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from PIL import Image | |
import sys | |
import os | |
import uuid | |
import streamlit as st | |
from streamlit_pills import pills | |
from streamlit_feedback import streamlit_feedback | |
from utils import thumbs_feedback, escape_dollars_outside_latex, send_amplitude_data | |
import sqlite3 | |
from datasets import load_dataset | |
from vectara_agentic.agent import AgentStatusType | |
from agent import initialize_agent, get_agent_config | |
initial_prompt = "How can I help you today?" | |
# 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 toggle_logs(): | |
st.session_state.show_logs = not st.session_state.show_logs | |
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 | |
def update_func(status_type: AgentStatusType, msg: str): | |
if status_type != AgentStatusType.AGENT_UPDATE: | |
output = f"{status_type.value} - {msg}" | |
st.session_state.log_messages.append(output) | |
def launch_bot(): | |
def reset(): | |
st.session_state.messages = [{"role": "assistant", "content": initial_prompt, "avatar": "π¦"}] | |
st.session_state.thinking_message = "Agent at work..." | |
st.session_state.log_messages = [] | |
st.session_state.prompt = None | |
st.session_state.ex_prompt = None | |
st.session_state.first_turn = True | |
st.session_state.show_logs = False | |
if 'agent' not in st.session_state: | |
st.session_state.agent = initialize_agent(cfg, update_func=update_func) | |
if 'cfg' not in st.session_state: | |
cfg = get_agent_config() | |
st.session_state.cfg = cfg | |
st.session_state.ex_prompt = None | |
example_messages = [example.strip() for example in cfg.examples.split(",")] if cfg.examples else [] | |
st.session_state.example_messages = [em for em in example_messages if len(em)>0] | |
reset() | |
cfg = st.session_state.cfg | |
# left side content | |
with st.sidebar: | |
image = Image.open('Vectara-logo.png') | |
st.image(image, width=175) | |
st.markdown(f"## {cfg['demo_welcome']}") | |
st.markdown(f"{cfg['demo_description']}") | |
st.markdown("\n\n") | |
bc1, _ = st.columns([1, 1]) | |
with bc1: | |
if st.button('Start Over'): | |
reset() | |
st.rerun() | |
st.divider() | |
st.markdown( | |
"## How this works?\n" | |
"This app was built with [Vectara](https://vectara.com).\n\n" | |
"It demonstrates the use of Agentic RAG functionality with Vectara" | |
) | |
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"], avatar=message["avatar"]): | |
st.write(message["content"]) | |
example_container = st.empty() | |
with example_container: | |
if show_example_questions(): | |
example_container.empty() | |
st.session_state.first_turn = False | |
st.rerun() | |
# User-provided prompt | |
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, "avatar": 'π§βπ»'}) | |
st.session_state.prompt = prompt # Save the prompt in session state | |
st.session_state.log_messages = [] | |
st.session_state.show_logs = False | |
with st.chat_message("user", avatar='π§βπ»'): | |
print(f"Starting new question: {prompt}\n") | |
st.write(prompt) | |
st.session_state.ex_prompt = None | |
# Generate a new response if last message is not from assistant | |
if st.session_state.prompt: | |
with st.chat_message("assistant", avatar='π€'): | |
with st.spinner(st.session_state.thinking_message): | |
res = st.session_state.agent.chat(st.session_state.prompt) | |
res = escape_dollars_outside_latex(res) | |
message = {"role": "assistant", "content": res, "avatar": 'π€'} | |
st.session_state.messages.append(message) | |
st.markdown(res) | |
send_amplitude_data( | |
user_query=st.session_state.messages[-2]["content"], | |
bot_response=st.session_state.messages[-1]["content"], | |
demo_name=cfg['demo_name'] | |
) | |
st.session_state.ex_prompt = None | |
st.session_state.prompt = None | |
st.session_state.first_turn = False | |
st.rerun() | |
# Record user feedback | |
if (st.session_state.messages[-1]["role"] == "assistant") & (st.session_state.messages[-1]["content"] != initial_prompt): | |
streamlit_feedback( | |
feedback_type="thumbs", on_submit = thumbs_feedback, key = st.session_state.feedback_key, | |
kwargs = {"user_query": st.session_state.messages[-2]["content"], | |
"bot_response": st.session_state.messages[-1]["content"], | |
"demo_name": cfg["demo_name"]} | |
) | |
log_placeholder = st.empty() | |
with log_placeholder.container(): | |
if st.session_state.show_logs: | |
st.button("Hide Logs", on_click=toggle_logs) | |
for msg in st.session_state.log_messages: | |
st.text(msg) | |
else: | |
if len(st.session_state.log_messages) > 0: | |
st.button("Show Logs", on_click=toggle_logs) | |
sys.stdout.flush() | |
def setup_db(): | |
db_path = 'ev_database.db' | |
conn = sqlite3.connect(db_path) | |
cursor = conn.cursor() | |
with st.spinner("Loading data... Please wait..."): | |
def tables_populated() -> bool: | |
tables = ['ev_population', 'county_registrations', 'ev_registrations'] | |
for table in tables: | |
cursor.execute(f"SELECT name FROM sqlite_master WHERE type='table' AND name='{table}'") | |
result = cursor.fetchone() | |
if not result: | |
return False | |
return True | |
if tables_populated(): | |
print("Database tables already populated, skipping setup") | |
conn.close() | |
return | |
else: | |
print("Populating database tables") | |
# Execute the SQL commands to create tables | |
with open('create_tables.sql', 'r') as sql_file: | |
sql_script = sql_file.read() | |
cursor.executescript(sql_script) | |
hf_token = os.getenv('HF_TOKEN') | |
# Load data into ev_population table | |
df = load_dataset("vectara/ev-dataset", data_files="Electric_Vehicle_Population_Data.csv", token=hf_token)['train'].to_pandas() | |
df.to_sql('ev_population', conn, if_exists='replace', index=False) | |
# Load data into county_registrations table | |
df = load_dataset("vectara/ev-dataset", data_files="Electric_Vehicle_Population_Size_History_By_County.csv", token=hf_token)['train'].to_pandas() | |
df.to_sql('county_registrations', conn, if_exists='replace', index=False) | |
# Load data into ev_registrations table | |
df = load_dataset("vectara/ev-dataset", data_files="Electric_Vehicle_Title_and_Registration_Activity.csv", token=hf_token)['train'].to_pandas() | |
df.to_sql('ev_registrations', conn, if_exists='replace', index=False) | |
# Commit changes and close connection | |
conn.commit() | |
conn.close() | |
if __name__ == "__main__": | |
st.set_page_config(page_title="Electric Vehicles Assistant", layout="wide") | |
setup_db() | |
launch_bot() |