app.py

from PIL import Image
import sys
import os

import streamlit as st
from streamlit_pills import pills

import sqlite3
import pandas as pd
from datasets import load_dataset

from vectara_agent.agent import AgentStatusType
from agent import initialize_agent, get_agent_config

initial_prompt = "How can I help you today?"

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": "🦖"}]
        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.markdown("---")
        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": '🧑‍💻'})
        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='🧑‍💻'):
            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='🤖'):
            with st.spinner(st.session_state.thinking_message):
                res = st.session_state.agent.chat(st.session_state.prompt)
                res = res.replace('$', '\\$')  # escape dollar sign for markdown
            message = {"role": "assistant", "content": res, "avatar": '🤖'}
            st.session_state.messages.append(message)
            st.markdown(res)
        st.session_state.ex_prompt = None
        st.session_state.prompt = None
        st.session_state.first_turn = False
        st.rerun()

    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()