from omegaconf import OmegaConf
from query import VectaraQuery
import os

import streamlit as st
from PIL import Image
from dotenv import load_dotenv


load_dotenv(override=False)

def isTrue(x) -> bool:
    if isinstance(x, bool):
        return x
    return x.strip().lower() == 'true'

def launch_bot():
    def generate_response(question):
        response = vq.submit_query(question)
        return response
    
    def generate_streaming_response(question):
        response = vq.submit_query_streaming(question)
        return response

    if 'cfg' not in st.session_state:
        corpus_ids = str(os.environ['corpus_ids']).split(',')
        cfg = OmegaConf.create({
            'customer_id': str(os.environ['customer_id']),
            'corpus_ids': corpus_ids,
            'api_key': str(os.environ['api_key']),
            'title': os.environ['title'],
            'description': os.environ['description'],
            'source_data_desc': os.environ['source_data_desc'],
            'streaming': isTrue(os.environ.get('streaming', False)),
            'prompt_name': os.environ.get('prompt_name', None)
        })
        st.session_state.cfg = cfg
        st.session_state.vq = VectaraQuery(cfg.api_key, cfg.customer_id, cfg.corpus_ids, cfg.prompt_name)

    cfg = st.session_state.cfg
    vq = st.session_state.vq
    st.set_page_config(page_title=cfg.title, layout="wide")

    # left side content
    with st.sidebar:
        image = Image.open('Vectara-logo.png')
        st.markdown(f"## Welcome to {cfg.title}\n\n"
                    f"This demo uses Retrieval Augmented Generation to ask questions about {cfg.source_data_desc}\n\n")

        st.markdown("---")
        st.markdown(
            "## How this works?\n"
            "This app was built with [Vectara](https://vectara.com).\n"
            "Vectara's [Indexing API](https://docs.vectara.com/docs/api-reference/indexing-apis/indexing) was used to ingest the data into a Vectara corpus (or index).\n\n"
            "This app uses Vectara [Chat API](https://docs.vectara.com/docs/console-ui/vectara-chat-overview) to query the corpus and present the results to you, answering your question.\n\n"
        )
        st.markdown("---")
        st.image(image, width=250)

    st.markdown(f"<center> <h2> Vectara chat demo: {cfg.title} </h2> </center>", unsafe_allow_html=True)
    st.markdown(f"<center> <h4> {cfg.description} <h4> </center>", unsafe_allow_html=True)

    if "messages" not in st.session_state.keys():
        st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]

    # Display chat messages
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.write(message["content"])

    # User-provided prompt
    if prompt := st.chat_input():
        st.session_state.messages.append({"role": "user", "content": prompt})
        with st.chat_message("user"):
            st.write(prompt)
    
    # Generate a new response if last message is not from assistant
    if st.session_state.messages[-1]["role"] != "assistant":
        with st.chat_message("assistant"):
            if cfg.streaming:
                stream = generate_streaming_response(prompt) 
                response = st.write_stream(stream) 
            else:
                with st.spinner("Thinking..."):
                    response = generate_response(prompt)
                    st.write(response)
            message = {"role": "assistant", "content": response}
            st.session_state.messages.append(message)
    
if __name__ == "__main__":
    launch_bot()