Streamlit Updates. Assistant

Blogs

April 12, 2025

AIssistant Updates

The aissistant was ideated as a some kind of all-in-one tool around LLMs.

Well, not only text centered, but using the multi-model capabilities as well:

Streamlit Speech Rater PoC MultiChat Project

And it had quite a few different features.

Do you prefer the box? the plastic?

Or the expensive toy I bought you?

Cat product Meme

DBChat Assistant

We come from here.

And I could not resist to combine it:

AIssistant

You can think about this is a kind of BI/AI part

Chat with Data

Using LangChain Chains

Data Chat Repository

Source Code for DB Chat with Langchain

Streamlit + DataChat AI / SQL + PyGWalker | Example 📌

streamlit run Z_ST_AIssistant_dbchat_v3b.py

https://github.com/JAlcocerT/Streamlit-AIssistant/tree/main/DB_Chat

import streamlit as st
from pygwalker.api.streamlit import StreamlitRenderer

from langchain_community.utilities import SQLDatabase

import pandas as pd
import os
from dotenv import load_dotenv

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI

st.set_page_config(
    page_title="Use Pygwalker In Streamlit",
    layout="wide"
)

st.title("SQL Database Table Viewer and Chat")

# Load environment variables
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
from openai import OpenAI

# Initialize the OpenAI client
client = OpenAI(api_key=api_key)

# MySQL URI (replace with your password)
mysql_uri = st.text_input("MySQL URI", "mysql+mysqlconnector://root:your_password@localhost:3306/Chinook")

# Debug Mode
DEBUG_MODE = st.checkbox("Enable Debug Mode (Show SQL Queries)", False)

# Get the list of tables
db = SQLDatabase.from_uri(mysql_uri)
tables_result = db.run("SHOW TABLES;")
tables = [table_tuple[0] for table_tuple in eval(tables_result)]

# Table selection
selected_table = st.selectbox("Select a Table", tables)

if selected_table:
    df = load_data_mysql(mysql_uri, selected_table)
    if df is not None:
        st.write(f"### Data from {selected_table}")
        st.dataframe(df)
        display_pygwalker(df)

# Chat with Database about the whole database
st.write("### Ask a Question about the Entire Database")

global_question = st.text_input("Global Question", "What are the top 5 artists with more albums published?")

if st.button("Submit Global Question"):
    if global_question:
        response, sql_query = chat_with_database(mysql_uri, global_question)
        st.write("Global Response:", response)
        if DEBUG_MODE and sql_query:
            with st.expander("SQL Query"):
                st.code(sql_query, language="sql")
    else:
        st.warning("Please enter a global question.")

# Interactive SQL Query Section
with st.expander("Run Custom SQL Query"):
    custom_query = st.text_area("Enter your SQL query:")
    if st.button("Execute Query"):
        if custom_query:
            try:
                db = SQLDatabase.from_uri(mysql_uri)
                result = db.run(custom_query)
                data = eval(result)
                if data:
                    st.write("Query Result:")
                    try:
                        query_columns = f"""SHOW COLUMNS FROM ({custom_query}) AS tmp_table"""
                        columns_result = db.run(query_columns)
                        columns_list = eval(columns_result)
                        columns = [column[0] for column in columns_list]
                        st.dataframe(pd.DataFrame(data, columns=columns))

                    except:
                        st.dataframe(pd.DataFrame(data))

                else:
                    st.write("Query returned no results.")
            except Exception as e:
                st.error(f"Error executing query: {e}")
        else:
            st.warning("Please enter a SQL query.")