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import sys
import toml
from omegaconf import OmegaConf
from query import VectaraQuery
import os
import streamlit as st
from PIL import Image
def launch_bot():
def generate_response(question):
response = vq.submit_query(question)
return response
if 'cfg' not in st.session_state:
corpus_ids = str(os.environ['corpus_ids']).split(',')
questions = list(eval(os.environ['examples']))
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'],
'examples': questions,
'source_data_desc': os.environ['source_data_desc']
})
st.session_state.cfg = cfg
st.session_state.vq = VectaraQuery(cfg.api_key, cfg.customer_id, cfg.corpus_ids)
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"With 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?"}]
if "messages" not in st.session_state:
st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]
st.session_state.first_round = True
# Display chat messages
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.write(message["content"])
if st.session_state.first_round:
st.markdown("**Example questions:**")
for example in cfg.examples:
if st.button(example):
prompt = example
st.session_state.first_round = False
process_prompt(prompt)
else:
if prompt := st.chat_input("Ask me anything..."):
process_prompt(prompt) # Process the user-provided prompt
# 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"):
# with st.spinner("Thinking..."):
# response = generate_response(prompt)
# st.write(response)
# message = {"role": "assistant", "content": response}
# st.session_state.messages.append(message)
def process_prompt(prompt):
# Your logic to process the prompt and generate response
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("assistant"):
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()
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