Hugging Face's logo
Hugging Face
Search models, datasets, users...
Models
Datasets
Spaces
Posts
Docs
Solutions
Pricing



Spaces:

vectara
/
IRS-chat


like
3

Logs
App
Files
Community
Settings
IRS-chat
/
app.py

ofermend's picture
ofermend
Update app.py
81cb44a
VERIFIED
1 day ago
raw
history
blame
edit
delete
No virus
3.56 kB
from omegaconf import OmegaConf
from query import VectaraQuery
import os

import streamlit as st
from PIL import Image

def isTrue(x) -> bool:
    if isinstance(x, bool):
        return s
    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)),
            'questions': os.environ['questions'],
            '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?"}]
        for question in questions:
            st.button(question, on_click=lambda q=question: submit_question(q))


    # 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():
        submit_question(prompt)

    def submit_question(question):
        st.session_state.messages.append({"role": "user", "content": question})
        with st.chat_message("user"):
            st.write(question)
        generate_and_display_response(question)
        
    def generate_and_display_response(question):
        if cfg.streaming:
            stream = generate_streaming_response(question)
            response = st.write_stream(stream)
        else:
            with st.spinner("Thinking..."):
                response = generate_response(question)
                st.write(response)
        message = {"role": "assistant", "content": response}
        st.session_state.messages.append(message)
                

if __name__ == "__main__":
    launch_bot()