app.py

from omegaconf import OmegaConf
from query import VectaraQuery
import os
from PIL import Image
import uuid

import streamlit as st
from streamlit_pills import pills
from streamlit_feedback import streamlit_feedback

from utils import thumbs_feedback, send_amplitude_data, escape_dollars_outside_latex


max_examples = 6
languages = {'English': 'eng', 'Spanish': 'spa', 'French': 'fra', 'Chinese': 'zho', 'German': 'deu', 'Hindi': 'hin', 'Arabic': 'ara',
             'Portuguese': 'por', 'Italian': 'ita', 'Japanese': 'jpn', 'Korean': 'kor', 'Russian': 'rus', 'Turkish': 'tur', 'Persian (Farsi)': 'fas',
             'Vietnamese': 'vie', 'Thai': 'tha', 'Hebrew': 'heb', 'Dutch': 'nld', 'Indonesian': 'ind', 'Polish': 'pol', 'Ukrainian': 'ukr',
             'Romanian': 'ron', 'Swedish': 'swe', 'Czech': 'ces', 'Greek': 'ell', 'Bengali': 'ben', 'Malay (or Malaysian)': 'msa', 'Urdu': 'urd'}

# Setup for HTTP API Calls to Amplitude Analytics
if 'device_id' not in st.session_state:
    st.session_state.device_id = str(uuid.uuid4())


if "feedback_key" not in st.session_state:
        st.session_state.feedback_key = 0

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

def launch_bot():
    def reset():
        st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]
        st.session_state.ex_prompt = None
        st.session_state.first_turn = True


    def generate_response(question):
        response = vq.submit_query(question, languages[st.session_state.language])
        return response
    
    def generate_streaming_response(question):
        response = vq.submit_query_streaming(question, languages[st.session_state.language])
        return response
    
    def show_example_questions():        
        if len(st.session_state.example_messages) > 0 and st.session_state.first_turn:            
            selected_example = pills("Queries to Try:", st.session_state.example_messages, index=None)
            if selected_example:
                st.session_state.ex_prompt = selected_example
                st.session_state.first_turn = False
                return True
        return False

    if 'cfg' not in st.session_state:
        corpus_keys = str(os.environ['corpus_keys']).split(',')
        cfg = OmegaConf.create({
            'corpus_keys': corpus_keys,
            'api_key': str(os.environ['api_key']),
            'title': os.environ['title'],
            'source_data_desc': os.environ['source_data_desc'],
            'streaming': isTrue(os.environ.get('streaming', False)),
            'prompt_name': os.environ.get('prompt_name', None),
            'examples': os.environ.get('examples', None),
            'language': 'English'
        })
        st.session_state.cfg = cfg
        st.session_state.ex_prompt = None
        st.session_state.first_turn = True
        st.session_state.language = cfg.language
        example_messages = [example.strip() for example in cfg.examples.split(",")]
        st.session_state.example_messages = [em for em in example_messages if len(em)>0][:max_examples]
        
        st.session_state.vq = VectaraQuery(cfg.api_key, cfg.corpus_keys, 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.image(image, width=175)
        st.markdown(f"## About\n\n"
                    f"This demo uses Retrieval Augmented Generation to ask questions about {cfg.source_data_desc}\n")
        
        cfg.language = st.selectbox('Language:', languages.keys())
        if st.session_state.language != cfg.language:
            st.session_state.language = cfg.language
            reset()
            st.rerun()

        st.markdown("\n")
        bc1, _ = st.columns([1, 1])
        with bc1:
            if st.button('Start Over'):
                reset()
                st.rerun()

        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.markdown(f"<center> <h2> Vectara AI Assistant: {cfg.title} </h2> </center>", unsafe_allow_html=True)

    if "messages" not in st.session_state.keys():
        reset()
                
    # Display chat messages
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.write(message["content"])

    example_container = st.empty()
    with example_container:
        if show_example_questions():
            example_container.empty()
            st.rerun()

    # select prompt from example question or user provided input
    if st.session_state.ex_prompt:
        prompt = st.session_state.ex_prompt
    else:
        prompt = st.chat_input()
    if prompt:
        st.session_state.messages.append({"role": "user", "content": prompt})
        with st.chat_message("user"):
            st.write(prompt)
        st.session_state.ex_prompt = None
        
    # 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)

            response = escape_dollars_outside_latex(response)
            message = {"role": "assistant", "content": response}
            st.session_state.messages.append(message)

            # Send query and response to Amplitude Analytics
            send_amplitude_data(
                user_query=st.session_state.messages[-2]["content"],
                chat_response=st.session_state.messages[-1]["content"],
                demo_name=cfg["title"],
                language=st.session_state.language
            )
            st.rerun()

    if (st.session_state.messages[-1]["role"] == "assistant") & (st.session_state.messages[-1]["content"] != "How may I help you?"):
        streamlit_feedback(feedback_type="thumbs", on_submit = thumbs_feedback, key = st.session_state.feedback_key,
                                      kwargs = {"user_query": st.session_state.messages[-2]["content"],
                                                "chat_response": st.session_state.messages[-1]["content"],
                                                "demo_name": cfg["title"],
                                                "response_language": st.session_state.language})
    
if __name__ == "__main__":
    launch_bot()