Spaces:
Running
Running
File size: 3,073 Bytes
7f46a81 06e14df 7f46a81 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
import sys
import toml
from omegaconf import OmegaConf
from query import VectaraQuery
import os
import streamlit as st
from PIL import Image
from functools import partial
def set_query(q: str):
st.session_state['query'] = q
def launch_bot():
def get_answer(question):
response = vq.submit_query(question)
return response
corpus_ids = list(eval(os.environ['corpus_ids']))
questions = list(eval(os.environ['examples']))
cfg = OmegaConf.create({
'customer_id': os.environ['customer_id'],
'corpus_ids': corpus_ids,
'api_key': os.environ['api_key'],
'title': os.environ['title'],
'description': os.environ['description'],
'examples': questions,
'source_data_desc': os.environ['source_data_desc']
})
vq = VectaraQuery(cfg.api_key, cfg.customer_id, cfg.corpus_ids)
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"With this demo uses Retieval 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 API 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 demo app: {cfg.title} </h2> </center>", unsafe_allow_html=True)
st.markdown(f"<center> <h4> {cfg.description} <h4> </center>", unsafe_allow_html=True)
# Setup a split column layout
main_col, questions_col = st.columns([4, 2], gap="medium")
with main_col:
cols = st.columns([1, 8], gap="small")
cols[0].markdown("""<h5>Search</h5>""", unsafe_allow_html=True)
cols[1].text_input(label="search", key='query', max_chars=256, label_visibility='collapsed', help="Enter your question here")
st.markdown("<h5>Response</h5>", unsafe_allow_html=True)
response_text = st.empty()
response_text.text_area(f" ", placeholder="The answer will appear here.", disabled=True,
key="response", height=1, label_visibility='collapsed')
with questions_col:
st.markdown("<h5 style='text-align:center; color: red'> Sample questions </h5>", unsafe_allow_html=True)
for q in list(cfg.examples):
st.button(q, on_click=partial(set_query, q), use_container_width=True)
# run the main flow
if st.session_state.get('query'):
query = st.session_state['query']
response = get_answer(query)
response_text.markdown(response)
if __name__ == "__main__":
launch_bot()
|