        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()
        
        try:
            response = retrieval_chain.invoke({'input': prompt1})
        except requests.exceptions.SSLError as e:
            st.error("SSL error occurred: {}".format(e))
            response = None

        if response:
            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("--------------------------------")