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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 generate_response(question): | |
| response = st.session_state.vq.submit_query(question) | |
| return response | |
| 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) | |
| def launch_bot(): | |
| 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 | |
| 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?"}] | |
| # Display chat messages | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.write(message["content"]) | |
| # Always allow typing in the chat input box | |
| user_input = st.text_input("Type your question here...", key="user_input") | |
| # Display example questions only during the first round | |
| if len(st.session_state.messages) == 0: # Only show examples in the first round | |
| for example in cfg.examples: | |
| if st.button(example): | |
| user_input = example # Directly use the example as input | |
| # Process the input immediately | |
| response = generate_response(user_input) | |
| st.session_state.messages.append(user_input) | |
| st.session_state.messages.append(response) | |
| st.experimental_rerun() # Rerun to refresh and show the response | |
| if st.button("Submit"): | |
| if user_input: # Ensure there's something to process | |
| response = generate_response(user_input) | |
| st.session_state.messages.append(user_input) | |
| st.session_state.messages.append(response) | |
| # Clear the input (workaround as direct clearing isn't supported) | |
| st.experimental_rerun() # Rerun to refresh and clear the input box | |
| if __name__ == "__main__": | |
| launch_bot() | |