import os
import streamlit as st
from dotenv import load_dotenv
from pinecone.grpc import PineconeGRPC
from pinecone import ServerlessSpec
from llama_index.embeddings import OpenAIEmbedding
from llama_index.ingestion import IngestionPipeline
from llama_index.query_engine import RetrieverQueryEngine
from llama_index.vector_stores import PineconeVectorStore
from llama_index.node_parser import SemanticSplitterNodeParser
from llama_index.retrievers import VectorIndexRetriever
from htmlTemplates import css, bot_template, user_template

# Load environment variables
load_dotenv()
pinecone_api_key = os.getenv("PINECONE_API_KEY")
openai_api_key = os.getenv("OPENAI_API_KEY")
index_name = os.getenv("INDEX_NAME")

# Initialize OpenAI embedding model
embed_model = OpenAIEmbedding(api_key=openai_api_key)

# Initialize connection to Pinecone
pinecone_client = PineconeGRPC(api_key=pinecone_api_key)
pinecone_index = pinecone_client.Index(index_name)
vector_store = PineconeVectorStore(pinecone_index=pinecone_index)

# Define the initial pipeline
pipeline = IngestionPipeline(
    transformations=[
        SemanticSplitterNodeParser(
            buffer_size=1,
            breakpoint_percentile_threshold=95,
            embed_model=embed_model,
        ),
        embed_model,
    ],
)

# Initialize LlamaIndex components
vector_index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
retriever = VectorIndexRetriever(index=vector_index, similarity_top_k=5)
query_engine = RetrieverQueryEngine(retriever=retriever)

# Function to handle user input and return the query response
def handle_userinput(user_question):
    response = st.session_state.conversation({'question': user_question})
    st.session_state.chat_history = response['chat_history']
    
    for i, message in enumerate(st.session_state.chat_history):
        if i % 2 == 0:
            st.write(user_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
        else:
            st.write(bot_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)

# Main function to run the Streamlit app
def main():
    load_dotenv()
    st.set_page_config(page_title="Chat with Annual Reports", page_icon=":books:")
    st.write(css, unsafe_allow_html=True)

    if "conversation" not in st.session_state:
        st.session_state.conversation = None
    if "chat_history" not in st.session_state:
        st.session_state.chat_history = None

    st.header("Chat with Annual Report Documents")
    user_question = st.text_input("Ask a question about your documents:")
    if user_question:
        handle_userinput(user_question)

if __name__ == "__main__":
    main()